We thank Minna Lehtonen, Tuomo H&#228;nninen, Merja Hallikainen, and Hilkka Soininen for their contribution to the data collection and processing reported here. The data collection was supported by VPH Dementia Research enabled by EU, Grant agreement No. 601055.

The protocol follows the guidelines of the Ethics Committee of the Hospital District of Northern Savo (IRB00006251).
1. Participant screening
<ol>
	<li>Recruit younger and older adults who have&#160;normal or corrected-to-normal vision and are native speakers of the language tested unless the study addresses specific research questions regarding second language acquisition.</li>
	<li>For healthy control groups, exclude participants who have a history of neurological or psychiatric disorders.</li>
	<li>For the clinical groups, recruit individuals who have been diagnosed with Alzheimer&#8217;s disease15 or mild cognitive impairment16,17. Recruit only individuals who are able to give informed consent, according to the clinician&#39;s judgment. For accurate comparisons, match the age range and mean of the clinical groups with that of the healthy older adult participants.</li>
	<li>Measure the severity of dementia, for example, using the Clinical Dementia Rating Scale18 (CDR, 0=no dementia, 0.5=very mild, 1=mild, 2=moderate, 3=severe). Exclude patients with severe dementia because the task may be too difficult for them. Do not include participants who seem unable to follow instructions, despite their severity rating.</li>
</ol>
2. Stimulus construction
<ol>
	<li>Select word stimuli to address specific research questions, for example, whether semantic or orthographic/phonological variables have a stronger influence on word recognition19 in different populations.</li>
	<li>Calculate from a corpus20 or retrieve from a database21 variables reflecting semantic, phonological, and orthographic characteristics of the stimuli so they can be used either as theoretically motivated predictors explaining word recognition reaction times or as control variables. Also, use participants&#8217; gender, age, and years of education as explanatory or control variables.</li>
	<li>In addition to the real words, build a set of matched pseudo-words. Pseudo-words resemble real words in that they conform to the language&#8217;s norms for placement of certain letters in certain word positions (phonotactics). In order to control for phonotactics, create pseudo-words, for example, by randomly recombining the first syllables from some words with the second syllables from other words. Remove any items that happened to produce a real word through this recombination and all the items that violate the phonotactics of the language.</li>
	<li>Match the pseudo-words with the target words in terms of the word length in letters and bigram frequency, which is the average number of times that all combinations of two subsequent letters occur in a text corpus. These variables have been shown to influence recognition speed. 
	NOTE: Manipulating the pseudo-word ratio (e.g., the number of real words relative to the number of pseudo-words) may lead to different results, with responses to the less probable stimuli being slower and less accurate22.</li>
	<li>Add a set of real-word fillers in order to decrease participant&#8217;s expectancy of the next stimulus belonging to a certain type (e.g., a certain inflectional class). Choose them, for instance, from different word categories (e.g., inflectional classes) than the ones used to construct stimuli according to the characteristics of interest.</li>
</ol>
3. Experimental design
<ol>
	<li>Present letter strings horizontally, one at a time, subtending a visual angle of about 5&#176;.</li>
	<li>Begin the experiment with a practice session that includes a small number of trials, with one word presented per trial (e.g., 15 words and 15 pseudo-words not included in the actual experiment). This is to familiarize the participant with the task and the response buttons. If the participant is not responding accurately (&#8216;yes&#8217; button for real words and &#8216;no&#8217; button for pseudo-words) during the practice trials, provide feedback and redo the practice session.</li>
	<li>Divide the experiment into blocks and give short breaks after the practice session and between the blocks. These breaks allow participants to rest their eyes and will reduce fatigue.</li>
	<li>Start each new block with a few filler items that will not be included in the analysis (e.g., common nouns such as dog, sister, year) because the first few trials of the block are sometimes ignored by participants with MCI or AD.</li>
	<li>Present the experimental items in a random order for each participant.</li>
	<li>Begin each trial with a fixation mark (e.g., a + sign) appearing in the center of the screen for 500 ms, followed by a blank screen for a fixed (e.g., 500 ms) or variable amount of time (e.g., 500-800 ms).</li>
	<li>Immediately after the blank screen, present a letter string (word or pseudo-word) for 1,500 ms or until the participant responds.</li>
	<li>After a response is made or after 1,500 ms from the onset of the word (whichever comes first), follow again with a blank screen until 3000 ms has passed from the beginning of the trial.</li>
	<li>Repeat this sequence until all of the items in the experiment have been presented. 
	NOTE: Times for the delay between the stimuli serve as an example. Changing them may affect the pattern of results.</li>
</ol>
4. Experimental procedure
<ol>
	<li>Place the participant in front of a computer monitor at a viewing distance of about 80 cm in a normally lit room.</li>
	<li>Instruct the participant to decide as quickly and accurately as possible whether the letter string on the screen is a real word or not by pressing one of two corresponding buttons with their dominant hand (e.g., the index finger for real words and the middle finger for pseudo-words) or using the index finger of each hand. 
	NOTE: Participants try to optimize their performance in line with the instructions. Thus, their responses will be affected by stressing speed over accuracy or vice versa23.</li>
</ol>
5. Analyzing data with a mixed-effects model in R
NOTE: Many different statistical programs can be used to perform the analysis. This section describes steps for analyzing data in R24.
<ol>
	<li>Obtain the reaction time (RT) measured in milliseconds for each trial from the output file of the presentation program (e.g., E-Studio software).</li>
	<li>Install the packages&#160;lme428&#160;and&#160;lmerTest29. Attach packages with the function&#160;library&#160;or&#160;require.</li>
	<li>Import data into R by using, e.g., the&#160;read.table&#160;function.</li>
	<li>Check the need for transformation, e.g., with the boxcox function from the MASS package25, as the distribution of RT data is typically highly skewed. 
	&#62; library (MASS) 
	&#62; boxcox(RT ~ Expnanatory_variable, data = yourdata) 
	NOTE: The graph produced by the boxcox function shows a 95% confidence interval for the boxcox transformation parameter. Depending on the lambda values located within this interval, the needed transformation can be chosen, e.g., &#955;=&#8722;1 (inverse transformation), &#955;=0 (logarithmic transformation), &#955;=1/2 (square root transformation), and &#955;=1/3 (cube root transformation).
	<ol>
		<li>Transform the RT values using inverted transformed RTs (e.g., -1000/RT) or binary logarithms of RTs (e.g., log2(RT)) since these transformations tend to provide more normal-like distributions for reaction times in lexical decision experiments than raw RTs26.</li>
		<li>Alternatively, use statistical methods that do not rely on normal distributions and fit robust linear mixed-effects models and provide estimates on which outliers or other sources of contamination have little influence27.</li>
	</ol>
	</li>
	<li>Since reaction time analyses are typically conducted on accurate responses, exclude trials in which the participants&#8217; response was incorrect (a response of &#8220;no&#8221; to real words) as well as omissions.
	<ol>
		<li>Also, exclude responses to pseudo-words and fillers unless there are specific hypotheses about them.</li>
		<li>Exclude trials with response times faster than 300 ms because they typically indicate that the participant was too late responding to a previous stimulus or that he or she accidentally pressed the response button before reading the stimulus.</li>
	</ol>
	</li>
	<li>Build a basic linear mixed-effects model that identifies RT as the outcome measure and Subject, Item, and Trial as random effects. Note that variables whose values are randomly sampled from a larger set (population) of values are included as random effects and variables with a small number of levels or for which all levels are included in the data are fixed effects. Add the random effects in the form (1 | Subject) in order to estimate random intercepts for each of the random effects. 
	&#62; g1 = lmer (RT ~ (1 | Subject) + (1 | Item) + (1 | Trial), data = yourdata) 
	&#62; summary (g1)</li>
	<li>Add explanatory variables in a theoretically motivated order. For instance, add words&#8217; base frequency as a fixed effect. Some variables, such as base or surface frequency, have Zipfian distributions, so insert them in the model with a transformation that results in a more Gaussian distribution shape, e.g., logarithmic transformation. 
	&#62; g2 = lmer (RT ~ log(BaseFrequency + 1) + (1 | Subject) + (1 | Item) + (1 | Trial), data = yourdata) 
	&#62; summary (g2)</li>
	<li>Check with the Anova function if adding each predictor (e.g., BaseFrequency) significantly improved the predictive power of the model compared to a model without the predictor. 
	&#62; anova (g1, g2)
	<ol>
		<li>If there is no significant difference in the fit of the new model over the simpler model, prefer the simplest model with fewer predictors. Also, check the Akaike Information Criterion (AIC)30 of each model. AIC is a measure of how well statistical models fit a set of data according to maximum likelihood. Lower values indicate a better fit for the data31. 
		&#62; AIC (g1); AIC (g2)</li>
	</ol>
	</li>
	<li>Repeat steps 5.7. and 5.8. by adding other explanatory variables, e.g., some of those that are presented in Table 1, one by one in a theoretically motivated order and keeping only those that significantly improve the predictive power of the model. If variable stimulus onset asynchrony was used, include it as a fixed-effect variable in the model.</li>
	<li>Check for theoretically motivated interactions between predictors. For instance, add a term of interaction the log of Base Frequency by Age. 
	&#62; g3 = lmer (RT ~ log(BaseFrequency + 1) + Age + log(BaseFrequency + 1) : Age + (1 | Subject) + (1 | Item) + (1 | Trial), data = yourdata) 
	NOTE: It is possible that a predictor is significant as a term of interaction with another variable, but not significant as the main predictor. In this case, do not remove this predictor from the model (include it also as the main effect).</li>
	<li>Add by-participant random slopes32 for predictors by including &#8220;1 +&#8221; before the variable name, then &#8220;| Subject&#8221;, e.g., (1 + log(BaseFrequency&#160; + 1) | Subject), because participants&#8217; response times might be affected by words&#39; lexical characteristics in different ways. 
	NOTE: If there are many continuous predictors, allowing them all to have random slopes is unrealistic because random slope models require large amounts of data to accurately estimate variances and covariances33,34. In case the maximal model does not converge (in other words, successfully compute), simplify the model33. Alternatively, implement Bayesian versions of multilevel modeling35.</li>
	<li>Run the analysis for each participant group separately. Alternatively, run an analysis on all data, with group as a fixed-effect predictor, and then test for an interaction of group by significant predictors. 
	&#62; g4 = lmer (RT ~ log(BaseFrequency + 1) + Age + log(BaseFrequency + 1) : Age&#160; + Group + log(BaseFrequency + 1) : Group +&#160; (1 + log(BaseFrequency + 1) | Subject) +&#160; (1 | Item) + (1 | Trial), data = yourdata)</li>
	<li>In order to remove the influence of possible outliers, exclude data points with absolute standardized residuals exceeding, e.g., 2.5 standard deviations26, and re-fit the model with the new data (yourdata2). 
	&#62; yourdata2 = yourdata [abs(scale(resid(g4))) &#60; 2.5, ] 
	&#62; g5 = lmer (RT ~ log(BaseFrequency + 1) + Age + log(BaseFrequency + 1) : Age&#160; + Group + log(BaseFrequency + 1) : Group + (1 + log(BaseFrequency +1) | Subject) +&#160; (1 | Item) + (1 | Trial), data = yourdata2) 
	NOTE: Not all extreme data points are harmful for the model &#8211; only those that have excessive leverage over the model.</li>
	<li>In the case of exploratory (data-driven) analysis, use backward stepwise regression: include all variables in the initial analysis and then remove non-significant variables from the model in a step-by-step fashion. Alternatively, use the automatic procedure of eliminating non-significant predictors with the step function provided by the package lmerTest29. 
	&#62; step (g4)</li>
</ol>

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M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Inflected+words+in+production:+Evidence+for+a+morphologically+rich+lexicon.">Inflected words in production: Evidence for a morphologically rich lexicon.</a> The Quarterly Journal of Experimental Psychology. 69, 432-454 (2016).</li><li>Yarkoni, T., Balota, D., Yap, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Moving+beyond+Coltheart&#8217;s+N:+A+new+measure+of+orthographic+similarity.">Moving beyond Coltheart&#8217;s N: A new measure of orthographic similarity.</a> Psychonomic Bulletin &amp; Review. 15 (5), 971-979 (2008).</li><li>Cohen, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Recognition+and+retrieval+of+proper+names:+Age+differences+in+the+fan+effect.">Recognition and retrieval of proper names: Age differences in the fan effect.</a> European Journal of Cognitive Psychology. 2 (3), 193-204 (1990).</li><li>Kemmerer, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Cognitive neuroscience of language. , (2015).</li><li>Baayen, R. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Analyzing linguistic data: A practical introduction to statistics using R. , (2008).</li><li>Nikolaev, A., Lehtonen, M., Higby, E., Hyun, J., Ashaie, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+facilitatory+effect+of+rich+stem+allomorphy+but+not+inflectional+productivity+on+single-word+recognition.">A facilitatory effect of rich stem allomorphy but not inflectional productivity on single-word recognition.</a> Applied Psycholinguistics. 39, 1221-1238 (2018).</li><li>Nikolaev, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Behavioural+and+ERP+effects+of+paradigm+complexity+on+visual+word+recognition.">Behavioural and ERP effects of paradigm complexity on visual word recognition.</a> Language, Cognition and Neuroscience. 10, 1295-1310 (2014).</li></ol>

The authors have nothing to disclose.

By using a simple language task that does not require language production, the present study investigated the impact of various lexical variables on word recognition in neurologically healthy younger and older adults, as well as in people with Alzheimer&#8217;s disease or Mild Cognitive Impairment. The age range used for recruiting &#8220;older adults&#8221; might depend on the specific research interests; however, the range for the healthy elderly group should match as closely as possible the age range and distribution ...

Words are stored in the mental lexicon in a highly interconnected network. The connections between words may reflect shared properties, such as semantic similarity (e.g., dog and cat), form similarity (dog and fog), or frequent co-occurrence in common language use (e.g., dog and leash). Cognitive theories of language, such as usage-based theory1, argue that every encounter of a word by a language user has an effect on the word&#8217;s mental representation. According to Exemplar Theory, a word&#8217;s representation consists of many exemplars, which are built up from individual tokens of language use and which represent the variability that exists for a given category. The frequency of use2 impacts representations in memory by contributing to the strength of an exemplar1.
Word recognition speed can reveal the characteristics of the mental lexicon. A commonly used experimental paradigm for measuring the speed of word recognition is the lexical decision task. In this task, participants are presented with letter strings on a monitor, one at a time. They are instructed to decide as quickly as possible whether the letter string on the screen is a real word or not by pressing the corresponding button.
By examining reaction times for real words, researchers can address a number of important questions about language processing. For example, identifying which factors make recognition faster can test hypotheses about the structure of the mental lexicon and reveal its architecture. Moreover, comparisons of performance across different groups of participants can help us understand the influence of various types of language experience, or, in the case of aging or neurodegenerative diseases (e.g., Alzheimer&#8217;s disease), the role of cognitive decline.
Some factors (e.g., the frequency of use) exhibit greater influence on word recognition than other factors (e.g., word length). With advancing age, the way people recognize written words might change3,4. Younger adults tend to rely heavily on semantic (meaning-based) aspects of a word, such as how many compounds (e.g., bulldog) or derived words (e.g., doggy) share aspects of both form and meaning with the target word (in this case, dog). Word recognition for older adults appears to be more influenced by form-based aspects, such as the frequency that two subsequent letters co-occur in the language (e.g., the letter combination st occurs more often in English words than the combination sk).
To determine the factors that influence the word recognition speed across different groups, the researcher can manipulate certain variables in the stimulus set and then test the power of these variables to predict word recognition speed. For example, to test whether word recognition is driven by semantic or form-based factors, the stimulus set should include variables that reflect the degree of connectivity of a word to its semantic neighbors in the mental lexicon or its connectivity to other words that share part of its form.
This method was used in the current study to investigate whether word recognition speed is influenced by different factors in younger and older adults and in individuals with Alzheimer&#8217;s disease (AD) or mild cognitive impairment (MCI)3. The method described here is based on visual word recognition but can be adapted to the auditory modality. However, some variables that are significant predictors of reaction times in a typical visual lexical decision experiment might not predict response latencies in an auditory lexical decision or may have the opposite effect. For example, the phonological neighborhood has the opposite effect across these two modalities5: &#160;words with larger phonological neighborhoods exhibit a facilitatory effect on visual word recognition but result in longer response latencies in auditory lexical decision6.
Word-finding difficulties in older adults7 have been generally attributed to difficulty accessing the phonological word form rather than a breakdown of the semantic representation8. However, AD research has primarily focused on semantic declines9,10,11,12,13,14. It is important to disentangle how semantic and orthographic factors influence the recognition of written words in aging with and without cognitive decline. The influence of form-related factors is more pronounced in older than in younger adults, and it remains significant in people with MCI or AD3. Thus, this methodology can help us uncover features of the mental lexicon across different populations and identify changes in the lexicon&#8217;s organization with age and neuropathology. One concern when testing patients with neuropathology is that they may have difficulties accessing task-related knowledge. However, the lexical decision task is a simple task with no burden on working memory or other complex cognitive skills that many patients exhibit problems with. It has been considered appropriate for AD and MCI populations.

<table><tbody><tr><td>E-Prime</td><td>Psychology Software Tools</td><td>version 2.0.10.356.</td><td></td></tr><tr><td>PC with Windows and Keyboard</td><td></td><td></td><td></td></tr><tr><td>R</td><td>R Foundation for Statistical Computing</td><td>R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.</td><td></td></tr></tbody></table>

lexical decision task for studying written word recognition in adults with and without dementia or mild cognitive impairment

Older adults are slower at recognizing visual objects than younger adults. The same is true for recognizing that a letter string is a real word. People with Alzheimer&#39;s disease (AD) or Mild Cognitive Impairment (MCI) demonstrate even longer responses in written word recognition than elderly controls. Despite the general tendency towards slower recognition in aging and neurocognitive disorders, certain characteristics of words influence word recognition speed regardless of age or neuropathology (e.g., a word&#8217;s frequency of use). We present here a protocol for examining the influence of lexical characteristics on word recognition response times in a simple lexical decision experiment administered to younger and older adults and people with MCI or AD. In this experiment, participants are asked to decide as quickly and accurately as possible whether a given letter string is an actual word or not. We also describe mixed-effects models and principal components analysis that can be used to detect the influence of different types of lexical variables or individual characteristics of participants on word recognition speed.

Table 1 shows a list of variables that were obtained from three different sources (a corpus, a dictionary, and pilot testing of test items) that are included in the analysis as fixed-effect predictors. Many of these variables have been previously reported to affect word recognition speed.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Corpus:</td>
			<td></td>
		</tr>
		<tr>
			<td>Base frequency</td>
			<td>the numbe...

Watch this Scientific Journal Video about Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment at JoVE.com

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

This article describes how to implement a simple lexical decision experiment to assess written word recognition in neurologically healthy participants and in individuals with dementia and cognitive decline. We also provide a detailed description of reaction time analysis using principal components analysis (PCA) and mixed-effects modeling.

Older adults are slower at recognizing visual objects than younger adults. The same is true for recognizing that a letter string is a real word. People ...

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Research

JoVE Journal

Behavior

9.1K Views.  University of Helsinki. De lexicale beslissingstaak wordt gebruikt om de snelheid van woordherkenning te meten. Deze taak kan kenmerken van het mentale lexicon onthullen en hoe het lexicon verandert met veroudering en met neurodegeneratieve aandoeningen. Veel taaltaken vereisen de coördinatie van taal en andere aspecten van cognitie.De lexicale beslissingstaak is niet sterk afhankelijk van andere cognitieve vaardigheden, die in sommige populaties, zoals patiënten met dementie, kunnen worden aangetast. Helpe...

Video: Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Examining the Characteristics of Episodic Memory using Event-related Potentials in Patients with Alzheimer's Disease

Our laboratory uses event-related EEG potentials (ERPs) to understand and support behavioral investigations of episodic memory in patients with amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD). Whereas behavioral data inform us about the patients' performance, ERPs allow us to record discrete changes in brain activity. Further, ERPs can give us insight into the onset, duration, and interaction of independent cognitive processes associated with memory retrieval. In patient populations, these types of studies are used to examine which aspects of memory are impaired and which remain relatively intact compared to a control population. The methodology for collecting ERP data from a vulnerable patient population while these participants perform a recognition memory task is reviewed. This protocol includes participant preparation, quality assurance, data acquisition, and data analysis. In addition to basic setup and acquisition, we will also demonstrate localization techniques to obtain greater spatial resolution and source localization using high-density (128 channel) electrode arrays.

The methodology for collecting high-density event-related potential data while patients with Alzheimer's disease perform a recognition memory task is reviewed. This protocol will include subject preparation, quality assurance, data acquisition, and data analysis.

Our laboratory uses event-related EEG potentials (ERPs) to understand and support behavioral investigations of episodic memory in patients with amnestic ...

Medicine

Comparing the Frequency Effect Between the Lexical Decision and Naming Tasks in Chinese

In psycholinguistic research, the frequency effect can be one of the indicators for eligible experimental tasks that examine the nature of lexical access. Usually, only one of those tasks is chosen to examine lexical access in a study. Using two exemplar experiments, this paper introduces an approach to include both the lexical decision task and the naming task in a study. In the first experiment, the stimuli were Chinese characters with frequency and regularity manipulated. In the second experiment, the stimuli were switched to Chinese two-character words, in which the word frequency and the regularity of the leading character were manipulated. The logic of these two exemplar experiments was to explore some important issues such as the role of phonology on recognition by comparing the frequency effect between both the tasks. The results revealed different patterns of lexical access from those reported in the alphabetic systems. The results of Experiment 1 manifested a larger frequency effect in the naming task as compared to the LDT, when the stimuli were Chinese characters. And it is noteworthy that, in Experiment 1, when the stimuli were regular Chinese characters, the frequency effect observed in the naming task was roughly equivalent to that in the LDT. However, a smaller frequency effect was shown in the naming task as compared to the LDT, when the stimuli were switched to Chinese two-character words in Experiment 2. Taking advantage of the respective demands and characteristics in both tasks, researchers can obtain a more complete and precise picture of character/word recognition.

Researchers adopt both the lexical decision task and the naming task to investigate some important topics such as character/word recognition by comparing the frequency effect between these two tasks. This article introduces this approach through two exemplar experiments and elaborates on the underlying logic.

In psycholinguistic research, the frequency effect can be one of the indicators for eligible experimental tasks that examine the nature of lexical access. ...

The 4 Mountains Test: A Short Test of Spatial Memory with High Sensitivity for the Diagnosis of Pre-dementia Alzheimer's Disease

This protocol describes the administration of the 4 Mountains Test (4MT), a short test of spatial memory, in which memory for the topographical layout of four mountains within a computer-generated landscape is tested using a delayed match-to-sample paradigm. Allocentric spatial memory is assessed by altering the viewpoint, colors and textures between the initially presented and target images.
Allocentric spatial memory is a key function of the hippocampus, one of the earliest brain regions to be affected in Alzheimer&#39;s disease (AD) and impairment of hippocampal function predates the onset of dementia. It was hypothesized that performance on the 4MT would aid the diagnosis of predementia AD, which manifests clinically as Mild Cognitive Impairment (MCI).
The 4MT was applied to patients with MCI, stratified further based on cerebrospinal fluid (CSF) AD biomarker status (10 MCI biomarker positive, 9 MCI biomarker negative), and with mild AD dementia, as well as healthy controls. Comparator tests included tests of episodic memory and attention widely accepted as sensitive measures of early AD. Behavioral data were correlated with quantitative MRI measures of the hippocampus, precuneus and posterior cingulate gyrus.
4MT scores were significantly different between the two MCI groups (p = 0.001), with a test score of &#8804;8/15 associated with 100% sensitivity and 78% specificity for the classification of MCI with positive AD biomarkers, i.e., predementia AD. 4MT test scores correlated with hippocampal volume (r = 0.42) and cortical thickness of the precuneus (r = 0.55).
In conclusion, the 4MT is effective in identifying the early stages of AD. The short duration, easy application and scoring, and favorable psychometric properties of the 4MT fulfil the need for a simple but accurate diagnostic test for predementia AD.

This article describes the 4 Mountains Test (4MT), a hippocampus-dependent test of working allocentric spatial memory. The hippocampus is affected early in Alzheimer's disease (AD) and this article outlines the 4MT methodology and results of patient testing, which demonstrates the value of the 4MT in the diagnosis of pre-dementia AD.

This protocol describes the administration of the 4 Mountains Test (4MT), a short test of spatial memory, in which memory for the topographical layout of ...

The Deese-Roediger-McDermott (DRM) Task: A Simple Cognitive Paradigm to Investigate False Memories in the Laboratory

The Deese,&#160;Roediger and McDermott&#160;(DRM) task is a false memory paradigm in which subjects are presented with lists of semantically related words (e.g., nurse, hospital, etc.) at encoding. After a delay, subjects are asked to recall or recognize these words. In the recognition memory version of the task, subjects are asked whether they remember previously presented words, as well as related (but never presented) critical lure words (&#39;doctor&#39;). Typically, the critical word is recognized with high probability and confidence. This false memory effect has been robustly demonstrated across short (e.g., immediate, 20 min) and long (e.g., 1, 7, 60 d) delays between encoding and memory testing. A strength of using this task to study false memory is its simplicity and short duration. If encoding and retrieval components of the task occur in the same session, the entire task can take as little as 2 - 30 min. However, although the DRM task is widely considered a &#39;false memory&#39; paradigm, some researchers consider DRM illusions to be based on the activation of semantic memory networks in the brain, and argue that such semantic gist-based false memory errors may actually be useful in some scenarios (e.g., remembering the forest for the trees; remembering that a word list was about &#34;doctors&#34;, even though the actual word &#34;doctor&#34; was never presented for study). Remembering the gist of experience (instead of or along with individual details) is arguably an adaptive process and this task has provided a great deal of knowledge about the constructive, adaptive nature of memory. Therefore, researchers should use caution when discussing the overall reach and implications of their experiments when using this task to study &#39;false memory&#39;, as DRM memory errors may not adequately reflect false memories in the real world, such as false memory in eyewitness testimony, or false memories of sexual abuse.

The Deese-Roediger-McDermott DRM Task: A Simple Cognitive Paradigm to Investigate False Memories in the Laboratory

Here we present the Deese,&#160;Roediger and McDermott&#160;(DRM) task, a tool to study false memories in the laboratory. Subjects study lists of semantically related words (e.g.,&#160;nurse, sick, etc.), and later falsely remember an unstudied word (doctor) that represents the gist, or theme, of the word list.

The Deese,&#160;Roediger and McDermott&#160;(DRM) task is a false memory paradigm in which subjects are presented with lists of semantically related words ...

Transcranial Direct Current Stimulation (tDCS) of Wernicke's and Broca's Areas in Studies of Language Learning and Word Acquisition

Language is a highly important yet poorly understood function of the human brain. While studies of brain activation patterns during language comprehension are abundant, what is often critically missing is causal evidence of brain areas&#39; involvement in a particular linguistic function, not least due to the unique human nature of this ability and a shortage of neurophysiological tools to study causal relationships in the human brain noninvasively. Recent years have seen a rapid rise in the use of transcranial direct current stimulation (tDCS) of the human brain, an easy, inexpensive and safe noninvasive technique that can modulate the state of the stimulated brain area (putatively by shifting excitation/inhibition thresholds), enabling a study of its particular contribution to specific functions. While mostly focusing on motor control, the use of tDCS is becoming more widespread in both basic and clinical research on higher cognitive functions, language included, but the procedures for its application remain variable. Here, we describe the use of tDCS in a psycholinguistic word-learning experiment. We present the techniques and procedures for application of cathodal and anodal stimulation of core language areas of Broca and Wernicke in the left hemisphere of the human brain, describe the procedures of creating balanced sets of psycholinguistic stimuli, a controlled yet naturalistic learning regime, and a comprehensive set of techniques to assess the learning outcomes and tDCS effects. As an example of tDCS application, we show that cathodal stimulation of Wernicke&#39;s area prior to a learning session can impact word learning efficiency. This impact is both present immediately after learning and, importantly, preserved over longer time after the physical effects of stimulation wear off, suggesting that tDCS can have long-term influence on linguistic storage and representations in the human brain.

Transcranial Direct Current Stimulation tDCS of Wernicke's and Broca's Areas in Studies of Language Learning and Word Acquisition

Here, we describe a protocol for using transcranial direct current stimulation for psycho- and neurolinguistic experiments aimed at studying, in a naturalistic yet fully controlled way, the role of cortical areas of the human brain in word learning, and a comprehensive set of behavioral procedures for assessing the outcomes.

Language is a highly important yet poorly understood function of the human brain. While studies of brain activation patterns during language comprehension ...

Highlighting and Reducing the Impact of Negative Aging Stereotypes During Older Adults' Cognitive Testing

As life expectancy increases, aging has become a major health challenge, resulting in a huge effort to better discriminate between normal and pathological cognitive decline. It is thus essential that cognitive tests and their administration are as fair as possible. However, an important source of bias during cognitive testing comes from negative aging stereotypes that can impair the memory performance of older adults and inflate age differences on cognitive tasks. The fear of confirming negative aging stereotypes creates an extra pressure among older adults which interferes with their intellectual functioning and leads them to perform below their true abilities. Here, we present a protocol that highlights simple but efficient interventions to alleviate this age-based stereotype threat effect. The first study showed that simply informing older participants about the presence of younger participants (threat condition) led older adults to underperform on a standardized memory test compared with younger participants, and that this performance difference was eliminated when the test was presented as age-fair (reduced-threat condition). The second study replicated these findings on short cognitive tests used to screen for predementia in clinical settings and showed that teaching older adults about stereotype threat inoculated them against its effects. These results provide useful recommendations about how to improve older adults' memory assessment both in Iab studies and in clinical settings.

Here, we present a protocol designed to show how negative aging stereotypes can impair memory performance of older adults during cognitive testing and how to reduce this deleterious effect. This method can help older people to perform at an optimal level during testing in both lab studies and clinical settings.

As life expectancy increases, aging has become a major health challenge, resulting in a huge effort to better discriminate between normal and pathological ...

An Experimental Paradigm for Measuring the Effects of Ageing on Sentence Processing

Previous studies have found that older adults have greater difficulties in processing syntactically complex sentences than younger adults. However, the exact regions where the difficulties arise have not been fully identified. In this study, a maze task was implemented to investigate how older adults and younger adults differentially processed two types of sentences with different levels of syntactic complexity, namely subject relative clauses and object relative clauses. Participants were asked to choose between two alternatives at each segment of the sentences. The task required participants to engage in a strictly incremental mode of processing. The reading times for each segment were recorded, allowing the quantification of the difficulty of sentence reading. The task allowed us to identify the exact locations of the processing difficulty and thus facilitate a more accurate assessment of the age-related decline in sentence processing. The results indicate that the effect of ageing was found mainly at the head nouns, but not at other regions of the sentences, a finding which suggests that the maze task is an effective method to identify the exact location of the ageing effect on sentence processing. The implications of this experimental paradigm for investigating the effect of ageing on sentence processing are discussed.

Here, we present a protocol to examine the age-related decline in sentence processing using a maze task that allowed us to localize the processing difficulty at each word of the sentences during reading.

Previous studies have found that older adults have greater difficulties in processing syntactically complex sentences than younger adults. However, the ...

Evaluation of the Cognitive Performance of Hypertensive Patients with Silent Cerebrovascular Lesions

Evidence accumulated from the last decade has proven that silent cerebrovascular lesions (SCLs) and their underlying pathogenic processes contribute to cognitive decline in the elderly. However, the distinct effects of each type of the lesions on cognitive performance remain unclear. Moreover, research data from Chinese elderly with SCLs is scarce. In this study, 398 otherwise healthy hypertensive elderly subjects (median age 72 years) were included and assessed. All participates were required to complete a battery of structured neuropsychological assessment, including forward and backward digit span tests, symbol digit modalities test, Stroop test, verbal fluency test and Montreal Cognitive Assessment. These tests were used to assess attention, executive function, information processing speed, language, memory and visuospatial function. A multi-sequence 3T MRI scanning was arranged within one month of the neuropsychological assessment to evaluate the burden of SCLs. SCLs were rated visually. Cerebral microbleeds (CMBs) and silent lacunes (SLs) were identified as strictly lobar CMBs and SLs or deep CMBs and SLs according to their locations, respectively. Similarly, white matter hyperintensities (WMHs) were separated into periventricular WMHs (PVHs) and deep WMHs (DWMHs). A series of linear regression models were used to assess the correlation between each type of SCLs and individual cognitive function domain. The results showed that CMBs tend to impair language-related cognition. Deep SLs affect executive function, but this association disappeared after controlling for other types of SCLs. PVHs, rather than DWMHs, are associated with cognitive decline, especially in executive function and processing speed. It is concluded that different aspects of SCLs have differential impact on cognitive performance in hypertensive elderly Chinese.

Here, we present a protocol to assess whether various types of silent cerebrovascular lesions are differentially associated with deficits in certain cognitive domains in a cohort of 398 hypertensive elderly Chinese, using a combination of neuropsychological tests and multi-sequence 3T MRI scanning.

Evidence accumulated from the last decade has proven that silent cerebrovascular lesions (SCLs) and their underlying pathogenic processes contribute to ...

Dual-Task Stroop Paradigm for Detecting Cognitive Deficits in High-Functioning Stroke Patients

General clinical cognitive assessment scales are not sensitive enough to cognitive impairment in high-functioning stroke patients. The dual-task assessment has advantages for identifying cognitive deficits in high-functioning stroke patients and has been gradually applied in clinical assessment and cognitive training. Moreover, the Stroop paradigm has higher sensitivity and specificity for attentional assessment than conventional clinical cognitive assessment scales. Therefore, this study presents the dual-task assessment based on the Stroop paradigm to identify cognitive deficits in high-functioning stroke patients. This study demonstrates a single- and dual-task evaluation based on the Stroop paradigm and confirms its feasibility through case experiments and synchronized functional near-infrared spectroscopy evaluation. The Stroop reaction time and correct rate are used as the main indicators to evaluate the cognitive level of the subjects. This study protocol aims to provide new ideas to figure out the ceiling effect in general clinical assessment failure for high-functioning stroke patients.

Clinical assessment scales are notsensitive enough to cognitive dysfunction in high-functioning stroke patients. The dual-task paradigm presents advantages and potential in the assessment and cognitive training of cognitive dysfunction. The study here proposes a dual-task Stroop paradigm to identify cognitive dysfunction in high-functioning stroke patients.

General clinical cognitive assessment scales are not sensitive enough to cognitive impairment in high-functioning stroke patients. The dual-task ...

Neuroscience

Anterograde Amnesia

Source: Laboratories of Jonas T. Kaplan and Sarah I. Gimbel&#8212;University of Southern California
Anterograde amnesia is the loss of the ability to form new memories. This can be distinguished from retrograde amnesia, which is the loss of old memories. Anterograde amnesia can result from damage to structures in the brain that are involved in the formation of new memories. Patients who have damage to the structures of the medial temporal lobe, including the hippocampus, amygdala, and the surrounding cortices, often have severe deficits in the formation of certain kinds of memories. These cases can be informative as to how memory is organized in the brain, and how different systems support different kinds of memories. 
In this video, we will test a patient with medial temporal lobe damage on a series of memory tasks designed to distinguish between different forms of memory. First, we will test short-term or working memory, which is the process we use to keep information in mind temporarily. Next, we will test two different forms of long-term memory: explicit and implicit memory. Explicit memories are conscious and easy to verbalize. For example, memories of facts or episodes from our lives are explicit memories. We can easily tell someone what we ate for breakfast, or what city is the capital of France. Implicit memory involves knowledge we gain from experience but that is not easily expressible. For example, knowing how to do things, or becoming habituated to a stimulus are forms of implicit memory. 
These procedures are based in part on studies of the famous patient Henry Molaison, also known by his initials H.M., who had severe anterograde amnesia as a result of a surgery for intractable epilepsy in which parts of both temporal lobes were resected.1 We will perform a test of digit span, which measures short-term memory, a test of paired-associate learning, which measures explicit or declarative memory, and mirror-drawing, a test of implicit skill learning.2

Testing Short- and Long-term Memories in Patients with Brain Injuries

Source: Laboratories of Jonas T. Kaplan and Sarah I. Gimbel&#8212;University of Southern California
Anterograde amnesia is the loss of the ability to form ...

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

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