BRAIN
AND
LANGUAGE
4, I- 10 (1977)
Numerical Taxonomy of Aphasia A. KERTESZ AND J. B. PHIPPS The University of Western Ontario Two hundred consecutively seen aphasics, 142 of them with infarcts, were examined by tests of fluency, comprehension, repetition, naming, and information content. The language scores were subjected to a minimum variance clustering algorithm separately for the total and for the infarct groups. The latter generated IO clusters on a dendrogram. Attribute analysis of each cluster provided a clinically meaningful profile of language performance for these groups. The degree of correlation of most computer generated clusters with clinically recognized groups was high, and the homogeneity of some of the clusters is striking. An exception which is bimodally distributed. One of these appears to be “conduction aphasia,” clusters, with high fluency and low comprehension scores, was renamed “afferent conduction” aphasia, and the other, with lower fluency and higher comprehension, was renamed “efferent conduction” aphasia. The Principal Components Analysis was used to evaluate the discriminatory value of language characteristics, and the Nearest Neighbor Network Analysis was used to evaluate the significance of clustering. The dendrogram for all aphasics showed a less specific and less homogenous six clusters.
Aphasics have been variously classified ever since clinicians became interested in the phenomenon of acquired language impairment. Even though the literature contains many often seemingly discordant classifications, certain frequently occurring, and certain rarer, classes of aphasia are agreed upon by most clinicians. The majority of authors in this field also accept the need for accurate classification as a basis for research and treatment. In recent years, attempts have been made to define aphasic types according to performance on certain tests (e.g., Vignolo, 1964), but most often clinicians are satisfied with labeling aphasics on the basis of impressions (unmeasured performance), or upon one, or at the most two, parameters such as the fluency-nonfluency scale or the severity scale. Apart from the imprecision of impressionistic approaches, they must inevitably run the risk of bias due to any taxonomic preconceptions of aphasia held by the diagnosing clinician. Thus the utilization of specified performance tests must increase objectivity. From the language behavior of aphasics one can test relatively pure aspects of structured performance or more complex, but less abstract, features. Fluency, comprehension, repetition, naming, and information We would like to thank Patricia McCabe and Robert Kormos for their assistance. reprint requests to Dr. A. Kertesz, Department of Clinical Neurological Sciences, University of Western Ontario, St. Joseph’s Hospital, London, Ontario, Canada. Copyright 411 rights
0 1977 by Academic Press. Inc. of reproductmn nn any form reserved
I
Send The
2
KERTESZ AND PHIPPS
FIG. I. A dendogram of minimum variance clustering of 142 patients with infarcts.
content of speech have thus become standard items in most aphasia tests by virtue of their importance in describing language impairment. An intuitive taxonomi,c method of classifying aphasias based on these parameters has been published previously (Kertesz & Poole, 1974), but in the present study an objective numerical method is used, free from the historical bias of previous classifications. Numerical taxonomy is a mathematical approach to the classification of a set of measurable characteristics. It can provide more objective, precise, and repeatable conclusions than intuitive classifications, and has been used extensively in botany, zoology, economics, medicine, etc. (Sneath & Sokal, 1973). The aims of our study are to define an optimal set of aphasiac clusters, to generate profile characteristics describing these clusters, and to determine the significance of clustering of the data. Numerical taxonomy covers a wide range of approaches to, and techniques for, the analysis of data structure. Included are actual taxonomic methods (cluster analyses), trend seeking analyses (principally ordinations), and various methods of attribute analysis of the perceived classes. METHODS Two hundred consecutively examined aphasics formed the data base, of which 142 were cerebral infarcts and were separately analyzed, and 18 were tumour cases, 16 trauma, 12 hemorrhage, 5 degenerative, 5 aneurysm, and 2 miscellaneous. This representative sample was obtained from three general hospitals and one veteran’s hospital. The patients had an average age of60.6, with aSD of 1.Ol and a range from 19-87. The sex ratio, M:F = 129:71, is
TAXONOMY
3
OF APHASIA
TABLE
1
CRITERIA FORCLASSIFICATION” Fluency Global Broca’s Isolation Transcortical motor Wemicke’s Transcortical sensory Conduction Anemic
o-4 o-4
O-S o-5 5-10 5-10 S-10 5-10
Comprehension
Repetition
Naming
o-3.9 4-10 o-4
o-4.9 o-7.9
O-6 O-8 O-6 O-8 o-7 o-9 o-9 o-9
S-IO O-6.9 O-6.9 7-10
7-10
5-10 8-10 o-7.9 8-10 O-6.9 7-10
n Numbers are raw scores of set totals on the WAB. strongly skewed towards males due to the veteran population in the study. The average educational level was grade 9.9, with a SD of 1.0 and a range of 0- 18. All patients were tested on the 17tasks of the Western Aphasia Battery (WAB) (Kertesz & Poole, 1974). a modification of the Boston Aphasia Test (Goodglass & Kaplan, 1972). We selected for this study only the spoken language tasks: (I) fluency, judged by a carefully constructed scale of I- 10; (2) comprehension, measured by (a) yes-no questions, (b) auditory discrimination or pointing, and (c) auditory sequencing with relational words; (3) repetition of words, numbers, and high and low probability sentences; (4) naming-(a) visual confrontation naming of objects, (b) word fluency, (c) sentence completion, and (d) responsive speech; and (5) information content of replies to standardized conversational questions. We term, for the purposes of this paper, the data for the 10 individual tests as “subscores” and the pooled results for each of the five main areas (e.g., fluency, comprehension, etc.) as “set totals.” A minimum variance clustering algorithm called the sum of squares agglomeration (Orloci, 1967a)on the euclidean distance matrix of dissimilarities was used. This is a “tight,” or high center of gravity, clustering technique that is especially valuable for beginning explorations of taxonomic structure. Like all clustering algorithms, it imposes structure on the group, and extrinsic tests of the significance of the groups formed are required. The clustering results are displayed in dendrograms. The groups recognized were subjected to a cluster attribute analysis which produced character profiles for each group. A further analysis, of group intercentroid distances, was conducted to obtain information on the validity of the groups so far recognized. Ordination was carried out by the Principal Components Analysis (PCA) (Cooley & Lohnes, 1962) of the correlation matrix. Both q- and r-strategies were employed (Orloci, l%7b), the former being used to depict the patients in the most efficient 2-space, while the latter provided information about the contribution of the different attributes to the spatial arrangement of the entities (patients).
RESULTS
The dendrogram of 142 infarcts (language area set totals) neatly generates 10 groups at the 2.5% level of total variance as shown in Fig. 1. The proportion of various clinical types as defined by previously published clinical taxonomic criteria (Table 1) was determined for each cluster (Table 2).
KERTESZ AND PHIPPS TABLE 2 CLUSTER COMPOSITION
Number of patients
Percentage of clinical aphasic types
I II III
30 15 12
IV
13
V VI VII VIII IX X
4 12 11 7 18 20
97% Global 86% Broca’s 25% Global 25% Broca’s 25% Isolation 54% Broca’s 23% Isolation 23% Transcortical motor 100% Transcortical sensory 58% Conduction 100% Wernicke’s 57% Conduction 63% Anemic 100% Anemic
Cluster
When subjected to attribute analysis, the average values and standard deviations for each of the 10 recognized clusters provided a meaningful profile for each group (Fig. 2). The first cluster showed low values on all scores (this group can be called global aphasia). Cluster two was very similar except for a much higher rating for character 2 (comprehension) and corresponds to Broca’s aphasia. The third group had moderately high repetition but low fluency (nonfluent echolalic sensory aphasia or “isolation syndrome”). Cluster four had higher scores with low fluency and high repetition and comprehension (transcortical motor aphasia). The small fifth cluster scored well on characters 3 and 1 (repetition and fluency) and moderately low on the others (transcortical sensory aphasia). The sixth cluster had low repetition, naming, and comprehension but high fluency (we called this “afferent conduction aphasia”). The seventh cluster had lowish scores, not unlike clusters I and II, but it differs in having a high value for fluency (this group corresponds to Wernicke’s aphasia). Cluster eight had scores slightly higher than the median but differed from group VI in having a high value for character 4 (naming) and a relatively low score for fluency (we took the liberty of calling this “efferent conduction aphasia”). Clusters nine and ten represent, respectively, a poorer and a better (near normal) performing set of high scores (anemic aphasia). The relatively low standard deviations for each attribute in each cluster support the significance of the groups detected. It proved interesting to subject the 10 clusters to a Nearest Neighbor Network Analysis in which intercentroid distances were computed and deviations along the intercentroid axes were established (Fig. 3). This is
TAXONOMY
”
&W
10 I
OF APHASIA
FLUENCY COMPREHENSION ;?&ikTlON INFORMATION
IX
“I
FIG. 2. Average each cluster.
values and standard deviations
of cluster attributes
x
(language scores) of
essentially an extension of the method presented by Sokal and Sneath (1973, p. 286). There are four pairs of mutually closest clusters (I, II), (IV, V), (VI, VII), and (IX, X). If the summed deviations on the intercentroid axes are more than the intercentroid distance, then the clusters are not significantly different statistically. The only example of this situation is cluster pair (I-II), and inspection of the cluster attribute analysis for this group confirms the similarity of these two clusters as well as the attribute which makes them distinct clinically: comprehension. Cluster pairs (IV-V), (VI-VII), and (I-III) are also quite close, but each member of the pair nevertheless constitutes a distinct cluster. It is noteworthy that when second-nearest neighbors have to be utilized to complete the network, e.g., by forming the bond (IV-VIII), the dispersions do not remotely approach the intercentroid distances. Thus, we may conclude that there are at least nine statistically distinct groups present. Theq-type PCA is essential in interpreting the general trends of variation observed (Fig. 4). The main scatter ofgroups is along the first factor which, significantly, accounts for 82% of the total variation. This underlines the fairly strong degree of correlation among all the attributes (tests) measured. Some groups however, e.g., (III, VII) and (IV, VI), are separated principally on the second factor, which accounts for about 9% of the total variation, and our results from the cluster profile analysis indicate in which characteristics they differ. The r-type PCA ranks the characteristics according to their discriminatory value on the q-type PCA factors (Table 3). It illustrates a fairly even significance (i.e., high correlation) of characters on the first root (this is why axis 1 accounts for so high a
KERTESZ AND PHIPPS
361
--------.
NEICHBOURS NEAREST 2nd NEAREST NEIGHBOURS
FIG. 3. The nearest-neighbor pairs are indicated by solid lines and the second-nearest connections by interrupted lines. The arrows indicate the direction and extent of dispersion towards the centroid of the neighboring clusters. The diagram illustrates all the nearest neighbors and only those second-nearest neighbors necessary to complete a network among the 10 clusters. Numbers represent units of taxonomic distance.
percentage of the variation), but on root 2, characteristics 1 (fluency) and 2 (comprehension) have by far the most differentiating power, on root 3, repetition. The dendrogram of all of the 200 aphasics’ language totals depicts a somewhat different group structure from that for the infarcts alone. There are six larger, less homogeneous clusters (Fig. 5). In the first, 67% of 41 patients in the cluster are global; 72% of 38 globals of the total sample belong to this cluster. In the second (N = 31), 5% are Broca’s; 56% of 32 Broca’s are in this cluster. In the third (N = 33), 37% are Wernicke’s, and 36% of 37 Wernicke’s are there. In the fourth (N = 32), 44% are “conduction”; 58% of24 conduction aphasias are in this cluster. In the fifth (N = 18), 50% are transcortical sensory; 64% of the 14 transcortical sensory aphasics belong in this cluster. In the sixth (N = 45), 80% were anomies; 90% of the 40 anemic aphasics are in this cluster. The least homogeneous, the third group, contained 100% of the isolation, 37% of Wernicke’s, and 35% of the transcortical sensory aphasics-a low scoring receptive group, if you like. The fourth group, although predominantly
TAXONOMY
7
OF APHASIA
FIG. 4. Scatter diagram of 142 infarcts illustrated in the first 2 factors of the component space. The 10dendrogram-generated clusters are superimposed and the clinical classification of patients is indicated by various symbols.
conduction patients, had a fair share of Broca’s aphasics, indicating the validity of the “efferent motor aphasia” as a cluster. DISCUSSION The reason for the lower specificity of clustering in the larger group of aphasics is partly due to the nature of the lesions. In the larger group, in addition to the 142 infarcted patients, a number caused by tumors, trauma, and hemorrhage were included. Anatomically, only the infarcts produce similar lesions repetitively by virtue of a similar territory of arterial supply being involved and with less variation than might occur with neoplasms or TABLE 3 T-TYPE
COMPONENT
ANALYSIS
OF 142 INFARCTS
Root
Percentage of total variation
Fluency
Comprehension
1 2 3 4 5
82 9 7 2 0
17.5 52.0 2.7 1.9 23.1
18.6 38.3 2.1 .9 34.6
Percentage of characteristics contributing to each root Repetition Naming 21.8 1.9 56. I 16.7 13.4
23.2 2.7 .6 60.9 17.0
information 18.8 5.0 38.6 19.6 11.9
8
KERTESZ AND PHIPPS
GLOBAL 727
n= II 67%
BROCli’l 56%
n-
1,
YtRNIcIE’s
59%
371
n=
I, II’
CONDUCTION 58%
n.
12
TCI
11%
61:
n
18
1110*1c
50%
901
n= 85 80:
FIG. 5. A dendrogram of all 200 patients. The first number under the clinical type predominant in that cluster is the percentage of that type belonging to the cluster. The second number under the n of the cluster indicates the percentage in the cluster belonging to that clinical type as defined in Table I.
trauma. Analyzing the infarcts alone seems to yield more precise and detailed clustering. The similarity between infarct groups I and II is not surprising since the clinical criteria to separate global from Broca’s aphasics is their comprehension and there is an obvious overlap in the other scores. The third group is an interesting mixture of various types, if you like, a “mixed expressive” group, including three global aphasics with relatively better repetition, three isolation patients who are like globals except for their better repetition, three low scoring Broca’s, one transcortical motor aphasic, and even two Wernicke’s aphasics who repeated relatively well. This cluster approaches in a broad sense what has been described clinically as the “isolation syndrome” (Geschwind, Quadfasel, & Segarra, 1968). The widely ranging composition is seemingly paradoxical but it underlines some of the drawbacks of arbitrary intuitive criteria applied in the previous classifications. One of the most interesting findings was the emergence of two distinct clusters in which the so-called conduction or central aphasics were bimodally distributed, confirming the long established doubts of clinicians about the homogeneity of this aphasic group. Cluster VI had seven conduction and five Wernicke’s aphasics. Common to this cluster were high fluency and low comprehension and repetition scores; if you like, these were “afferent conduction” aphasics. Cluster VIII, on the other hand, had conduction patients with lower fluency and higher comprehen-
TAXONOMY
OF APHASIA
9
sion, along with a few Broca’s and transcortical motor patients indicating the existence of an “efferent conduction” group. Rating of paraphasias may further clarify the distinctness of these groups. The homogeneity of some of the clusters is striking. The first and tenth represent the two most clearly defined and largest aphasic groups (globals and anomies), and these occupy the opposite extremes in the component analyses and dendrograms as well as in the severity scales. There is general agreement in the literature about the existence of these groups. However, pure clusters (by previously established criteria) can be found elsewhere. In the seventh cluster, the seven patients were all Wernicke’s, and in the fifth all were transcortical sensory aphasics. Even the large second cluster turned out to be overwhelmingly (86%) a Broca’s group. Group nine appears to be distinct from anemic aphasia because of its greater comprehension difficulty, but not to such a degree as to be grouped with Wernicke’s aphasia. It may be advantageous to use the term semantic aphasia, first advocated by Head (1926), for this group of mild impairment of comprehension associated with verbal paraphasias, and to reserve the term anemic for a purer disturbance of word finding and naming without comprehension difficulty. The degree of correspondence of most of the computer-generated clusters with clinically recognizable groups was surprising. We had been prepared to discover many alternative groupings to the classical ones, but the only major deviation was the distinct bimodality of conduction aphasia. Some of these patients were examined in a more chronic state than others. The acute ones were well enough to be tested, but some of them undoubtedly changed category in the course of recovery. For instance, some global aphasics evolved into the Broca’s group, and anemic aphasia is a common endstage for the conduction, Wernicke’s, and transcortical groups. In another study, we shall attempt to show how certain aphasias evolve and what rules this evolution follows. We anticipate the probability that further tightening of aphasic clusters will be achieved by analyzing acute and chronic cases separately, and this study is also proposed. Including nonverbal tasks or different linguistic parameters will broaden the scope of clustering and will undoubtedly alter the number and character of the groups. The spatial distribution of the clustering and the degree of separation or similarity of individual aphasics are of course due to those characteristics which were examined and subjected to analysis. The q-type Principal Component Analysis provides a two-dimensional picture where separation along the horizontal axis (root 1 in Table 3) derives from all characteristics just about evenly. The summary of these characteristics is the “aphasia quotient” or the severity scale. Therefore, distribution on the horizontal axis depends on the severity of aphasic impairment. Fluency and comprehension are the main contributions to positioning in the vertical axis
10
KERTESZ AND PHIPPS
(root 2). The visual display of further discriminating characteristics has not been undertaken due to the graphical complexity of superimposing information pertinent to third and fourth dimensions in the scatter diagram. These data are, however, made explicit in Table 3. CONCLUSION This pilot study attempts to provide objectivity and precision for clinical classification, which are very much needed for reliable research in aphasia. Ten major clusters were revealed by numerical taxonomy of infarcts and most corresponded more or less to recognizable clinical groups of aphasics. “Conduction” aphasics distributed bimodally into clearly separated clusters. The balance between specificity and objectivity, clinical relevance and mathematical abstraction is yet to be worked out fully, but appears to be a promising and exciting project. REFERENCES Cooley, W. W., & Lohnes, P. R. 1%2. Multivariate procedure for the behavioral sciences. New York: Wiley. Geschwind, N., Quadfasel, F., & Segarra, S. 1968. Isolation of the speech area. Neuropsychologia,
6,327-34X
Goodglass, H., & Kaplan, E. 1972. The assessment of aphasia and related disorders. Philadelphia, PA: Lea & Febiger. Head, H. 1926. Aphasia and kindred disorders of speech. New York: Macmillan. Kertesz, A., & Poole, E. 1974. The aphasia quotient: The taxonomic approach to measurement of aphasic disability. The Canadian Journalof NeurologicalSciences, 1, 7- 16. Orloci, L. 1%7a. An agglomerative method for the classification of plant communities. Journal
of Ecology,
55, 193-205.
Orloci, L. 1967b. Data centering: A review and evaluation with reference to component analysis. Systematic Zoology, 16, 208-212. Sneath, P. H. A., & Sokal, R. R. 1973.Numerical Taxonomy. San Francisco, CA: Freeman. Vignolo, L. A. 1%4. Evolution of aphasia and language rehabilitation: A retrospective exploratory study. Cortex, 1, 344-367.
AND
LANGUAGE
4, I- 10 (1977)
Numerical Taxonomy of Aphasia A. KERTESZ AND J. B. PHIPPS The University of Western Ontario Two hundred consecutively seen aphasics, 142 of them with infarcts, were examined by tests of fluency, comprehension, repetition, naming, and information content. The language scores were subjected to a minimum variance clustering algorithm separately for the total and for the infarct groups. The latter generated IO clusters on a dendrogram. Attribute analysis of each cluster provided a clinically meaningful profile of language performance for these groups. The degree of correlation of most computer generated clusters with clinically recognized groups was high, and the homogeneity of some of the clusters is striking. An exception which is bimodally distributed. One of these appears to be “conduction aphasia,” clusters, with high fluency and low comprehension scores, was renamed “afferent conduction” aphasia, and the other, with lower fluency and higher comprehension, was renamed “efferent conduction” aphasia. The Principal Components Analysis was used to evaluate the discriminatory value of language characteristics, and the Nearest Neighbor Network Analysis was used to evaluate the significance of clustering. The dendrogram for all aphasics showed a less specific and less homogenous six clusters.
Aphasics have been variously classified ever since clinicians became interested in the phenomenon of acquired language impairment. Even though the literature contains many often seemingly discordant classifications, certain frequently occurring, and certain rarer, classes of aphasia are agreed upon by most clinicians. The majority of authors in this field also accept the need for accurate classification as a basis for research and treatment. In recent years, attempts have been made to define aphasic types according to performance on certain tests (e.g., Vignolo, 1964), but most often clinicians are satisfied with labeling aphasics on the basis of impressions (unmeasured performance), or upon one, or at the most two, parameters such as the fluency-nonfluency scale or the severity scale. Apart from the imprecision of impressionistic approaches, they must inevitably run the risk of bias due to any taxonomic preconceptions of aphasia held by the diagnosing clinician. Thus the utilization of specified performance tests must increase objectivity. From the language behavior of aphasics one can test relatively pure aspects of structured performance or more complex, but less abstract, features. Fluency, comprehension, repetition, naming, and information We would like to thank Patricia McCabe and Robert Kormos for their assistance. reprint requests to Dr. A. Kertesz, Department of Clinical Neurological Sciences, University of Western Ontario, St. Joseph’s Hospital, London, Ontario, Canada. Copyright 411 rights
0 1977 by Academic Press. Inc. of reproductmn nn any form reserved
I
Send The
2
KERTESZ AND PHIPPS
FIG. I. A dendogram of minimum variance clustering of 142 patients with infarcts.
content of speech have thus become standard items in most aphasia tests by virtue of their importance in describing language impairment. An intuitive taxonomi,c method of classifying aphasias based on these parameters has been published previously (Kertesz & Poole, 1974), but in the present study an objective numerical method is used, free from the historical bias of previous classifications. Numerical taxonomy is a mathematical approach to the classification of a set of measurable characteristics. It can provide more objective, precise, and repeatable conclusions than intuitive classifications, and has been used extensively in botany, zoology, economics, medicine, etc. (Sneath & Sokal, 1973). The aims of our study are to define an optimal set of aphasiac clusters, to generate profile characteristics describing these clusters, and to determine the significance of clustering of the data. Numerical taxonomy covers a wide range of approaches to, and techniques for, the analysis of data structure. Included are actual taxonomic methods (cluster analyses), trend seeking analyses (principally ordinations), and various methods of attribute analysis of the perceived classes. METHODS Two hundred consecutively examined aphasics formed the data base, of which 142 were cerebral infarcts and were separately analyzed, and 18 were tumour cases, 16 trauma, 12 hemorrhage, 5 degenerative, 5 aneurysm, and 2 miscellaneous. This representative sample was obtained from three general hospitals and one veteran’s hospital. The patients had an average age of60.6, with aSD of 1.Ol and a range from 19-87. The sex ratio, M:F = 129:71, is
TAXONOMY
3
OF APHASIA
TABLE
1
CRITERIA FORCLASSIFICATION” Fluency Global Broca’s Isolation Transcortical motor Wemicke’s Transcortical sensory Conduction Anemic
o-4 o-4
O-S o-5 5-10 5-10 S-10 5-10
Comprehension
Repetition
Naming
o-3.9 4-10 o-4
o-4.9 o-7.9
O-6 O-8 O-6 O-8 o-7 o-9 o-9 o-9
S-IO O-6.9 O-6.9 7-10
7-10
5-10 8-10 o-7.9 8-10 O-6.9 7-10
n Numbers are raw scores of set totals on the WAB. strongly skewed towards males due to the veteran population in the study. The average educational level was grade 9.9, with a SD of 1.0 and a range of 0- 18. All patients were tested on the 17tasks of the Western Aphasia Battery (WAB) (Kertesz & Poole, 1974). a modification of the Boston Aphasia Test (Goodglass & Kaplan, 1972). We selected for this study only the spoken language tasks: (I) fluency, judged by a carefully constructed scale of I- 10; (2) comprehension, measured by (a) yes-no questions, (b) auditory discrimination or pointing, and (c) auditory sequencing with relational words; (3) repetition of words, numbers, and high and low probability sentences; (4) naming-(a) visual confrontation naming of objects, (b) word fluency, (c) sentence completion, and (d) responsive speech; and (5) information content of replies to standardized conversational questions. We term, for the purposes of this paper, the data for the 10 individual tests as “subscores” and the pooled results for each of the five main areas (e.g., fluency, comprehension, etc.) as “set totals.” A minimum variance clustering algorithm called the sum of squares agglomeration (Orloci, 1967a)on the euclidean distance matrix of dissimilarities was used. This is a “tight,” or high center of gravity, clustering technique that is especially valuable for beginning explorations of taxonomic structure. Like all clustering algorithms, it imposes structure on the group, and extrinsic tests of the significance of the groups formed are required. The clustering results are displayed in dendrograms. The groups recognized were subjected to a cluster attribute analysis which produced character profiles for each group. A further analysis, of group intercentroid distances, was conducted to obtain information on the validity of the groups so far recognized. Ordination was carried out by the Principal Components Analysis (PCA) (Cooley & Lohnes, 1962) of the correlation matrix. Both q- and r-strategies were employed (Orloci, l%7b), the former being used to depict the patients in the most efficient 2-space, while the latter provided information about the contribution of the different attributes to the spatial arrangement of the entities (patients).
RESULTS
The dendrogram of 142 infarcts (language area set totals) neatly generates 10 groups at the 2.5% level of total variance as shown in Fig. 1. The proportion of various clinical types as defined by previously published clinical taxonomic criteria (Table 1) was determined for each cluster (Table 2).
KERTESZ AND PHIPPS TABLE 2 CLUSTER COMPOSITION
Number of patients
Percentage of clinical aphasic types
I II III
30 15 12
IV
13
V VI VII VIII IX X
4 12 11 7 18 20
97% Global 86% Broca’s 25% Global 25% Broca’s 25% Isolation 54% Broca’s 23% Isolation 23% Transcortical motor 100% Transcortical sensory 58% Conduction 100% Wernicke’s 57% Conduction 63% Anemic 100% Anemic
Cluster
When subjected to attribute analysis, the average values and standard deviations for each of the 10 recognized clusters provided a meaningful profile for each group (Fig. 2). The first cluster showed low values on all scores (this group can be called global aphasia). Cluster two was very similar except for a much higher rating for character 2 (comprehension) and corresponds to Broca’s aphasia. The third group had moderately high repetition but low fluency (nonfluent echolalic sensory aphasia or “isolation syndrome”). Cluster four had higher scores with low fluency and high repetition and comprehension (transcortical motor aphasia). The small fifth cluster scored well on characters 3 and 1 (repetition and fluency) and moderately low on the others (transcortical sensory aphasia). The sixth cluster had low repetition, naming, and comprehension but high fluency (we called this “afferent conduction aphasia”). The seventh cluster had lowish scores, not unlike clusters I and II, but it differs in having a high value for fluency (this group corresponds to Wernicke’s aphasia). Cluster eight had scores slightly higher than the median but differed from group VI in having a high value for character 4 (naming) and a relatively low score for fluency (we took the liberty of calling this “efferent conduction aphasia”). Clusters nine and ten represent, respectively, a poorer and a better (near normal) performing set of high scores (anemic aphasia). The relatively low standard deviations for each attribute in each cluster support the significance of the groups detected. It proved interesting to subject the 10 clusters to a Nearest Neighbor Network Analysis in which intercentroid distances were computed and deviations along the intercentroid axes were established (Fig. 3). This is
TAXONOMY
”
&W
10 I
OF APHASIA
FLUENCY COMPREHENSION ;?&ikTlON INFORMATION
IX
“I
FIG. 2. Average each cluster.
values and standard deviations
of cluster attributes
x
(language scores) of
essentially an extension of the method presented by Sokal and Sneath (1973, p. 286). There are four pairs of mutually closest clusters (I, II), (IV, V), (VI, VII), and (IX, X). If the summed deviations on the intercentroid axes are more than the intercentroid distance, then the clusters are not significantly different statistically. The only example of this situation is cluster pair (I-II), and inspection of the cluster attribute analysis for this group confirms the similarity of these two clusters as well as the attribute which makes them distinct clinically: comprehension. Cluster pairs (IV-V), (VI-VII), and (I-III) are also quite close, but each member of the pair nevertheless constitutes a distinct cluster. It is noteworthy that when second-nearest neighbors have to be utilized to complete the network, e.g., by forming the bond (IV-VIII), the dispersions do not remotely approach the intercentroid distances. Thus, we may conclude that there are at least nine statistically distinct groups present. Theq-type PCA is essential in interpreting the general trends of variation observed (Fig. 4). The main scatter ofgroups is along the first factor which, significantly, accounts for 82% of the total variation. This underlines the fairly strong degree of correlation among all the attributes (tests) measured. Some groups however, e.g., (III, VII) and (IV, VI), are separated principally on the second factor, which accounts for about 9% of the total variation, and our results from the cluster profile analysis indicate in which characteristics they differ. The r-type PCA ranks the characteristics according to their discriminatory value on the q-type PCA factors (Table 3). It illustrates a fairly even significance (i.e., high correlation) of characters on the first root (this is why axis 1 accounts for so high a
KERTESZ AND PHIPPS
361
--------.
NEICHBOURS NEAREST 2nd NEAREST NEIGHBOURS
FIG. 3. The nearest-neighbor pairs are indicated by solid lines and the second-nearest connections by interrupted lines. The arrows indicate the direction and extent of dispersion towards the centroid of the neighboring clusters. The diagram illustrates all the nearest neighbors and only those second-nearest neighbors necessary to complete a network among the 10 clusters. Numbers represent units of taxonomic distance.
percentage of the variation), but on root 2, characteristics 1 (fluency) and 2 (comprehension) have by far the most differentiating power, on root 3, repetition. The dendrogram of all of the 200 aphasics’ language totals depicts a somewhat different group structure from that for the infarcts alone. There are six larger, less homogeneous clusters (Fig. 5). In the first, 67% of 41 patients in the cluster are global; 72% of 38 globals of the total sample belong to this cluster. In the second (N = 31), 5% are Broca’s; 56% of 32 Broca’s are in this cluster. In the third (N = 33), 37% are Wernicke’s, and 36% of 37 Wernicke’s are there. In the fourth (N = 32), 44% are “conduction”; 58% of24 conduction aphasias are in this cluster. In the fifth (N = 18), 50% are transcortical sensory; 64% of the 14 transcortical sensory aphasics belong in this cluster. In the sixth (N = 45), 80% were anomies; 90% of the 40 anemic aphasics are in this cluster. The least homogeneous, the third group, contained 100% of the isolation, 37% of Wernicke’s, and 35% of the transcortical sensory aphasics-a low scoring receptive group, if you like. The fourth group, although predominantly
TAXONOMY
7
OF APHASIA
FIG. 4. Scatter diagram of 142 infarcts illustrated in the first 2 factors of the component space. The 10dendrogram-generated clusters are superimposed and the clinical classification of patients is indicated by various symbols.
conduction patients, had a fair share of Broca’s aphasics, indicating the validity of the “efferent motor aphasia” as a cluster. DISCUSSION The reason for the lower specificity of clustering in the larger group of aphasics is partly due to the nature of the lesions. In the larger group, in addition to the 142 infarcted patients, a number caused by tumors, trauma, and hemorrhage were included. Anatomically, only the infarcts produce similar lesions repetitively by virtue of a similar territory of arterial supply being involved and with less variation than might occur with neoplasms or TABLE 3 T-TYPE
COMPONENT
ANALYSIS
OF 142 INFARCTS
Root
Percentage of total variation
Fluency
Comprehension
1 2 3 4 5
82 9 7 2 0
17.5 52.0 2.7 1.9 23.1
18.6 38.3 2.1 .9 34.6
Percentage of characteristics contributing to each root Repetition Naming 21.8 1.9 56. I 16.7 13.4
23.2 2.7 .6 60.9 17.0
information 18.8 5.0 38.6 19.6 11.9
8
KERTESZ AND PHIPPS
GLOBAL 727
n= II 67%
BROCli’l 56%
n-
1,
YtRNIcIE’s
59%
371
n=
I, II’
CONDUCTION 58%
n.
12
TCI
11%
61:
n
18
1110*1c
50%
901
n= 85 80:
FIG. 5. A dendrogram of all 200 patients. The first number under the clinical type predominant in that cluster is the percentage of that type belonging to the cluster. The second number under the n of the cluster indicates the percentage in the cluster belonging to that clinical type as defined in Table I.
trauma. Analyzing the infarcts alone seems to yield more precise and detailed clustering. The similarity between infarct groups I and II is not surprising since the clinical criteria to separate global from Broca’s aphasics is their comprehension and there is an obvious overlap in the other scores. The third group is an interesting mixture of various types, if you like, a “mixed expressive” group, including three global aphasics with relatively better repetition, three isolation patients who are like globals except for their better repetition, three low scoring Broca’s, one transcortical motor aphasic, and even two Wernicke’s aphasics who repeated relatively well. This cluster approaches in a broad sense what has been described clinically as the “isolation syndrome” (Geschwind, Quadfasel, & Segarra, 1968). The widely ranging composition is seemingly paradoxical but it underlines some of the drawbacks of arbitrary intuitive criteria applied in the previous classifications. One of the most interesting findings was the emergence of two distinct clusters in which the so-called conduction or central aphasics were bimodally distributed, confirming the long established doubts of clinicians about the homogeneity of this aphasic group. Cluster VI had seven conduction and five Wernicke’s aphasics. Common to this cluster were high fluency and low comprehension and repetition scores; if you like, these were “afferent conduction” aphasics. Cluster VIII, on the other hand, had conduction patients with lower fluency and higher comprehen-
TAXONOMY
OF APHASIA
9
sion, along with a few Broca’s and transcortical motor patients indicating the existence of an “efferent conduction” group. Rating of paraphasias may further clarify the distinctness of these groups. The homogeneity of some of the clusters is striking. The first and tenth represent the two most clearly defined and largest aphasic groups (globals and anomies), and these occupy the opposite extremes in the component analyses and dendrograms as well as in the severity scales. There is general agreement in the literature about the existence of these groups. However, pure clusters (by previously established criteria) can be found elsewhere. In the seventh cluster, the seven patients were all Wernicke’s, and in the fifth all were transcortical sensory aphasics. Even the large second cluster turned out to be overwhelmingly (86%) a Broca’s group. Group nine appears to be distinct from anemic aphasia because of its greater comprehension difficulty, but not to such a degree as to be grouped with Wernicke’s aphasia. It may be advantageous to use the term semantic aphasia, first advocated by Head (1926), for this group of mild impairment of comprehension associated with verbal paraphasias, and to reserve the term anemic for a purer disturbance of word finding and naming without comprehension difficulty. The degree of correspondence of most of the computer-generated clusters with clinically recognizable groups was surprising. We had been prepared to discover many alternative groupings to the classical ones, but the only major deviation was the distinct bimodality of conduction aphasia. Some of these patients were examined in a more chronic state than others. The acute ones were well enough to be tested, but some of them undoubtedly changed category in the course of recovery. For instance, some global aphasics evolved into the Broca’s group, and anemic aphasia is a common endstage for the conduction, Wernicke’s, and transcortical groups. In another study, we shall attempt to show how certain aphasias evolve and what rules this evolution follows. We anticipate the probability that further tightening of aphasic clusters will be achieved by analyzing acute and chronic cases separately, and this study is also proposed. Including nonverbal tasks or different linguistic parameters will broaden the scope of clustering and will undoubtedly alter the number and character of the groups. The spatial distribution of the clustering and the degree of separation or similarity of individual aphasics are of course due to those characteristics which were examined and subjected to analysis. The q-type Principal Component Analysis provides a two-dimensional picture where separation along the horizontal axis (root 1 in Table 3) derives from all characteristics just about evenly. The summary of these characteristics is the “aphasia quotient” or the severity scale. Therefore, distribution on the horizontal axis depends on the severity of aphasic impairment. Fluency and comprehension are the main contributions to positioning in the vertical axis
10
KERTESZ AND PHIPPS
(root 2). The visual display of further discriminating characteristics has not been undertaken due to the graphical complexity of superimposing information pertinent to third and fourth dimensions in the scatter diagram. These data are, however, made explicit in Table 3. CONCLUSION This pilot study attempts to provide objectivity and precision for clinical classification, which are very much needed for reliable research in aphasia. Ten major clusters were revealed by numerical taxonomy of infarcts and most corresponded more or less to recognizable clinical groups of aphasics. “Conduction” aphasics distributed bimodally into clearly separated clusters. The balance between specificity and objectivity, clinical relevance and mathematical abstraction is yet to be worked out fully, but appears to be a promising and exciting project. REFERENCES Cooley, W. W., & Lohnes, P. R. 1%2. Multivariate procedure for the behavioral sciences. New York: Wiley. Geschwind, N., Quadfasel, F., & Segarra, S. 1968. Isolation of the speech area. Neuropsychologia,
6,327-34X
Goodglass, H., & Kaplan, E. 1972. The assessment of aphasia and related disorders. Philadelphia, PA: Lea & Febiger. Head, H. 1926. Aphasia and kindred disorders of speech. New York: Macmillan. Kertesz, A., & Poole, E. 1974. The aphasia quotient: The taxonomic approach to measurement of aphasic disability. The Canadian Journalof NeurologicalSciences, 1, 7- 16. Orloci, L. 1%7a. An agglomerative method for the classification of plant communities. Journal
of Ecology,
55, 193-205.
Orloci, L. 1967b. Data centering: A review and evaluation with reference to component analysis. Systematic Zoology, 16, 208-212. Sneath, P. H. A., & Sokal, R. R. 1973.Numerical Taxonomy. San Francisco, CA: Freeman. Vignolo, L. A. 1%4. Evolution of aphasia and language rehabilitation: A retrospective exploratory study. Cortex, 1, 344-367.
