Lexical factors and cerebral regions influencing verbal fluency performance in MCI.

Neuropsychologia 54 (2014) 98–111

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Neuropsychologia journal homepage: www.elsevier.com/locate/neuropsychologia

Lexical factors and cerebral regions influencing verbal fluency performance in MCI D.G. Clark a,b,n, V.G. Wadley c, P. Kapur d, T.P. DeRamus e, B. Singletary f, A.P. Nicholas a,b, P.D. Blanton a, K. Lokken a, H. Deshpande g, D. Marson b, G. Deutsch g a

Birmingham VA Medical Center, USA Department of Neurology, University of Alabama at Birmingham, USA c Division of Gerontology, Geriatrics, and Palliative Care, Department of Medicine, University of Alabama at Birmingham, USA d Department of Medicine, Mount Sinai School of Medicine, USA e Department of Psychology and Behavioral Neuroscience, University of Alabama at Birmingham, USA f Department of Surgery, University of Alabama at Birmingham, USA g Department of Radiology, University of Alabama at Birmingham, USA b

art ic l e i nf o

a b s t r a c t

Article history: Received 26 July 2013 Received in revised form 26 September 2013 Accepted 11 December 2013 Available online 30 December 2013

Objective: To evaluate assumptions regarding semantic (noun), verb, and letter fluency in mild cognitive impairment (MCI) and Alzheimer disease (AD) using novel techniques for measuring word similarity in fluency lists and a region of interest (ROI) analysis of gray matter correlates. Method: Fifty-eight individuals with normal cognition (NC, n ¼25), MCI (n ¼23), or AD (n ¼ 10) underwent neuropsychological tests, including 10 verbal fluency tasks (three letter tasks [F, A, S], six noun categories [animals, water creatures, fruits and vegetables, tools, vehicles, boats], and verbs). All pairs of words generated by each participant on each task were compared in terms of semantic (meaning), orthographic (spelling), and phonemic (pronunciation) similarity. We used mixed-effects logistic regression to determine which lexical factors were predictive of word adjacency within the lists. Associations between each fluency raw score and gray matter volumes in sixteen ROIs were identified by means of multiple linear regression. We evaluated causal models for both types of analyses to specify the contributions of diagnosis and various mediator variables to the outcomes of word adjacency and fluency raw score. Results: Semantic similarity between words emerged as the strongest predictor of word adjacency for all fluency tasks, including the letter fluency tasks. Semantic similarity mediated the effect of cognitive impairment on word adjacency only for three fluency tasks employing a biological cue. Orthographic similarity was predictive of word adjacency for the A and S tasks, while phonemic similarity was predictive only for the S task and one semantic task (vehicles). The ROI analysis revealed different patterns of correlations among the various fluency tasks, with the most common associations in the right lower temporal and bilateral dorsal frontal regions. Following correction with gray matter volumes from the opposite hemisphere, significant associations persisted for animals, vehicles, and a composite nouns score in the left inferior frontal gyrus, but for letter A, letter S, and a composite FAS score in the right inferior frontal gyrus. These regressions also revealed a lateralized association of the left subcortical nuclei with all letter fluency scores and fruits and vegetables fluency, and an association of the right lower temporal ROI with letter A, FAS, and verb fluency. Gray matter volume in several bihemispheric ROIs (left dorsal frontal, right lower temporal, right occipital, and bilateral mesial temporal) mediated the relationship between cognitive impairment and fluency for fruits and vegetables. Gray matter volume in the right lower temporal ROI mediated the relationship between cognitive impairment and five fluency raw scores (animals, fruits and vegetables, tools, verbs, and the composite nouns score). Conclusion: Semantic memory exerts the strongest influence on word adjacency in letter fluency as well as semantic verbal fluency tasks. Orthography is a stronger influence than pronunciation. All types of fluency task raw scores (letter, noun, and verb) correlate with cerebral regions known to support verbal or nonverbal semantic memory. The findings emphasize the contribution of right hemisphere regions to fluency task performance, particularly for verb and letter fluency. The relationship between diagnosis

Keywords: Alzheimer0 s disease Mild cognitive impairment Verbal fluency Semantic memory Natural language processing

n

Correspondence to: 1720, 7th Ave. South, SC 620C, Birmingham, AL 35294, USA. Tel.: þ 1 205 996 6050; fax: þ1 205 975 7365. E-mail address: [email protected] (D.G. Clark).

0028-3932/$ - see front matter Published by Elsevier Ltd. http://dx.doi.org/10.1016/j.neuropsychologia.2013.12.010

D.G. Clark et al. / Neuropsychologia 54 (2014) 98–111

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and semantic fluency performance is mediated by semantic similarity of words and by gray matter volume in the right lower temporal region. Published by Elsevier Ltd.

1. Introduction Verbal fluency tasks are frequently used in neuropsychological assessment due to their brevity, ease of administration, independence from special equipment or props, and diagnostic utility. These tasks are typically scored by counting all of the valid items provided by the subject during a given time interval to obtain a raw score. Research on raw scores from various tasks has proven them to be useful for diagnosing dementia and discerning among various types of dementia (Duff-Canning, Leach, Stuss, Ngo, & Black, 2004; Monsch et al., 1994). More recent work suggests that verbal fluency raw scores perform better than chance for discerning between patients with mild cognitive impairment (MCI) who are destined to convert to AD and those who are not, albeit not as well as more sophisticated scoring methods introduced in the same article (Clark et al., in press). Several investigators have delved into the component processes that underlie performance on verbal fluency tasks, with the most prominent approach being to evaluate clustering and switching (Troyer, 2000; Troyer & Moscovitch, 2006; Troyer, Moscovitch, Winocur, Alexander, & Stuss, 1998). Clustering refers to the tendency for subjects to generate similar items in close succession during a fluency task. This phenomenon is thought to be due largely to unconscious processes, such as spreading activation in a network, and is considered to be relatively “automatic.” Switching refers to a more deliberate process, by which an individual makes a (possibly) conscious decision to shift from one subcategory of exemplars to a different subcategory. Work in this area has evaluated distinct scores for each of these component processes. Usually, the decision of whether a pair of consecutive words should be designated a switch or placed together in a cluster is made on semantic grounds for semantic fluency tasks and on phonological or orthographic grounds for letter fluency tasks (Troyer, 2000). However, some work suggests that there is useful information to be gained by quantifying “task discrepant” clustering and switching scores, e.g., semantic clusters and switches during a letter fluency task (Abwender, Swan, Bowerman, & Connolly, 2001). Beyond the application of verbal fluency to clinical assessment, there has also been interest in the neural basis for performance on these tasks. Performance on letter fluency tasks relies heavily on frontal–subcortical circuits, particularly in the left hemisphere (assuming left hemisphere language dominance). Some of this evidence is clinical, based on the fact that patients with known frontal–subcortical disruption, such as patients with vascular dementia (Duff-Canning et al., 2004), progressive supranuclear palsy, or Huntington disease (Monsch et al., 1994; Rosser & Hodges, 1994), perform worse on letter fluency. Evidence from brain imaging indicates that left frontal regions are activated during letter fluency tasks (Mummery, Patterson, Hodges, & Wise, 1996) and frontal lesions are associated with worse performance on letter fluency (Robinson, Shallice, Bozzali & Cipolotti, 2012). Similarly, temporal lobe structures are strongly associated with performance on semantic fluency tasks. Patients with semantic dementia, a degenerative disease associated with severe asymmetric temporal lobe atrophy, often perform poorly on semantic fluency tasks, generally worse than on letter fluency (Libon et al., 2009; Marczinski & Kertesz, 2006). Brain imaging studies give credence to this relationship (Mummery, Patterson, Ashburn, Frackowiak, & Hodges, 2000; Libon et al., 2009), but investigations of cerebral correlates of verbal fluency in MCI have

been limited to letter fluency, animals, vegetables, and things one finds in a supermarket (Ahn et al., 2011; Eastman et al., in press). Many factors could affect performance on these tasks and consequently their cerebral correlates. These factors include the overall size of the category (Diaz, Sailor, Cheung, & Kuslansky, 2004), the familiarity, imageability, age of acquisition, and lexical frequency of the items produced during the task, and variation in the cerebral topographic encoding of entities in the various categories (Damasio, Tranel, Grabowski, Adolphs, & Damasio, 2004; Simmons & Barsalou, 2003; Warrington & Shallice, 1984). We sought to evaluate the validity of assumptions regarding verbal fluency tasks in MCI and mild AD by two complementary methods—an analysis of fluency word list structure and an analysis of gray matter correlations with raw scores. The specific assumptions we wished to evaluate were as follows. First, the commonly used term ‘phonemic’ fluency to refer to letter fluency tasks suggests that individuals must attend to and act according to the speech sounds that compose the words generated during the task. However, the instructions pertain to orthography, not pronunciation, as subjects are told to generate words beginning with a certain letter. While the letter F in word-initial position can represent only one sound in English, the letter S may occur with at least two initial sounds (due to words beginning with sh), and the letter A may occur with at least eight initial sounds for valid items (consider the pronunciations of the words amen, avalanche, asparagus, automobile, aisle, ache, arachnid, and aegis, all of which are assigned different initial phonemes in the Carnegie-Mellon University (CMU) electronic pronunciation dictionary). Not only do the instructions generally fail to specify an initial phoneme, they exclude words that begin with the same phonemes as the target words if they do not begin with the correct letter. For example, words that are spelled with initial ‘ph,’ despite beginning with an /f/ phoneme, are disallowed, and words that are spelled with initial ‘c’ are disallowed in the S-words task even when the ‘c’ is pronounced like an /s/. Based on these observations, we wished to evaluate the importance of phonemic vs. orthographic similarity for predicting word adjacency in fluency word lists. Second, there is an assumption that semantic fluency depends primarily on semantic memory and we wished to evaluate the contribution of semantic similarity for predicting word adjacency in both letter and semantic tasks. To this end, we employed a vector space model of semantics (Turney & Pantel, 2010; Widdows, 2004). The intuition behind such models is that the meaning of a target word may be summarized by the counts of context words that occur in its surroundings within a large corpus of text. For our purposes, the target words were all of the words generated by any study subject during any verbal fluency task and the context words were taken from the Google n-grams corpus. The “meaning” of a target word in such a model is based on the associated vector of context word counts. The key assumption of this approach is that the information within a large volume of English literature reflects shared qualities of the organization of concepts in the minds of English-speaking people, including semantic associations that may never be made explicit. The analysis of gray matter volume in specific regions of interest was designed to evaluate the neuroanatomical underpinnings of these relationships, with the expectation that if letter fluency task performance is driven by attention to sublexical information, such as speech sounds, then the raw scores should correlate not only with dorsal frontal regions, but also with regions

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known to support auditory processing, lexical and sublexical representations, and articulation (specifically, left superior temporal and inferior parietal regions, and the inferior frontal gyrus). Similarly, we anticipated that if semantic fluency performance depends chiefly on the integrity of semantic memory, then raw scores on these tasks should correlate most strongly with gray matter volume in temporal lobe structures, and possibly in mesial parietal or occipital regions. Because fluency tasks entail verbal output, we anticipated stronger correlations with left hemisphere regions. However, there is evidence that semantic memory has a bihemispheric organization (Damasio et al., 2004; Lambon Ralph, Cipolotti, Manes, & Patterson, 2010; Mendez, Kremen, Tsai, & Shapira, 2010) and disruption of networks supporting nonverbal semantic associations could certainly impact performance on verbal semantic tasks (i.e., if one cannot nonverbally conceive of a zebra, it will be more difficult to generate the word zebra in a fluency task). We therefore wished to explore both right and left hemisphere correlations with verbal fluency task performance.

poor memory and evidence of impairment in one or more cognitive domains (e.g., memory, language, executive function) but intact routine activities of daily living and little or no evidence of functional decline were placed in the mild cognitive impairment (MCI) group (Petersen et al., 2001; Petersen, Smith, Waring, Ivnik, Tangalos, & Kokmen, 1999). Alzheimer Disease (AD) was diagnosed on the basis of the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer0 s Disease and Related Disorders Association (NINCDS-ADRDA) criteria for probable Alzheimer disease (McKhann et al., 1984). Demographic data on the participants are presented in Table 1. Four participants were excluded from the analysis for not meeting criteria for any of the diagnostic groups. Of 37 participants assigned to the NC group, 25 were selected to match the MCI group as closely as possible in terms of age, sex, and educational level. Five MRI scans were lost due to a technical problem, including three scans on NC participants, one scan on an MCI participant, and one scan on an unclassified participant. The final participant sample consisted of 58 research participants, comprising twenty-five NC participants (22 with MRI), 23 patients with MCI (22 with MRI), and 10 patients with mild AD (all 10 with MRI). Participant groups were compared in terms of age and educational level using multiple linear regression, reordering the diagnosis factor to obtain all three comparisons. We employed Fisher0 s exact test to compare the three groups in terms of sex. All participants provided written informed consent in accordance with the Declaration of Helsinki and all study procedures were approved by the Institutional Review Boards of both the Birmingham VA Medical Center and the University of Alabama at Birmingham.

2. Subjects and methods 2.2. Neuropsychological testing 2.1. Participants Seventy-four participants were recruited into a longitudinal study of MCI and Alzheimer disease through advertisements in the community and the Memory Disorders clinics at the Birmingham VA Medical Center and the University of Alabama at Birmingham. All participants were right-handed, native speakers of English with no contraindication to 3-T magnetic resonance imaging (3 T MRI) and no evidence of other causes of cognitive dysfunction, such as Parkinson disease, drug or alcohol abuse, or liver failure. Participants were assigned to diagnostic categories by a consensus panel consisting of two neurologists (DGC and APN) and two neuropsychologists (KL and PDB). The consensus panel considered each participant on the basis of subjective complaints of memory or cognitive dysfunction, a battery of neuropsychological tests, and an informant-based questionnaire regarding instrumental activities of daily living. Participants with normal cognitive test performance and no evidence of functional decline were placed in the cognitively normal control (NC) group. Participants with subjective complaints of

Participants underwent a battery of neuropsychological tests, including tests of general cognitive function (extended version of the mini-mental state exam (Folstein, Folstein, & McHugh, 1975)), memory (California Verbal Learning Test (CVLT—Delis, Kramer, Kaplan, & Ober, 2000), 10/36 Spatial Recall), language and semantic memory (30-item version of the Boston Naming Test (BNT—Kaplan, Goodglass, & Weintraub, 1983), Pyramids and Palm Trees (PPT—Howard & Patterson, 1992)), and executive function (Trail Making Tests A & B, Reitan, 1958). Ten verbal fluency tasks were administered to each participant, including three letter fluency tasks (F, A, and S), and six noun category fluency tasks (three for living things: animals, water creatures, fruits and vegetables, three for non-living things: vehicles, boats, and tools), and 1 abstract syntactic category (verbs). Neuropsychological test scores are shown in Table 1. Groupwise comparisons were performed for each neuropsychological test using multiple linear regression, initially comparing AD and NC groups to the MCI group, but reordering factors as needed to obtain the remaining group comparisons.

Table 1 Demographics and neuropsychological test scores.

Age (years) Sex (M:F) Education (years) Letter F Letter A Letter S Sum of F, A, S Animals Water creatures Fruits and vegetables Tools Vehicles Boats Nouns Verbs Boston Naming Test Trail Making Test A Trail Making Test B Pyramids and palm trees (words) Pyramids and palm trees (pictures) CVLT LDFR CVLT LDCR 10/36 Total 10/36 Delayed xMMSE

NC (n¼ 25)

MCI (n¼ 23)

AD (n¼ 10)

70.1 (6.9) 13:12 16.2 (2.5) 12.8 (4.4) 9.7 (4.3) 13.5 (4.7) 36.1 (12.2) 17.5 (5.1) 11.9 (3.7) 16.0 (4.8) 12.6 (2.7) 12.4 (2.9) 9.1 (2.7) 81.5 (9.7) 15.9 (3.6) 27.7 (2.1) 33.3 (9.8) 84.5 (29.4) 50.8 (1.0) 50.0 (1.1) 10.5 (3.4) 11.3 (3.0) 19.2 (5.2) 7.1 (2.5) 47.5 (2.2)

70.7 (7.4) 18:5 14.8 (2.7) 10.0 (4.5) 8.1 (3.9) 11.0 (4.6) 29.0 (11.4) 14.4 (4.5) 9.0 (3.1) 13.0 (3.7) 9.7 (4.0) 9.0 (4.0) 6.7 (3.0) 61.8 (17.2) 10.3 (5.2) 26.0 (4.2) 44.2 (14.2) 130.7 (63.6) 50.0 (1.8) 49.4 (1.5) 5.9 (4.2) 7.6 (3.6) 16.3 (4.8) 5.5 (2.4) 43.8 (4.8)

74.7 (7.8) 7:3 14.8 (2.7) 9.7 (4.5) 6.9 (5.4) 10.7 (5.6) 27.3 (14.6) 10.8 (3.4) 5.6 (2.7) 7.8 (4.7) 8.0 (4.7) 9.4 (4.8) 4.2 (2.8) 45.8 (18.5) 9.3 (5.0) 20.7 (5.2) 80.7 (70.6) 180.1 (84.6) 48.0 (5.0) 46.3 (5.2) 1.6 (1.3) 2.6 (1.4) 13.4 (3.1) 5.5 (1.8) 35 (9.0)

ns ns ns ns, F4 M ns ns ns, F4 M NC, MCI4AD NC, MCI4AD NC, MCI4AD, F 4M NC4MCI4 AD ns NC, MCI4AD, edu NC4MCI4 AD NC4MCI, AD NC, MCI4AD edu NC, MCIo AD NCoMCI, AD NC, MCI4AD edu NC, MCI4AD NC4MCI4 AD edu NC4MCI4 AD edu NC4MCI, AD NC4MCI NC4MCI4 AD

NC¼ normal control, MCI¼ mild cognitive impairment, AD ¼ Alzheimer disease, CVLT ¼California verbal learning test, CMU Pronouncing Dictionary (2013), LDFR¼ long-delay free recall, LDCR¼ long-delay cued recall, xMMSE ¼extended mini-mental state exam. “Nouns” refers to the sum of all the semantic fluency scores for concrete noun categories. These group comparisons should be interpreted with caution, as most of the scores shown here were used by the consensus panel to assign diagnoses. With regard to fluency scores, the consensus panel was given the score for animals, the FAS composite score, and a composite score consisting of the sum of all noun categories and verbs. Comparisons were performed with multiple linear regression, with the exception of sex (for which we used Fisher0 s exact test).


2.3. MRI scan and preprocessing Participants underwent a high-resolution T1-weighted MRI scan within one month of cognitive testing. The T1-weighted MPRAGE scans were acquired on a Phillips Integra 3T scanner with a single channel transmit–receive coil (TR/TE/flip angle ¼ 8.108 ms/3.6 ms/81, 230 mm FOV, 1 mm slice, 135 slices, 256 matrix 256 matrix). Cortical reconstruction and volumetric segmentation were performed with the Freesurfer image analysis suite (version 5.3), which is documented and freely available for download online (http://surfer.nmr.mgh.harvard.edu/). The technical details of these procedures have been described previously (Dale, Fischl, & Sereno, 1999; Fischl & Dale, 2000; Fischl et al., 2002; Fischl, Sereno, & Dale, 1999; Fischl, Sereno, Tootell et al., 1999; Fischl et al., 2004; Segonne et al., 2004). All segmentations were individually inspected and manually corrected. Cortical and subcortical regions of interest from the Desikan–Killiany atlas were automatically extracted and measured as part of the default FreeSurfer processing pipeline.

2.4. Lexical analysis

occurrences by the summed counts of all context words in the sample. The probability of an individual word was calculated by dividing the unigram count of the word by the total of all unigram counts for all words in the sample (i.e., target and context words). The PMI transformation attenuates the influence of uninformative words on the calculation of word similarity. The vector of PMI values for the target word dog, for example, included the entries dingo (6.00), mongrel (6.13), junkyard (6.27), woofed (6.73), as well as somewhat less informative entries such as buns (3.99), biscuits (4.41), and unthankful (4.15). Such vectors of context words represent a composite of all possible meanings for a given target word. For example, the context word buns probably occurs here due to use of the word dog to refer to a frankfurter, and the context word unthankful is likely a modifier for dog when used as a pejorative term for a person. Such a composite semantic representation is preferable for the letter fluency tasks, because the precise intended meanings of polysemous words generated during letter fluency cannot be inferred from the task instructions. Vectors of context words and their corresponding PMI values were used to compute a similarity between words that occurred in the same fluency list. The similarity metric was calculated using the following equation. sim ¼

We sought to evaluate the contributions of semantic similarity, orthographic similarity, and phonemic similarity to the structure of verbal fluency lists, specifically the importance of these factors in determining the tendency for two words to be produced consecutively during performance of verbal fluency tasks. Numerical metrics with good face validity were derived for each of these factors.

2.4.1. Semantic similarity A method for quantifying the semantic similarity between two words was developed using methods employed for information retrieval (Turney & Pantel, 2010; Widdows, 2004). The key assumption of this method, which has worked well in practice for companies such as Google, is that an accurate representation of a word0 s meaning may be derived by examining the words that occur around it in actual text. To implement this approach, we placed all words generated by the participants during the verbal fluency tasks (“target” words) in a list and used the Google n-gram corpus to generate a list of context words for each word at a distance of up to four words (Michel et al., 2011; http://books.google.com/ngrams). The Google n-gram corpus consists of counts of small groups of consecutive words from one to five words in length (denoted as unigrams, bigrams, etc.) that have recurred in millions of books over the past 200 years. Thus, for this research, the unigrams corpus was searched to count the number of times the word dog occurred in works of English fiction published since the 1800s. The bigrams corpus was searched to generate a list all words that occurred adjacent to the word dog and to count their occurrences. For n-grams of greater length, such as trigrams, only those in which the target word occurred in the initial or final position were considered. Furthermore, if the target word was initial (or final) in the n-gram, only the final (or initial) word in the n-gram was listed or counted, as intervening words should already have been listed and counted when the shorter n-grams were searched. This procedure yielded a list of context words for each target word, along with a count of each context word. The raw counts of the context words nearly always included high counts for uninformative, non-specific words, such as the and of. The counts were therefore transformed into positive point-wise mutual information (PMI), according to the following equation: pðxyÞ PMI ¼ max ;0 ð1Þ pðxÞpðyÞ Thus, positive point-wise mutual information equals the natural logarithm of the probability that the target and context word occur together, divided by the product of the probabilities of the target and context words, or zero if this logarithm is negative. The probability of a context word was calculated by dividing the count of its

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vT w ‖v‖‖w‖

ð2Þ

This equation represents the dot product of two vectors, v and w, of PMI measurements, i.e., the sum of the products of the PMIs of all of the context words that they have in common, divided by the product of the vector magnitudes. The magnitude of a vector is the square root of the sum of squares of all of the entries in the vector (a generalization of the Pythagorean theorem to more than two dimensions). This similarity metric is equal to the cosine of the angle between the two vectors, and is 1.0 for identical vectors and 0.0 for orthogonal vectors (i.e., vectors with no context words in common). Examples of high, medium, and low semantic similarity values are shown in Table 2.

2.4.2. Orthographic similarity (string overlap) The extent to which two words are spelled the same was quantified using the following equation: δðα ;β Þ max ∑lk ¼ 1 max 1 þ jmm ðkk þ jÞj 1rmrp 0rjrd overlap ¼ ð3Þ p In Eq. (3), d is the difference in length between the two words, l is the length of the shorter word, p is the length of the longer word, αm is the mth letter of the longer word, βk is the kth letter of the shorter word, and δ is a function that returns 1 if the two letters are the same and 0 otherwise. Non-alphabetical characters (spaces, dashes) were removed prior to calculating the overlap. A simple algorithm for computing this value is depicted in Fig. 1. Examples of high, medium, and low orthographic similarity values are listed in Table 2.

2.4.3. Phonemic similarity Similarity of pronunciation between two words was calculated using pronunciations from an electronic resource developed at Carnegie Mellon University (The CMU Pronouncing Dictionary) included with the Natural Language Toolkit (NLTK—Bird, Loper, & Klein, 2009) for the Python programming language. The CMU dictionary is an electronic corpus of words and pronunciations encoded as sequences of standard alphanumeric symbols. For example, the pronunciation of cat in the CMU dictionary consists of the list [‘K’, ‘AE1’, ‘T’]. The numeral ‘1’ indicates that the vowel ‘AE’ takes primary stress. Phonemic similarity between words generated during verbal fluency tasks was calculated using the same procedure described in Section 2.4.2 for orthographic similarity, but using lists of phonetic symbols from the CMU dictionary instead of character strings. Numerals representing accent patterns were stripped from the vowel symbols prior to computing the phonemic overlap.

Table 2 Examples of high, medium, and low similarity items for each metric. Semantic similarity

Orthographic similarity

Phonemic similarity

High

Collard greens Garlic Elk

Turnip greens Onion Moose

0.656 0.483 0.439

Attitude Funny Feather

Altitude Funky Father

0.938 0.900 0.786

Sea Start Symbol

See Starch Simple

1.00 0.850 0.830

Medium

Eggplant Spider Hot air balloon

Lettuce Snake Surfboard

0.148 0.148 0.148

Alphabet Skate Leopard

Adolescent Water Pelican

0.298 0.300 0.298

Carrot Beans Cockatoo

Corn Quinoa Kangaroo

0.400 0.400 0.389

Low

Motor scooter Tangerine Fish

Cruise ship Field peas Finalist

5.5 10 5 6.0 10 5 3.5 10 5

Osprey Mushroom Pontoon boat

Raccoon Clementine Cruise ship

0.064 0.064 0.009

Tangerine Watermelon Although

Brussels sprout Grapefruit After

0.013 0.019 0.0

Three examples each of high, medium, and low scoring word pairs are provided for each lexical comparison variable. All measures have a theoretical maximum of 1.0 and minimum of 0.0.

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Fig. 1. Illustration of the algorithm for calculating Eq. (3). (A) The two words to be compared are initially placed with their first letters lined up (i.e., the lag variable, j, is set to zero). Each letter in the shorter word is then compared to each letter in the longer word and a quotient is calculated, with the numerator equal to one if the letters are the same and equal to zero otherwise. The denominator is proportional to the distance between the positions of the two letters at the current lag, and equals 1 if the letters are perfectly aligned, 1/2 if they are offset by 1, 1/3 if they are offset by 2, etc. The maximum quotient for each letter of the shorter word is obtained and all of the quotients are summed and divided by the length of the longer word. (B) The shorter word is then shifted to the right by one letter (i.e., the lag variable, j, is incremented) and the process is repeated until the end of the shorter word lines up with the end of the longer word. (C) The maximum value at all lags is then taken as the final orthographic similarity. For phonemic similarity, the same procedure was followed, but with lists of phonemes instead of letters.

2.5. Statistical analysis of verbal fluency Because adjacency is a categorical variable and each task was associated with thousands of word pairs, scatterplots were not very informative. To visualize the contributions of lexical factors to word adjacency in the lists, we generated moving average plots for all ten fluency tasks. Plots were generated by the following method. First, the matrix containing all data within a given fluency task was sorted according to the lexical variable of interest (e.g., semantic similarity), with values ascending. Then, with the data matrix thus sorted, a moving average of the adjacency variable was calculated with a window of 1500 measurements. This moving average was essentially the proportion of adjacent word pairs among 1500 word pairs at a given level of lexical similarity. The value of the moving average was then plotted against the rank of the lexical measurement values. An increasing value represents increasing probability of adjacency with increasing values of a given lexical comparison, with steeper curves suggesting a stronger relationship. The contributions of these lexical factors to verbal fluency word list structure were evaluated using mixed-effects logistic regression with the lme4 package in R (The R Core Team, 2012; Bates, Maechler, & Bolker, n.d.). Measures of semantic, orthographic, and phonemic similarity were calculated for each pairing of words from the verbal fluency lists produced by the participants. These variables were entered into a regression model with word adjacency as the dependent variable. A random intercept was included for each subject. The raw score achieved on the task during which the two words were generated was included as a covariate, as the proportion of word pairs that are adjacent is necessarily smaller when the list is longer. We produced a separate regression model for each of the ten verbal fluency tasks. Diagnosis (a factor with three levels) was added to each model and removed if it did not contribute significantly. Correction for multiple comparisons from all the mixed-effects models was undertaken with false discovery rate (FDR—Benjamini & Hochberg, 1995). We then evaluated a causal model (Fig. 2A) that addressed the question of whether any of these forms of lexical similarity mediated the relationship between diagnosis and word adjacency (Baron & Kenny, 1986; MacKinnon, Fairchild & Fritz, 2007; MacKinnon, Lockwood, & Williams, 2004). We undertook two versions of this analysis. In the first version, we included any diagnosis of cognitive impairment (MCI or AD) as the primary causative variable. In the second version, in order to be certain that effects observed in the first analysis were not driven entirely by the AD group, we entered only the diagnosis of AD as the primary causative variable. Each analysis comprised three mixedeffects regression models, as follows: (1) Adj τDx þcovariatesþ ε1 (2) Sim αDx þcovariatesþ ε2 (3) Adj τ0 Dx þβSimþ covariatesþ ε3

Fig. 2. Causal models for evaluating mediator effects. (A) The direct model (top) evaluates the significance of the relationship between diagnosis and word adjacency (coefficient τ). A second model evaluates the significance of the relationship between diagnosis and word similarity (i.e., semantic, orthographic, or phonemic similarity, represented by coefficient α). A third model is generated by adding word similarity to the direct model. This model evaluates the significance of the relationship between word similarity and word adjacency (coefficient β) and permits the measurement of the reduction in coefficient τ (by subtracting τ τ0 ). If coefficients α, β, and τ were all significant, then the significance of the reduction in τ was estimated by constructing a bootstrapped 95% confidence interval. A significant reduction indicates that the relationship between the primary causal variable (diagnosis) and the outcome variable (word adjacency) is mediated by word similarity. (B) The direct model measures the significance of the relationship between diagnosis and verbal fluency raw score (τ). The second and third regression models ensure that diagnosis is significantly related to gray matter volume in a given ROI (α) and that gray matter volume influences fluency raw score (β). If all three of these relationships are statistically significant, then the significance of the reduction in τ is estimated with a bootstrapped 95% confidence interval. A significant reduction indicates that the relationship between the primary causal variable (diagnosis) and the outcome variable (fluency raw score) is mediated by gray matter volume in a particular ROI.

Model 1 tested the hypothesis that words generated by individuals with cognitive impairment or AD (Dx) were more likely to be adjacent (Adj). Another way of expressing this thought is that individuals with cognitive impairment or AD generated shorter lists, because the number of adjacent pairs in any list was always equal to 2/N, where N was the number of words in the list. Obviously, this hypothesis already had limited support from the findings in Table 1. We considered this hypothesis to be supported if the τ coefficient was significant. Model 2 tested the hypothesis that diagnosis (Dx) exerted some significant effect on word similarity (Sim) within lists. There was no obvious reason that this hypothesis should be true, but it seemed plausible that individuals with cognitive impairment or AD might select exemplars more haphazardly, resulting in a significant negative α coefficient, or might be more constrained in their overall word production, resulting in a significant positive coefficient. Model 3 evaluated the effect of adding the mediator variable (Sim) from Model 2 to Model 1. If the tendency for words to be adjacent (Adj) was mediated by word similarity (Sim), then we anticipated not only a significant β coefficient, but also a reliable reduction in the magnitude of the τ0 coefficient. In cases where all three regression coefficients (α, β, and τ) were statistically significant, we undertook a test of the significance of the reduction in τ caused by addition of the mediator variable by repeating the regressions on 1000 bootstrap samples of the data and measuring the reduction (τ–τ0 ) on each iteration. We then constructed 95% confidence intervals from the list of reductions. Covariates among the three models were always identical, and consisted of a random intercept for each subject and the two similarity measures that were not being considered as a mediator variable (i.e., if we were evaluating semantic similarity as a mediator variable, then orthographic and phonemic similarity were included as covariates).

2.6. Region of interest (ROI) analysis The FreeSurfer pipeline produces measurements from more than 100 cortical and subcortical regions of interest. In order to reduce the number of statistical comparisons, we selected a subset of these ROIs and partitioned them into subunits

D.G. Clark et al. / Neuropsychologia 54 (2014) 98–111 with a high probability of functional similarity. Measurements of gray matter volume from each subunit were summed to generate a gray matter volume measurement for a single, larger ROI. The eight large ROIs and their components were defined as follows: inferior frontal gyrus (pars opercularis, pars triangularis, and pars orbitalis), dorsal frontal cortex (rostral and caudal middle frontal gyrus, superior frontal gyrus, frontal pole, and precentral gyrus), mesial parietal (precuneus and isthmus of posterior cingulate), inferior parietal/superior temporal (superior temporal gyrus, transverse temporal region, inferior parietal lobule, and supramarginal gyrus), lower temporal (fusiform gyrus, lingual gyrus, inferior temporal gyrus, middle temporal gyrus, bank of the superior temporal sulcus, parahippocampal gyrus, and entorhinal cortex), mesial temporal (hippocampus and amygdala), occipital (pericalcarine cortex, cuneus, and lateral occipital gyrus), and subcortical nuclei (thalamus, putamen, caudate, and pallidum). The cortical ROIs are depicted in Fig. 3. Measurements for these ROIs were created for each hemisphere for each participant with MRI data (n¼ 54). We entered gray matter volumes as the dependent variable in a series of multiple linear regressions, each including a verbal fluency raw score as the independent variable of interest. Nuisance covariates included age, sex, educational level, and estimated total intracranial volume. (Intracranial volume was estimated automatically as part of the FreeSurfer pipeline.) Correction for multiple comparisons across all regressions was implemented with FDR. We performed an additional regression for each ROI, in which the corresponding ROI from the opposite hemisphere was included as a covariate. This additional regression was used as a check to insure that a significant finding in any given ROI was not due simply to a strong correlation between measurements of corresponding right and left hemisphere ROIs. We then undertook a second mediation analysis to address the question of whether the relationship between cognitive impairment and fluency raw score was mediated by gray matter volume in each ROI (Fig. 2B). Again, we undertook two versions of this analysis. In the first version, we included any diagnosis of cognitive impairment (MCI or AD) as the primary causative variable. In the second version, we entered AD as the primary causative variable. Each analysis comprised three linear regression models, as follows: (1) Flu τDx þcovariatesþ ε1 (2) GM αDxþ covariatesþ ε2 (3) Flu τ0 Dx þβGMþ covariatesþε3

Model 1 tested the hypothesis that diagnosis (Dx) had a significant influence on fluency raw score (Flu). Model 2 tested the hypothesis that diagnosis (Dx) had a significant influence on gray matter volume in a given ROI (GM). Model 3 tested the hypothesis that gray matter volume (GM) made a significant contribution to variance in fluency raw scores (Flu) when added to Model 1. In cases where all three of these hypotheses were upheld, we constructed 95% confidence intervals to test the significance of the reduction in the τ coefficient (τ–τ0 ) using 1000 bootstrap samples of the data. Covariates included age, sex, and educational level for all models.

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3. Results 3.1. Neuropsychological test results Performance of the three groups on the neuropsychological tests is summarized in Table 1. Differences identified between diagnostic groups on most of these tasks must be handled with caution, as the scores were available to the consensus panel when diagnoses were rendered. Although scores on the letter fluency tasks were numerically lower for the cognitively impaired groups, the difference did not reach statistical significance.

3.2. Lexical predictors of word adjacency in verbal fluency lists Letter fluency tasks were associated with more gradual curves on the moving average plots than most of the semantic tasks (Fig. 4). For the F task, the semantic similarity curve was obviously the steepest, but the other two letter fluency tasks appeared more ambiguous. On the A task, spelling and phonemic similarity reached similarly high moving average values, but semantic and orthographic similarity appeared to have steeper slopes. On the S task, semantic and orthographic similarity appeared similarly steep and reached comparable heights. Semantic similarity was markedly steeper for all semantic tasks in comparison to the letter fluency tasks, with the exception of the boats fluency task. Orthographic and phonemic similarity were associated with more gradual curves for all semantic tasks, and with negatively sloping curves on the water creatures task. Statistical results from the linear mixed effects models are shown in Table 3. Diagnosis of MCI or AD did not contribute significantly to variance for any model, and was removed from all models. The semantic similarity measurement was the strongest predictor of word adjacency across all tasks, with Z-statistics ranging from 5.37 to 7.45 for F, A, and S, and from 5.32 to 19.23 for the semantic fluency tasks (all corrected p-values o0.001). Orthographic similarity was significant for both the A (Z¼ 4.45, po 0.001) and S tasks (Z¼ 2.32, p ¼0.031) and phonemic similarity was significant for the S task (Z ¼3.17, p ¼0.002). Orthographic similarity was not a significant predictor of word adjacency for any

Fig. 3. Cortical regions of interest (ROIs): (1) dorsal frontal, (2) inferior frontal, (3) inferior parietal/superior temporal, (4) lower temporal, (5) occipital, and (6) mesial parietal. Subcortical and mesial temporal ROIs are not visible.

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semantic fluency task, but phonemic similarity was a significant predictor of fluency for vehicles (Z ¼2.49, p ¼0.02). Raw scores were included in the mixed effects models to control for the arithmetically unavoidable fact that words in longer lists have a lower probability of being adjacent to one another. Consistent with this expectation, all Z-statistics were negative and all p-values were less than 0.001.

3.3. Mediation of the diagnosis–word adjacency relationship by lexical similarity Semantic similarity mediated the relationships between the diagnosis of cognitive impairment and word adjacency in the three “living things” fluency tasks (Table 4). To investigate whether these effects were driven entirely by the small group of participants with AD, we undertook a second version of the mediator variable analysis in which we entered AD as the diagnosis variable. We found that the relationship between AD diagnosis and word adjacency was mediated by semantic similarity only for the animals task (bootstrapped 95% CI: 0.077–0.243).

3.4. Correlation of gray matter volumes with fluency raw scores A multiple linear regression was fit for each fluency raw score and each of the 16 ROIs. Results from the regression analyses with corrected p-values are shown in Table 5. In order to draw specific inferences regarding laterality, each regression was repeated using the gray matter measurements from the homologous ROI in the opposite hemisphere. This section will focus on those ROIs in which the FDR-corrected p-value was significant and the association retained significance (p o0.05, uncorrected) despite the Table 4 Bootstrapped 95% confidence intervals for mediation by word similarity.

Meaning similarity

Animals

Water creatures

Fruits and vegetables

0.049, 0.150

0.005, 0.111

0.040, 0.121

Numbers represent the upper and lower boundaries of the confidence interval for each task. Similarity of spelling and pronunciation did not meet the criteria for mediator variables and no confidence intervals were constructed. There was no evidence that any similarity score mediated the relationship between cognitive impairment and word adjacency for the other fluency tasks.

Fig. 4. Moving average plots of the ten fluency tasks. Each moving average was generated by first sorting the data matrix in ascending order according to one of the lexical similarity metrics (black¼ semantic similarity, red/dark gray¼ orthographic similarity, cyan/light gray ¼phonemic similarity). A vector of averages for the adjacency values (as arranged in the sorted matrix) was then generated with a moving window of 1500 samples. Plots are drawn with the rank of the similarity score on the x-axis and the proportion of words that are adjacent on the y-axis. Semantic similarity appears to be among the steepest of the three curves for all tasks, but is generally steeper for the semantic fluency tasks. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Table 3 Lexical variables influencing word adjacency in fluency lists.

Letter F Letter A Letter S Animals Water creatures Fruits and vegetables Tools Vehicles Boats Verbs

(Intercept)

Semantic similarity

Ortho-graphic similarity

Phonemic similarity

Raw score

3.82 (0.000) 4.70 (0.000) 6.39 (0.000) 10.97 (0.000) 3.93 (0.000) 8.58 (0.000) 3.85 (0.000) 7.31 (0.000) – 6.67 (0.000)

6.16 (0.000) 5.37 (0.000) 7.45 (0.000) 19.23 (0.000) 10.32 (0.000) 13.02 (0.000) 7.84 (0.000) 11.09 (0.000) 5.32 (0.000) 11.22 (0.000)

– 4.45 (0.000) 2.32 (0.031) – – – – – – –

– – 3.17 (0.002) – – – – 2.49 (0.020) – –

7.55 (0.000) 6.60 (0.000) 7.02 (0.000) 5.65 (0.000) 6.86 (0.000) 5.70 (0.000) 7.51 (0.000) 4.90 (0.000) 5.95 (0.000) 7.16 (0.000)

Table entries are Z-scores with FDR-corrected p-values from mixed-effects logistic regression models with word adjacency entered as the dependent variable and word similarity scores entered as independent variables. Fluency raw score was included in each model as a nuisance covariate.

(0.029) (0.002) (0.034) (0.034) (0.002)

ROI ¼region of interest, H2O¼water creatures fluency, IFG ¼ inferior frontal gyrus, IP/ST ¼inferior parietal/superior temporal. Each entry in the table shows the t-statistic and FDR-corrected p-value (in parentheses) from a multiple linear regression in which the dependent variable was the measured gray matter volume in the ROI and the independent variable of interest was the fluency raw score. Additional nuisance covariates included age, sex, and educational level. We repeated each regression with the gray matter measurement from the corresponding hemisphere included as an additional covariate. Relationships that remained significant at the p o 0.05 level in this additional regression are shown in bold face. The boats task and the right mesial parietal ROI are not included in the table, as neither was associated with any statistically significant findings.

– 4.213 (0.003) – 2.441 (0.044) 2.395 (0.048) 3.050 (0.020) – – 3.802 (0.006) – – 3.081 (0.019) 2.407 (0.048) – – 2.998 (0.021) 2.582 (0.036) – – 2.501 (0.041) – (0.020) (0.018) (0.039)

– 3.040 3.097 2.535 – 2.790 – 3.122 (0.018) 3.250 (0.015) 3.782 (0.006) 2.695 (0.034) – 5.286 (0.001) 2.522 (0.040) 3.460 (0.010) 3.107 (0.018) 3.513 (0.009) 2.448 (0.044) – 4.541 (0.002) – (0.034) (0.025) (0.034) (0.034)

2.670 2.905 2.629 2.689 – 4.359 2.622 – 2.536 3.796 – 2.639 4.604 – Right hemisphere IFG Lower temporal IP/ST Occipital Mesial temporal Dorsal frontal Subcortical nuclei

(0.039) (0.006)

(0.003) (0.007)

– – – – 2.476 (0.043) – –

– 3.204 (0.016) 3.413 (0.011) 3.282 (0.015) 3.003 (0.021) 3.328 (0.013) –

– 2.832 (0.027) – – – – –

(0.025) (0.034)

– 2.639 (0.034) – – – – 3.232 (0.015) – (0.041) (0.009) (0.025)

2.499 3.524 2.899 – – 2.903 2.656 – 2.464 (0.043) 2.636 (0.034) – – – 2.562 (0.037) – – – 2.678 (0.034) – – – – – – – 3.907 (0.005) 3.554 (0.009) 2.636 (0.034) 2.615 (0.034) 3.265 (0.015) 3.200 (0.016) 2.702 (0.034) – – 2.858 (0.026) – 2.840 (0.027) – 4.177 (0.003) 2.783 (0.029) Left hemisphere IFG Lower temporal IP/ST Mesial parietal Occipital Mesial temporal Dorsal frontal Subcortical nuclei

– – 2.971 (0.022) – – – 4.526 (0.002) 3.384 (0.012)

2.634 2.609 3.942 2.444 – – 4.238 3.660

(0.034) (0.034) (0.005) (0.044)

– – 3.745 (0.006) – 2.629 (0.034) – 5.034 (0.001) 3.760 (0.006)

2.926 (0.024) 2.883 (0.025) 3.147 (0.018) 2.704 (0.034) – – 2.730 (0.033) –

– – – – – – – –

Tools FAS S A F ROI

Table 5 Correlations between verbal fluency raw scores and gray matter volume in selected ROIs.

Animals

H20

Fruits and veg

Vehicles

Nouns

Verbs


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addition of gray matter volume from the corresponding ROI in the opposite hemisphere as a covariate. Letter fluency scores were associated with gray matter volumes in both hemispheres. In the left hemisphere, all letter fluency scores were associated with the volume of the subcortical nuclei. In the right hemisphere, A, S, and FAS were associated with the volume of the inferior frontal gyrus, while A and FAS were associated with the volume of the lower temporal ROI and F words were associated with the inferior parietal/superior temporal and mesial temporal ROIs. Of note, neither dorsal frontal ROI maintained a significant relationship with any task when the ROI from the opposite hemisphere was included in the regression. This finding is likely due, at least in part, to the fact that the volumes in these ROIs were highly correlated (r ¼0.92). The animals and vehicles scores and the composite score for all nouns were both associated with gray matter volume in the left inferior frontal gyrus. The fruits and vegetables score was associated with gray matter volume in the left lower temporal and subcortical ROIs. Tools and vehicles scores were associated with gray matter volumes from ROIs in both hemispheres, with both tasks correlating significantly with bilateral lower temporal ROIs. However, these correlations did not persist after controlling for gray matter in the homologous ROI. Boats scores did not show any association with any ROI and were not included in the table. Verbs were associated with gray matter volume in the right lower temporal ROI. 3.5. Mediation of the diagnosis–fluency raw score relationship by ROI In the first version of the mediator variable analysis, we investigated the tendency for gray matter volume in each ROI to mediate the relationship between cognitive impairment and raw score on each fluency task (Table 6) according to the causal model illustrated in Fig. 2B. The criteria of the causal model were met for 13 of the candidate relationships and for these relationships we quantified the significance of the mediator effect by constructing 95% confidence intervals with a bootstrap procedure. These procedures revealed that 11/13 of the mediator relationships were significant. We found that the relationship between cognitive impairment and fruits and vegetables fluency was mediated by gray matter volume in dorsal frontal and mesial temporal regions of the left hemisphere and by mesial and inferior temporal and occipital regions in the right hemisphere. The lower temporal region of the right hemisphere mediated the relationship between cognitive impairment and fluency raw score on animals, fruits and vegetables, tools, nouns, and verbs. We repeated this analysis with the diagnosis of AD as the primary causal variable, to ascertain whether these effects were driven entirely by the AD group (Table 7). Although we identified 6 relationships that met the criteria of the causal model, only two of these relationships proved to be significant based on the bootstrapped confidence intervals. The left lower temporal ROI mediated the relationship between AD and fruits and vegetables score. The right lower temporal ROI mediated the relationship between AD and the summed fluency raw scores for noun categories. The findings from the previous analysis using cognitive impairment as the primary causal variable do not appear to be driven entirely by the AD group.

4. Discussion The goals of this research were to examine lexical factors influencing performance on ten different verbal fluency tasks and to evaluate the relationships between task performance and

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Table 6 Mediation between cognitive impairment and fluency raw score by gray matter volume – all cognitive impairment. Animals


Tools

Nouns

Verbs

Left hemisphere Dorsal frontal Occipital Mesial temporal

2.734, 0.050 – –

2.797, 0.013 2.343, 0.134 3.034, 0.159

– – –

– – –

2.809, 0.117 – –

Right hemisphere Lower temporal Occipital Mesial temporal

3.502, 0.138 – –

3.461, 0.191 2.680, 0.070 2.800, 0.068

2.553, 0.063 – –

12.847, 0.344 – 8.293, 0.271

3.797, 0.376 – –

Numbers are lower and upper bounds of the bootstrapped 95% confidence interval. Those confidence intervals that do not include 0 are in bold font. ROIs and fluency tasks that are not shown were excluded due to failure to meet the statistical criteria for the causative model (i.e., there was no significant relationship between diagnosis and raw score in Model 1, or between diagnosis and gray matter volume in Model 2, or between gray matter volume and raw score in Model 3).

Table 7 Mediation between cognitive impairment and fluency raw score by gray matter volume – AD only.

Left hemisphere Lower temporal Inferior parietal/superior temporal Right hemisphere Lower temporal

Animals


Nouns

– 3.226, 0.039

4.218, 0.029 3.230, 0.014

14.041, 0.182 10.296, 0.259

–

–

15.197, 0.742

Numbers represent lower and upper bounds for bootstrapped 95% confidence intervals of mediation effects for each ROI. Those that do not contain 0 are in bold font.

gray matter volumes in research participants on a continuum between normal aging and mild AD. For the first goal, we developed novel methods inspired by techniques in natural language processing and information retrieval to examine lexical factors related to adjacency of words in verbal fluency lists. We then evaluated a causal model in which word similarity measures were considered as potential mediators of the relationship between diagnosis (cognitive impairment or AD) and word adjacency. For the second goal, we performed separate multiple linear regression analyses on gray matter volumes with verbal fluency raw scores as the key predictor variable. We evaluated a second causal model, in which gray matter volume in each ROI was considered as a potential mediator of the relationship between diagnosis and fluency raw score.

4.1. Group comparisons Only limited conclusions may be drawn from the group comparisons, as some of the verbal fluency scores (FAS composite score, animals, and a composite score with all noun categories and verbs) and all of the remaining neuropsychological test scores were available to the consensus team at the time that participants were categorized. As in other studies, the difference between the memory impaired groups and controls on letter fluency tests did not reach statistical significance (Adlam, Patterson, Bozeat, & Hodges, 2010; Henry, Crawford, & Phillips, 2004). Cognitively normal controls performed significantly better than AD patients on all of the semantic tasks except for vehicles, and significantly better than MCI patients on tools, verbs, and the nouns composite score. These findings give credence to the idea that patients with AD and MCI suffer from changes in semantic memory. Semantic fluency tasks remain a useful tool for cognitive assessment, particularly for the detection of degenerative diseases that may be associated with semantic impairment, such as AD and semantic

dementia (Hodges & Patterson, 1995; Hodges, Patterson, Oxbury, & Funnell, 1992; Libon et al., 2009; Rogers & Friedman, 2008). Fluency for verbs has not been studied as extensively as the other two major categories of fluency tasks (Woods et al., 2005). Our control subjects perform on par with other elderly controls (Piatt, Fields, Paolo, & Troester, 2004), yet both MCI and AD patients perform significantly worse than controls, suggesting that this task could have utility for detecting early AD. This finding is similar to findings by other groups both in MCI/AD and in Parkinson disease patients with and without dementia (Ostberg, Fernaeus, Hellstroem, Bogdanovic, & Wahlund, 2005; Piatt, Fields, Paolo, Koller & Troester, 1999). 4.2. Analysis of lexical similarity of words The moving average plots generated for the ten fluency tasks depict the patterns that were further elucidated in the linear mixed-effects models. Specifically, while semantic similarity generally showed the steepest incline for all tasks (with the possible exception of the A task), the slope was more gradual for the letter fluency tasks and the boats task. The key findings from the linear mixed-effects logistic regression analysis of verbal fluency word lists are as follows. First, measurements of semantic similarity exert the strongest and most consistent influence on word adjacency in all verbal fluency tasks, including letter fluency tasks and fluency for verbs. Second, a measure of orthographic similarity is a significant predictor of word adjacency for two of the letter fluency tasks (A and S). Third, phonemic similarity is a significant predictor of word adjacency only for the letter S and vehicles tasks. With regard to the causal model, we find that only semantic similarity appears to mediate the relationship between cognitive impairment and word adjacency, and we find evidence for this effect only with fluency for biological categories. When AD is used as the primary causative variable instead of cognitive impairment in general, we find


evidence of mediation only by semantic similarity on the animals task, indicating that not all of the effects are driven by including a group of participants with dementia. Language imparts a survival advantage to our species because it allows us to share ideas with one another, whether contemporaneously or across generations. Given that this advantage stems from the ideas that are shared and not the medium by which they are shared, it would be surprising if the brain were organized according to features of arbitrary symbols rather than the features of real-world referents. One might not expect that the dominance of semantic associations would persist even in the setting of a letter fluency task, but our results suggest that it does. Other investigators observe evidence that semantic associations are stronger than other lexical associations in implicit word association tasks (Frost, Deutsch, Gilboa, Tannenbaum, & Marslen-Wilson, 2000; Radeau, Besson, Fonteneau, & Castro, 1998). Previous studies of structure in verbal fluency word lists have employed multi-dimensional scaling, cluster analysis, and quantification of clustering and switching (Chan et al., 1993; Troyer, 2000; Troyer & Moscovitch, 2006; Troyer et al., 1998). Clustering and switching appear to have utility for the study of MCI and AD (Murphy, Rich, & Troyer, 2006), as MCI patients exhibit clustering scores between those of controls and AD patients on semantic fluency tasks and produce more switches than AD patients on letter fluency tasks. Clustering and switching scores both correlate positively with raw scores in cognitively normal individuals, suggesting that participants who perform the best do so by optimizing their use of clustering and switching strategies. Studies of clustering and switching typically use only phonological and orthographic similarity criteria for evaluating letter fluency tasks. For example, two adjacent words in a letter fluency list would be considered linked if they rhymed (including homonyms) or began with the same two letters. However, there is some evidence that measuring clustering and switching with both semantic and phonological/orthographic criteria may have value for interpreting both types of verbal fluency tasks (Abwender et al., 2001; Ho, Sahakian, Robbins, Barker, Rosser, & Hodges, 2002). Our findings suggest that semantic memory contributes to variance in letter fluency word list structure, but we do not currently find evidence that the diagnosis of MCI or AD plays a significant causative role in these effects. The absence of such detectable changes might be the reason that letter fluency tasks appear to provide a valuable contrast to semantic fluency tasks for dementia diagnosis (DuffCanning et al., 2004; Monsch et al., 1994). Based on the analysis of mediator variables in the causal model we specified, we find that semantic similarity mediates the relationship between cognitive impairment (MCI or AD) and word adjacency within lists, but only for the semantic fluency tasks that make use of biological category cues. Stated somewhat more concretely, there is a positive relationship between cognitive impairment and word adjacency (indicating that lists are shorter among the cognitively impaired subjects), but this relationship is reliably attenuated when semantic similarity (which is also positively associated with word adjacency) is included in the model. In addition, we find that the relationship between cognitive impairment and semantic similarity within word lists is always positive, indicating that our cognitively impaired subjects exhibit an exaggerated tendency to list semantically related living things consecutively. Altogether, these findings suggest that patients with MCI/AD are influenced by the same kinds of word relationships that influence cognitively normal subjects, but that (at least on the tasks involving biological categories) they select items within a restricted semantic range. These findings could result from degradation of semantic stores (Chan et al., 1993) or from limitation of processes for searching semantic stores (Storms, Dirikx, Saerens, Verstraeten, & De Deyn, 2003).

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4.3. Correlations between verbal fluency raw scores and gray matter within ROIs 4.3.1. Letter fluency Evidence from the ROI analysis indicates that variation in performance on letter fluency in the setting of AD and MCI is driven by atrophy in portions of both hemispheres, including the dorsal frontal regions (possibly bilaterally), left subcortical nuclei, and the right inferior frontal gyrus, lower temporal region, and inferior parietal/superior temporal region. The bilateral distribution of this network is supported by the finding that controlling for gray matter volume in the opposite hemisphere sometimes indicates left hemisphere dominance (for the subcortical nuclei) and other times indicates right hemisphere dominance (for the inferior frontal, temporal, and inferior parietal regions). The dorsal frontal regions are so highly correlated that it is impossible to say for certain whether either one is dominant for letter fluency in these subjects, but the laterality of the subcortical influence suggests that the left prefrontal cortex is a stronger factor than the right. We hypothesized that letter fluency would correlate strongly with the left inferior frontal gyrus and inferior parietal/ superior temporal region due to the importance of these areas for representing sublexical information (such as phonemes or letters). Although there was a significant correlation with both inferior parietal/superior temporal regions, when the gray matter volume measurements from the homologous contralateral region were added to the regression, only the right hemisphere region remained significant at the p o0.05 level, and only for the F task. Similarly, only the right (not the left) inferior frontal gyrus was significantly correlated with letter fluency tasks, and remained so when the left inferior frontal ROI was added to the regression, at least for the A, S, and FAS composite scores. We did not find evidence for mediation of the relationship between diagnosis and fluency raw score by any ROI. As mentioned in the analysis of lexical factors (Section 4.2), semantic similarity influences performance on letter fluency. As such, degradation of non-verbal conceptual information, which may be supported by regions of the right hemisphere (Damasio et al., 2004), may lead to reduction of the raw score. Along the same lines, items produced during letter fluency tasks are not restricted to concrete entities the way most semantic fluency tasks are, and may include verbs, prepositions, modifiers, or abstract nouns. Among these possibilities, at least the concepts these words refer to may have right hemisphere representations. Evidence from fMRI and PET suggests that motion, arguably an important component of action semantics, has a bihemispheric, posterior ventral temporal representation (Damasio et al., 2001; Kable & Chatterjee, 2006) and that specific regions of right temporal cortex are associated with biological and human-agent motion (Han et al., 2013). Similarly, spatial relationships, when dissected away from recognizable concrete entities that may be spatially arranged, are associated with right parietal and bilateral posterior mesial temporal activations (Damasio et al., 2001). Abstract noun concepts are associated with increased activation of the right prefrontal and posterolateral temporal regions (Grossman et al., 2002), the right superior temporal gyrus (Kiehl et al., 1999), and the right superior frontal gyrus (D0 Esposito et al., 1997) in cognitively normal subjects undergoing functional MRI. An H215O-positron emission tomography (PET) study suggests increased blood flow in bilateral regions in response to abstract nouns, including the left inferior frontal and superior temporal gyri and the right inferior frontal gyrus, temporal pole, parietooccipital junction, anterior cingulate gyrus, and amygdala (Perani et al., 1999). Our finding of bihemispheric associations with letter fluency task performance is supported by other imaging studies of verbal

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fluency, which have shown bilateral dorsolateral frontal activation (Abrahams et al., 2003). When compared directly with category fluency, letter fluency performance leads to activation of the bilateral fusiform gyri and the right superior parietal lobule (Birn et al., 2010) or bilateral dorsolateral frontal activation (Mummery et al., 1996). A voxel-based morphometry study of patients with three different fronto-temporal dementia (FTD) syndromes reveals correlation of the right inferior frontal region with letter fluency performance among the behavioral variant patients (Libon et al., 2009), along with several more prominent left hemisphere foci. Cortical mapping in a large sample of controls, AD patients, and MCI patients reveals correlations of letter fluency with gray matter thickness in the right middle temporal gyrus and left superior and middle temporal gyri, middle frontal gyrus and mesial frontal region, inferior parietal lobule, precentral and postcentral gyri, and fusiform gyrus (Ahn et al., 2011). Our findings, therefore, point to the utility of letter fluency tasks for assessing not only systems supporting executive function (dorsal frontal regions and subcortical nuclei), but also regions that most likely support concepts outside the set of concrete entities. Despite the fact that letter fluency raw scores do not significantly differ among the patient groups evaluated here, these tasks appear to provide an important contrast to other fluency tasks. In addition, there may be methods for extracting useful diagnostic information from the actual words listed by patients using methods described for semantic fluency (Clark et al., in press).

4.3.2. Noun fluencies Raw scores on the six tasks in which the cue is a category of concrete nouns are variably associated with regions in both hemispheres, with the lower temporal ROIs showing particular consistency across tasks bilaterally. Boats are not significantly correlated with any ROI after correction for multiple comparisons, and tools are not associated with any correlation that maintains significance when gray matter from the opposite hemisphere is included in the regression. Two of the categories of living things (animals and fruits and vegetables) show evidence of left hemisphere lateralization, albeit in different ROIs. For animals, the left lateralization is in the inferior frontal region. Fruits and vegetables scores lateralize more strongly to the left lower temporal and subcortical ROIs. Despite the finding that the search for laterality yielded only left-lateralized ROIs across the nouns tasks, the frequent correlation of these raw scores with right hemisphere ROIs and the emergence of three right hemisphere ROIs in the causal model analysis suggest some degree of bilaterality to the patterns of atrophy that lead to poor performance on these tasks in our participant sample. Others have examined the relationship between brain lesions and knowledge and naming of concrete entities, most notably Damasio et al. (2004). For animals, these investigators report predominantly right hemisphere associations for lesions affecting recognition (mesial temporal and posterior inferior temporal, but bilateral mesial occipital regions) and left hemisphere associations for lesions affecting naming (inferior precentral gyrus, lateral occipito-temporal region and anterior insula). Recognition of fruits and vegetables is associated with lesions in the left temporal pole and inferior frontal operculum, right angular gyrus and a region extending from the right lateral inferior temporal gyrus into temporal pole, while naming of fruits and vegetables is associated with lesions of the left inferior precentral and postcentral gyri and anterior insula. Lesions impacting tool recognition and naming are both found to be restricted to the left hemisphere, with both skills diminished by lesions in the left temporo-occipito-parietal region, and naming also associated with lesions involving the inferior precentral and

postcentral gyri and insula. An fMRI study in which controls and AD patients performed a “pleasantness” judgment on words denoting either animals or implements revealed left ventral temporal activation for animals and right medial frontal and caudate activation for implements (Grossman et al., 2003). The findings from these studies suggest that conceptualization and naming of concrete entities is bihemispheric, with significant contributions from both temporal lobes. These findings are consistent with the findings from our ROI analysis. Several studies have evaluated semantic fluency performance, either with structural imaging in diseased populations or with functional imaging. H215O-PET in healthy controls reveals bilateral mesial temporal and right parietal activation for living things tasks and left posterior temporal activation for manmade objects (Mummery et al., 1996). When all categories are contrasted with letter fluency tasks, categories are found to produce relatively greater activation in both temporal lobes, although the right-sided cluster is quite small. Two studies have correlated category fluency raw scores with gray matter thickness. Ahn et al. (2011) report findings with animals and things one finds in a supermarket. Animal fluency is associated with gray matter thickness in the right superior and middle temporal gyri, medial temporal region, middle frontal gyrus, orbitofrontal gyrus, insula, and fusiform, and the left superior and middle temporal gyri, inferior frontal gyrus, medial frontal region, orbitofrontal region, inferior parietal lobule, and fusiform. Regions associated with supermarket fluency are more widespread, including most of the same areas associated with animal fluency, but also the right inferior frontal gyrus, superior parietal lobule, lingual gyrus, and bilateral cuneus, precuneus, and posterior cingulate gyrus. The second recent study of this nature evaluated animal and vegetable fluency among a sample of patients scanned through the Alzheimer0 s Disease Neuroimaging Initiative (Eastman et al., in press). These authors report that both tasks are associated with gray matter thickness in the left posterior temporal, parietal, cingulate, and prefrontal cortices, and the right posterior temporal, temporo-occipital, parietal, and prefrontal cortices, with more widespread regions of correlation for the vegetables task. The authors raise the possibility that if the category of vegetables is smaller than the category of animals, the greater effort necessary to generate exemplars may lead to stronger correlations with disease state. We find the most widespread cerebral correlations with a similar task (fruits and vegetables). However, we find that subset categories (boats and water creatures) are both correlated with fewer ROIs than their corresponding supersets (vehicles and animals). This suggests instead that categories with more exemplars yield stronger or more widespread correlations in the cerebrum. These observations may be related to differences in lexical frequency, familiarity, or category size. Some investigators report that semantic fluency tasks that use larger categories contain more disease-associated variance, and are more sensitive for detecting AD (Diaz et al., 2004).

4.3.3. Verbal fluency for verbs We find correlations for verb fluency with left dorsal frontal and lower temporal ROIs, and with right lower temporal, mesial temporal, occipital, and inferior parietal/superior temporal ROIs. Among these areas, the right lower temporal ROI remains significant in a regression model that includes gray matter values from the left lower temporal ROI, suggesting that the finding on the right is not due merely to symmetry of atrophy (although the finding on the left might be). Gray matter in the left dorsal frontal and right lower temporal ROIs mediates the relationship between diagnosis and fluency for verbs. Several imaging studies have compared cortical activations induced by nouns and verbs. Perani et al. (1999) report that verbs


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are associated with predominantly left hemisphere increases in blood flow in H215O-PET, specifically in the middle and inferior frontal gyri, superior parietal lobule, superior and middle temporal gyri, and the lingual and inferior occipital gyri. They report rightsided activation only in the lentiform nucleus (Perani et al., 1999). A more recent functional MRI experiment identifies activations in both hemispheres in response to verbs (compared to nouns): specifically, the bilateral superior and middle temporal gyri, the left parsopercularis and insula, and the right calcarine cortex (Yu, Law, Han, Zhu, & Bi, 2011). These authors provide an extensive review of the literature on the topic and indicate that other groups report associations of verbs with the left inferior frontal gyrus, inferior temporal gyrus, and inferior parietal gyrus, but also the right middle and superior temporal gyri. These studies, however, usually employ lexical stimuli. As noted above (Section 4.3.1), performance on verbal fluency tasks may be influenced by disease that affects brain regions supporting nonverbal conceptualization. For verbs, these areas could include those that encode biological or non-biological motion processing (Damasio et al., 2001; Kable & Chatterjee, 2006; Han et al., 2013). We know of only one study seeking cerebral correlates with fluency for verbs. The authors report an association of reduced fluency for verbs among AD patients with reduced temporal lobe signal on single photon emission computed tomography (SPECT) (Ostberg et al., 2007).

concrete, consider the words jeep and ship, two items that were occasionally provided on the vehicles task. The phonemic similarity metric we actually used compared these words in terms of their CMU dictionary phoneme sequences [‘JH’, ‘IY1’, ‘P’] and [‘SH’, ‘IH1’, ‘P’]. Since both words have three phonemes and only one of the phonemes was shared by both words, the overlap was calculated to be 0.33. The alternative metric that was originally considered took into account the fricative qualities of ‘JH’ and ‘SH’ and the high placement of the mandible during articulation of the ‘IY’ and ‘IH’ sounds, as well as other qualities common to vowels. This method yielded a phonemic overlap of 0.91. The more discrete metric provided a better fit to the data, but perhaps methods that quantify phonetic similarity even more precisely would outperform the discrete method. (One possibility for such a method would be the maximum correlation of the actual recorded voice waveforms or spectrograms across all possible lags.) The third limitation that should be considered is the fact that these findings are not immediately applicable to diagnoses or prognostications for patients suspected of having AD. Nevertheless, we hope that a deeper understanding of verbal fluency tasks and how they relate to cognition and neuroanatomy in the setting of MCI and AD will influence such work and ultimately impact the conduct of clinical trials.

4.4. Limitations

We conclude that several presuppositions regarding performance on fluency tasks and the neural basis for these tasks are upheld by our findings, in particular the importance of semantic memory for semantic fluency tasks, the contribution of brain regions known to support semantic memory, particularly the temporal lobes, and the relatively greater influence of dorsal frontal regions and left hemisphere subcortical nuclei to the performance of letter fluency tasks. Analysis of our first causal model supports the view that semantic fluency scores are impacted by cognitive impairment on the MCI-AD continuum and that changes in semantic memory or access to semantic memory are the reason for this finding, at least for biological categories. A few presuppositions are challenged by our findings. First, semantic memory appears to be the dominant predictor of word adjacency even on letter fluency lists. Second, word spellings appear to be a stronger predictor than word pronunciations for letter fluency tasks. Third, there is a significant contribution of right hemisphere structures to the performance of fluency tasks for initial letters and verbs, and these findings persist even when gray matter volumes from the homologous left hemisphere regions are incorporated into the model. Evaluation of our second causal model indicates that the relationship between cognitive impairment and fruits and vegetables fluency is mediated by a large, bihemispheric network, and that the right lower temporal region most consistently mediates the relationships between cognitive impairment and fluency scores. Right hemisphere regions might be engaged to a greater extent when a task requires access to abstract or action semantics.

A few limitations of this analysis should be considered, as they indicate goals for future work. First, the localization of the fluency tasks to cerebral regions was somewhat coarse. We adopted the large ROI approach to reduce the number of statistical comparisons and to focus on brain regions hypothesized to be important for language, semantic memory, and executive function. However, it would be interesting to refine the localizations described here by including a larger number of subjects or a more sensitive method for analyzing the cerebrum (Apostolova et al., 2008; Thompson et al., 2003). Second, we can offer only limited evidence that the methods employed for measuring orthographic and phonemic similarity yielded the best quality measurements. That is, semantic memory might have emerged as such a strong predictor of word adjacency simply because the measure of semantic similarity performed better at its job than the other two measures. On the other hand, we do have some reasons to feel confident about our choices of metrics. Word spellings are publically shared arrangements of discrete symbols rather than nebulous unconscious processes, and it is not difficult to make computer programs to compare them. In fact, spellchecking programs typically make use of the Levenshtein edit distance and this method works very well in practice. Early work on this project used a proximity metric derived from the edit distance, but the fit of the models was not quite as good as the fit when using the string overlap metric to estimate orthographic similarity. A similar argument can be made for our approach to measuring phonemic similarity. Speech sound sequences differ from spellings in that even literate individuals have limited conscious awareness of the exact sounds they produce when speaking. The study of phonology is largely the study of unconscious processes leading to phonological alternations when words or morphemes are combined (Kenstowicz & Kisseberth, 1979). The overlap metric we employed for these analyses assumed that phonemes are discrete entities in the mind, rather than being more or less connected according to the similarity of their phonetic features. Experiments demonstrating the presence of a perceptual magnet effect in phonemic perception support the approach we took (Kuhl, 1991). Earlier analyses for this project employed an alternate metric that took into account the similarity between phonemes. To make this distinction

4.5. Conclusions

Funding sources VA (E6553W).

Acknowledgments The research reported here was supported by the Department of Veterans Affairs, Veterans Health Administration, Rehabilitation

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Research and Development Service (E6553W, David Clark, PI). Mr. Greg Hammond and Dr. Glenn L. Clark provided with manuscript proofreading.

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Verbal fluency in cerebral small vessel disease and Alzheimer's disease.

A normative study of lexical verbal fluency in an educationally-diverse elderly population.

Lexical-Semantic Search Under Different Covert Verbal Fluency Tasks: An fMRI Study.

Verbal fluency and positron emission tomographic mapping of regional cerebral glucose metabolism.

Interacting effects of age and time of day on verbal fluency performance and intraindividual variability.

Verbal fluency and intrinsic brain activity in Alzheimer's disease.

Effects of extraversion, anxiety, and sex on children's verbal fluency and coding task performance.

Embedded measures of performance validity using verbal fluency tests in a clinical sample.

Picture naming and verbal fluency in children with cochlear implants.

Initial phase performance in a 30-s verbal fluency task as being reflective of aging effect.

Age-related changes in functional network connectivity associated with high levels of verbal fluency performance.

Verbal fluency: a NART-based equation for the estimation of premorbid performance.

Trait Anxiety Modulates Brain Activity during Performance of Verbal Fluency Tasks.

Verbal fluency patterns in mild cognitive impairment and Alzheimer's disease.

Hemodynamic response in a geographical word naming verbal fluency test.

The cognitive abilities associated with verbal fluency task performance differ across fluency variants and age groups in healthy young and old adults.

Factors influencing dressing performance after stroke.

Verbal comprehension ability in aphasia: demographic and lexical knowledge effects.

Phonemic verbal fluency task in adults with high-level literacy.

Verbal fluency in breast cancer patients treated with chemotherapy.

Factors influencing risky decision-making in patients with cerebral infarction.

Performance of Verbal Fluency as an Endophenotype in Patients with Familial versus Sporadic Schizophrenia and Their Parents.

A new standardization of semantic verbal fluency test.

Daily crosswords improve verbal fluency: a brief intervention study.