Regional differences in brain electrical activity in dementia: use of spectral power and spectral ratio measures

Electroencephalography and clinical Neurophysiology, 87 (1993) 385-393

385

© 1993 Elsevier Scientific Publishers Ireland, Ltd. 0013-4694/93/$06.00

EEG91065

Regional differences in brain electrical activity in dementia: use of spectral power and spectral ratio measures Andrew F. Leuchter *, Ian A. Cook, Thomas F. Newton, Jennifer Dunkin, Donald O. Walter, Susan Rosenberg-Thompson, Peter A. Lachenbruch and Herbert Weiner Quantitatit,e EEG Laboratory, UCLA Neuropsychiatric Institute and Hospital, 760 Westwood Plaza, Los Angeles, CA 90024 (USA); Department of Psychiatry and Biobehauioral Sciences, UCLA School of Medicine; Department of Psychiatry, West Los Angeles Veterans Affairs Medical Center," and Department of Biostatistics, UCLA School of Public Health, Los Angeles, CA (USA) (Accepted for publication: 6 July 1993)

Summary The pathologic changes in dementia of the Alzheimer's type (DAT) commonly affect selected brain regions. The cortical areas affected in multi-infarct dementia (MID) are less predictable and may be secondary to subcortical gray or white matter damage that is widespread in MID. We compared several types of quantitative EEG power measures (absolute and relative power, and ratios of power) to determine their regional distribution, and their association with changes in cognitive status and age. We examined 49 subjects with clinically diagnosed mild-to-moderate DAT, 29 with mild-to-moderate MID, and 38 elderly controls (CON). We used discriminant analysis to identify, for each parameter type, the brain region and frequency band where the parameter best distinguished between groups of subjects. The parameters showed regional differences in distinguishing between DAT and MID subjects, and in their association with age and cognitive status. All parameters were useful for detecting differences between normal and demented subjects and correctly identified comparable proportions of subjects as having dementia. Subjects who were abnormal on several parameters were much more likely to have dementia. The additive effects of these parameters in correct classification suggest that they may be monitoring different physiologic processes. Combinations of several types of parameters may be more useful than individual parameters for distinguishing demented from non-demented subjects. Key words: Quantitative EEG; Alzheimer's disease; Multi-infarct dementia; Regional differences

T h e n e u r o p a t h o l o g i c findings of A l z h e i m e r ' s d i s e a s e follow a c h a r a c t e r i s t i c r e g i o n a l d i s t r i b u t i o n . A s s o c i a tion cortices ( b o t h in t h e p r e - a n d p o s t - r o l a n d i c regions) a r e c o n s i s t e n t l y a m o n g t h e b r a i n r e g i o n s containing t h e h i g h e s t c o n c e n t r a t i o n of senile p l a q u e s a n d n e u r o f i b r i t l a r y tangles, a n d a r e t h e largest cortical areas t h a t a r e p r o m i n e n t l y a f f e c t e d ( K e m p e r 1984; R o g e r s a n d M o r r i s o n 1985; Lewis et al. 1987). S t u d i e s using p o s i t r o n emission t o m o g r a p h y a n d single p h o t o n emission c o m p u t e d t o m o g r a p h y have shown t h a t functional c h a n g e s in t h e a n t e r i o r a n d p o s t e r i o r a s s o c i a t i o n cortices a r e c h a r a c t e r i s t i c o f A l z h e i m e r ' s d i s e a s e (Jagust et al. 1987; J o h n s o n et al. 1990). T h e n e u r o p a t h o l o g i c p a t t e r n o f m u l t i - i n f a r c t dem e n t i a ( M I D ) is not as p r e d i c t a b l e . M o s t c o m m o n l y , M I D l e a d s to diffuse subcortical d i s e a s e ( T o m l i n s o n 1980) with d e m y e l i n a t i o n a n d W a l l e r i a n d e g e n e r a t i o n

* Corresponding author. Tel.: (310) 825-0207; Fax: (310) 206-0991.

in a r e a s d i s t a n t from c l e a r infarcts ( W a l l i n et al. 1989a,b). B o t h white m a t t e r tracts a n d subcortical gray m a t t e r s t r u c t u r e s a r e d a m a g e d , a n d it is the v o l u m e of b r a i n a f f e c t e d r a t h e r t h a n d a m a g e to any o n e a r e a that l e a d s to the d e m e n t i a s y n d r o m e ( T o m l i n s o n 1980; W a l l i n a n d B l e n n o w 1991). T h e s e d i s p a r a t e n e u r o p a t h o l o g i c p r o c e s s e s l e a d to a c o m m o n p r e s e n t a t i o n on c o n v e n t i o n a l E E G , specifically i n c r e a s e d g e n e r a l i z e d slow-wave activity. W h i l e focal a b n o r m a l i t i e s r e p o r t e d l y a r e m o r e c o m m o n a m o n g subjects with M I D , this finding is not sufficiently p r e v a l e n t to b e of d i a g n o s t i c usefulness (Ettlin et al. 1989). W h i l e q u a n t i t a t i v e E E G ( q E E G ) p r o m i s e s to e n h a n c e the usefulness o f c o n v e n t i o n a l E E G in the d i a g n o s i s o f d e m e n t i a ( L e u c h t e r et al. 1987; L e u c h t e r a n d W a l t e r 1989), t h e t o p o g r a p h i c d i s t r i b u t i o n of abn o r m a l i t i e s a n d how t h e y might c o r r e l a t e with r e g i o n a l p a t h o l o g y in D A T or M I D have not b e e n s t u d i e d extensively. W e p e r f o r m e d this study to e x a m i n e t h e s e topographic patterns.

386 Of the many possible types of q E E G parameters, we selected four for examination in this study: absolute E E G power, relative E E G power, and spectral ratios based on either slow-wave or alpha power. The intensity of power measured in square microvolts (i.e., absolute power), as well as the percentage of total power (i.e., relative power) in both low- and high-frequency bands, have been shown to reveal shifts in the nature of E E G activity in dementia that are not apparent from examination of mean frequency (Coben et al. 1983); these measures also may be more sensitive indicators than mean frequency of the progression of disease (Coben et al. 1990). Power measures also appear to be more useful than mean or peak frequency for the detection of focal brain disease (Oken et al. 1989). In our earlier work, we proposed ratios of spectral E E G power, so-called "spectral ratios" (Leuchter et al. 1987; Leuchter and Walter 1989), as an alternative to absolute or relative power for the study of dementia. In addition to correcting for baseline power variation, spectral ratios capture both the increases in lowfrequency and decreases in high-frequency power seen in individuals with dementia. Spectral ratios appear to offer some advantages over both power and other power ratios for the early detection of dementia (Leuchter et al. 1987; Leuchter and Walter 1989). In this study, we first examined the selected parameters to determine the brain areas where the greatest differences between DAT, MID and control subjects (CON) were detected. We also studied the patterns of association of each q E E G parameter with two factors that have prominent effects on the EEG, namely subject age and the degree of cognitive impairment. Finally, we examined the diagnostic classification of subjects who showed abnormality on one or more type of parameter.

Methods

Subjects Subjects with dementia were recruited from patients at the UCLA Neuropsychiatric Hospital Alzheimer's Disease and Memory Disorders Clinic, and Geriatric Psychiatry Inpatient Unit; controls were spouses of patients or were recruited from the community. All subjects were at least 60 years old and had received no medications known to affect CNS function for more than 10 days prior to the time of their EEG. Control subjects with a history of mental illness were excluded, as were control or demented subjects with confounding factors that might affect brain function (i.e., head trauma or brain surgery). The evaluation for dementia included a complete medical history, physical examination, comprehensive laboratory testing for reversible causes of dementia, as well as a structural imaging

A.F. LEUCHTER ET AL. study of the brain (either computed tomography or CT, or magnetic resonance imaging or MRI). Demented subjects were diagnosed using modified DSM-III-R criteria for primary degenerative dementia of the Alzheimer's type and multi-infarct dementia, as previously described (Leuchter et al. 1987). The DAT subjects all met the criteria proposed by McKhann et al. (1984) for probable Alzheimer's disease. All MID subjects had Hachinski Ischemia Scale (Hachinski et al. 1974) scores greater than 7, or evidence of vascular damage on CT or MRI. This report is based upon 116 subjects enrolled in a longitudinal study of q E E G in dementia (49 DAT, 29 MID, and 38 CON). All subjects underwent a Folstein Mini-Mental State Examination (MMSE) within 1 week of their E E G (Folstein et al. 1975). Although the MMSE provides a limited estimate of the mental status of a subject, it is one of the more commonly used cognitive measures in q E E G studies, and previous studies have shown moderate associations between MMSE scores and q E E G measures (Brenner et al. 1986, 1988; Breslau et al. 1989; Jordan et al. 1989). There was no significant difference in the mean ages of the DAT and CON groups, although the MID subjects were slightly older (72 +_ 8, 72 _+ 8 and 78 + 7 years, respectively) (all numbers represent mean_+ S.D.). There was a nearly equal ratio of males and females in the CON group (1.1:1), and more females than males in the D A T and MID groups (2.2:1 and 1.9:1, respectively). There was no significant difference in the level of impairment between the DAT and MID groups as judged by MMSE scores; both the demented groups differed in mean and standard deviation from the CON group (17.5 +_ 7.4, 15.7 +_ 8.8 and 29.3 _+ 1.3, respectively).

Electroencephalogram procedures EEGs were performed by qualified technicians using standard clinical procedures. Electrodes were applied according to the international 10-20 system of electrode placement. The E E G was recorded with a Nihon-Kohden 4317 electroencephalograph. Sixteen channels of E E G data were collected in the eyes-closed, maximally alert, state using a montage of 4 longitudinal electrode chains (2 temporal, 2 parasagittal), designed to sample brain electrical activity in all major regions: Fp2-F8, F8-T4, T4-T6, T6-O2, Fpl-F7, F7-T3, T3-T5, T5-O1, Fp2-F4, F4-C4, C4-P4, P4-O2, Fpl-F3, F3-C3, C3-P3, and P3-OI. Eye movements were monitored with anterior scalp electrodes and a right infraorbital electrode referenced to the right ear. Quantitatit:e data collection Five minutes of q E E G data were collected from each subject while they rested in the eyes-closed, maximally alert, waking state. Data were collected for 32

REGIONAL

MEASURES

OF BRAIN

FUNCTION

IN DEMENTIA

sec periods of time; at the end of each period, data collection was suspended and the patient was vigorously re-alerted. If drowsiness occurred during a 32 sec period, data collection was suspended immediately and the patient re-alerted at that time. Data were digitized and analyzed using the FACT system (for Frequency And Coherence Topography) described previously (Leuchter et al. 1987, 1992). Sixteen channels of data were digitized at a rate of 128 p o i n t s / c h a n n e l / sec in 4 sec epochs with a time constant of 0.16 and high-frequency filter of 70 Hz. A technician subsequently eliminated epochs contaminated by muscle, eye movement, or other artifacts, or by the appearance of drowsiness. The first nine 4 sec epochs (a total of 36 sec) of artifact-free data were processed using a fast Fourier Transform and averaged to obtain a frequency spectrum for that individual. E E G power values were calculated for each of 5 frequency bands (each 4 Hz wide) between 2 and 22 Hz (2-6 Hz, 6-10 Hz, 10-14 Hz, 14-18 Hz, and 18-22 Hz). Because of concern about artifacts due to subtle eye movements in the frontal channels, commonly seen in demented patients, and since this band has not proved useful in detecting early dementia (Hier et al. 1991), power in the 0 - 2 Hz band was excluded from further analysis. Power above 22 Hz was discarded because of the low proportion of power contained in these bands. For this study, we wished to compare quantitative aspects of the posterior dominant rhythm in normal controls with that in demented subjects. This is a problematic comparison, since rhythmic posterior activity in demented subjects commonly slows out of the alpha range, into what is commonly called the high theta range (i.e., 6-8 Hz). Furthermore, the "alpha" rhythm in the normal elderly commonly slows to less than 10 Hz, so that what is commonly called the high alpha band is less likely to contain posterior dominant rhythmic activity (Mundy-Castle et al. 1954; Obrist et al. 1962; Harner 1975; Hubbard et al. 1976; Hughes and Cayaffa 1977; Matejcek 1980; Soininen et al. 1982; Torres et al. 1983; Visser et al. 1985; Giaquinto and Nolfe 1986; Prinz and Vitiello 1989). To compensate for these changes and encompass posterior dominant rhythmic activity from normal and demented elderly subjects in a single 4 Hz band, we shifted the conventional "alpha" band downward in this study (from 8-12 Hz to 6-10 Hz). This downward shift takes into account the fact that elderly demented individuals frequently have rhythmic posterior dominant activity in what is sometimes called the high theta range (i.e., 6 - 8 Hz), and that the mean posterior dominant rhythm for group of elderly normals is usually less than 10 Hz. The high end of the conventional alpha band is included in the 10-14 Hz band, and all slow-wave power is included in the 2 - 6 Hz band.

387

Data analysis Absolute power was calculated for each of the 16 E E G channels in each of 5 frequency bands between 2 and 22 Hz. Since we were interested in the relationship between power in different bands, we first performed linear regression between slow-wave power and power in each of the 4 higher-frequency bands (6-10, 10-14, 14-18, and 18-22 Hz). We performed this analysis to determine if there was a linear relationship between increases in low-frequency and decreases in highfrequency power. Such a relationship could indicate that increases in the former and decreases in the latter represent a simple "shift" from higher to lower frequencies, and that power in the high- and low-frequency bands provides redundant information. We next calculated relative power, as well as two types of spectral ratio for each channel: (1) four in which the denominator was power in the 2 - 6 Hz band ("slow-wave ratios"), and (2) three in which the denominator was power in the 6-10 Hz band ("alpha ratios"). For each type of ratio, the numerator was power in each band of higher frequency. We used logarithms of the absolute power and spectral ratios to minimize problems with skewness and kurtosis (Pollock et al. 1990). As shown in Table I, this procedure substantially normalized the distribution of the power variables. To assess the topographic characteristics of the q E E G differences for each type of parameter, we used a series of 3 2-way discriminant analyses (BMDP 7M, BMDP 1988) to examine differences between groups of subjects (i.e., D A T vs. CON, MID vs. CON, and DAT vs. MID). From these discriminant analyses we identified, for each of the 4 parameters (log transformed TABLE

I

Effect of log tansforrnation

on skew/kurtosis

ables. Both raw and log-transformed electrode Channels

of EEG

data are shown

power

site. Skew (S.E.)

Kurtosis (S.E.)

Raw

Raw

Transformed

vari-

separately

Transformed

Fp2-F8

3.82

-0.79

2.26

Fp2-F4

3.19

- 0.53

0.60

- 1.13 - 1.44

Fpl-F7

3.23

- 0.66

0.77

- 1.26

Fpl-F3

3.43

- 0.09

1.45

- 1.54

F8-T4

5.64

0.33

5.82

- 1.02

F4-C4

7.34

0.03

10.87

- 0.46

F7-T3

5.65

0.61

5.82

- 0.84

F3-C3

4.55

0.47

3.32

- 1.47

T4-T6 C4-P4

6.02 4.10

- 0.60 - 0.55

7.55 2.69

- 0.53 - 1.17

T3-T5

4.64

- 0.30

3.37

- 0.56

C3-P3

5.88

- 0.05

6.47

- 0.97

T6-O2 P4-O2

12.08 4.03

-0.21 - 1.49

31.46 2.29

-0.27 - 0.10

T5-O1

5.26

- 0.53

5.64

- 0.80

P3-O1

2.68

- 1.39

10.42

- 0.07

by

388

A.F. L E U C H T E R ET AL.

slow-wave and alpha ratios, log transformed absolute and relative power), the frequency band containing the variable showing the greatest discrimination. The percent correct classification at step 0 was tabulated, as was a list of the subjects so classified. We performed 3 additional analyses on data from all brain locations within the selected frequency band. First, we identified the 3 E E G recording locations which at step 0 in the discriminant analysis showed the greatest ability to discriminate between groups (i.e., had the highest F statistic). We reasoned that if there was consistency among the recording locations so selected (i.e., all locations came from the same brain region), this would indicate that the region was the most severely affected by disease. Second, we performed partial correlations to determine the separate associations between MMSE, age, and all E E G recording locations within the selected frequency band for each parameter. These results were tabulated by electrode site to permit examination of the topographic associations between E E G and mental status or age by diagnostic group. Third, for each subject in each of the 2-way discriminant analyses, we determined the number of parameters on which they were classified as abnormal. Given the four analyses in each 2-way comparison (one for each p a r a m e t e r type), the values for each subject ranged from 0 from 4. We then used logistic regression to examine the relationship between research diagnosis and the number of parameters on which a subject was classified as abnormal. We used research diagnoses as dichotomous response variables in two separate analyses (0 = normal, 1 = D A T or MID), and the number of variables on which the subject was classified as D A T or M I D as the predictor. Following estimation of the logit models, we calculated the predicted probabilities for having a correct research diagnosis of D A T or MID.

Results

Relationship between slow-wave and high-frequency power There was no significant linear relationship between power in the slow-wave band and bands of higher frequency, with P values consistently greater than 0.2. In particular, the correlation between the highestfrequency and lowest-frequency bands (numerator and denominator of the slow wave ratios) was negligible (r = 0.1, P = 0.39).

Frequency bands and parameters showing intergroup differences Among the absolute and relative power variables, the 2 - 6 Hz band showed the greatest ability to discriminate between normal and demented (either D A T or M I D ) subjects. For absolute power, the best discriminators were seen in the anterior head region, while for relative power the best discriminators were seen posteriorly (Table II). A m o n g the slow-wave spectral ratio variables, the 18-22 Hz band showed the greatest differences between normal and demented (either D A T or M I D ) subjects. Like the relative power measure, the largest consistent between-group difference was seen in the posterior head regions. The highest F statistics for the the absolute and relative power and slow-wave ratio variables were comparable (all above 20). The alpha ratios showed a different magnitude and topographic pattern of between-group difference. For the D A T subjects the greatest differences from controls were seen anteriorly, while for the M I D subjects the greatest differences were seen posteriorly. The F statistics comparing the normal and demented subjects were lowest for the alpha ratios, and the percentage classifications were slightly lower as well (Table II).

T A B L E II Variables with greatest ability to discriminate among groups. Characteristics of the variables that show the greatest discrimination between the 3 groups of subjects in 2-way comparisons, f indicates the frequency band that contained the highest between-group variance ( F statistic) at step 0, sites are the electrode pairs that showed the highest F statistics within the selected band, F is the value of the statistic for each site, and % is the overall percentage of subjects correctly classified using the single best variable. Percentage classifications are not shown for the second and third variables in each comparison since F statistics are adjusted by the combination of several variables in subsequent steps. Absolute power

CON vs. DAT CON vs. MID DAT vs. MID

Relative power

Slow-wave ratio

Alpha ratio

f

Sites

F

%

f

Sites

F

%

f

Sites

F

%

f

Sites

F

%

2-6

Fpl-F7 Fpl-F3 F3-C3 Fpl-F3 F3-C3 Fpl-F7 T5-O1 Fpl-F7 T3-T5

24 20 18 32 25 22 3 2 1

77 81 66 -

2-6

T5-OI T3-T5 P3-O1 T3-T5 T5-OI P3-O1 Fp2-F8 T5-OI T6-O2

23 21 20 24 23 22 5 3 2

66 71 64 -

18-22

P3-O1 P4-O2 C4-P4 P3-O1 C4-P4 P4-O2 Fpl-F7 T6-O2 F pl -F 3

21 18 14 22 20 19 3 2 1

66 76 69 -

10-14

Fpl-F3 Fp2-F8 F7-T3 T5-O1 C3-P3 P3-OI T5-OI T6-O2 Fp2-F8

9 7 3 12 9 8 4 3 2

63 67

2-6

10-14

2-6

6-10

18-22

14-18

10-14

14-18

66 -

REGIONAL

TABLE

MEASURES

OF

BRAIN

FUNCTION

389

IN DEMENTIA

I11

Partial correlations of absolute power E E G parameter Channels

CON

(logO0

(n = 38)

×

power

DAT

@ 4 Hz)

with MMSE and age, by electrode site.

(n = 49)

MID

(n = 29)

MMSE

Age

MMSE

Age

MMSE

Age

Fp2-F8

- 0.060

- 0.061

- 0.234

- 0.229

- 0.441

-- 0 . 0 5 8

Fp2-F4

- 0.110

- 0.034

- 0.240

- 0.236

- 0.438

0.088

Fpl-F7

- 0.113

- 0.034

- 0.150

- 0.302

- 0.324

0.079

Fpl-F3

-0.029

-0.045

-0.372

-0,336

-0.41/8

0.093

F8-T4

- 0.235

- 0.253

- 0.367

- 0,084

- 0.389

0.059

F4-C4

- 0.174

0.032

- 0.325

- 0,053

- 0.439

0.032

F7-T3

- 1/.291

- 0.047

- 0.295

- 0,127

- 0.316

{1.3 l 0

F3-C3

- 0.166

0.005

- 0.340

- 0,170

- 0.355

0.126

T4-T6

- 0.303

- 0.302

- 0.216

- 0,181

- 0.338

0.009

**

C4-P4

- 0.236

- 0.055

- 0.271

- 0,077

- 0.412

0.171

T3-T5

- 0.403

- 0.159

- 0.195

- 0.131

- 0.340

I). 1 3 4

C3-P3

- 0.229

0.077

- 0.302

0.006

- 0.455

0.320

T6-O2

- 0.270

- 0.103

- 0.190

- 0.076

- 0.4/)8

- 0.032

P4-O2

- 0.276

- 0.182

- 0.287

- 0.024

- 0.482

- 0.ll36

T5-O 1

- 0.357

- 0.225

- 0.116

- 0.071

- 0.439

0.176

P3-O1

- 0.356

- 0.243

- 0.2(10

0.031

- 0.439

(I.098

* * Significant at P < 0 . 0 1 l e v e l . Correlations are not significant at the

0.01 level

unless marked.

The comparison between the D A T and MID subjects revealed that none of the qEEG parameters detected large differences between the two groups. All F statistics were less than 6. There was no consistent regional pattern, with sites from both anterior and posterior regions selected. In comparisons between normal and demented subjects, the proportions of subjects correctly classified into the correct diagnostic group were highest for the absolute power measures (77% of DAT, 81% of MID), and lowest for the alpha ratios (63% of DAT, 67% of MID). In the comparison between the D A T and MID TABLE

subjects, all variables selected classified roughly the same proportion of subjects (66%).

Relationships between qEEG variables and cognitive status

Because of the number of linear correlations examined, we used a conservative level of statistical significance, and only associations that achieved significance at the P < 0.01 level were considered significant. All the associations are reported in Tables III-VI. Among the CON subjects, there were a few significant associations between MMSE scores and isolated EEG vari-

IV

Partial correlation of relative power E E G parameter (log(10× relative power Channel

CON

(n = 38)

MMSE

DAT

(fl, 4 H z )

with

(n = 49)

MMSE

and

age,

MID

Age

MMSE

Age

(n = 29)

MMSE

Fp2-F8

0.212

0.056

- 0.316

- 0.527

* * *

- 0.674

Fp2-F4

0.180

0.049

- 0.271

- 0.502

* * *

- 0.372

Fpl-F7

0.383

0.169

- 0.452

* * *

- 0.541 * * *

- 0.644

Fpl-F3

0.351

0.096

- 0.464

* * *

- 0.541 * * *

- 0.421

F8-T4

0.154

0.063

- 0.407

* * *

- 0.370

- 0.620

F4-C4

0.133

0.221

- 0.416

* * *

- 0.349

- 0.402

F7-T3

0.161

0.157

- 0.523

* * *

- 0.345

0.666

F3-C3

0.250

0.243

- 0.454

* * *

- 0.420

T4-T6

0.025

0.192

- 0.508

* * *

C4-P4

- 0.016

0.146

- 0.490

T3-T5

- 0.048

0.236

C3-P3

0.022

T6-O2 P4-O2

-0.136 0.004

Age * * *

-0.141 -0.127

* * *

-0.210 -0.221

* * *

0.114 1/.1/20

* * *

0.157

- 0.497

* *

0.053

- 0.317

- 0.497

* *

0.154

* * *

- 0.346

- 0.418

- 0.478

* * *

- 0.315

- 0.624

0.195

- 0.489

* * *

- 0.333

- 0.464

0.160

0.124

-0.545

***

-0.255

-0.416

0.096

0.201

- 0.483

* * *

- 0.356

- 0.358

* * *

T5-O1

-0.131

0.113

-0.510

***

-0.241

-0.541

P3-O 1

- 0.010

0.205

- 0.490

* * *

- 0.329

- 0.411

Significant at P < 0 . 0 1 l e v e l . Significant at P < 0 . 0 0 5 l e v e l . Correlations are not significant at the

by electrode site.

**

* * *

0.01 level

unless marked.

0.148 * * *

0.262

1/.111 ***

0.207 0.100

390

A.F. LEUCHTER

ET AL.

TABLE V P a r t i a l c o r r e l a t i o n o f s l o w - w a v e r a t i o E E G p a r a m e t e r (log(10 x s l o w - w a v e r a t i o @ 20 H z ) with M M S E a n d age, by e l e c t r o d e site. Channels

C O N (n = 38)

Fp2-F8 Fp2-F4 Fpl-F7 Fp l-F3 F8-T4 F4-C4 F7-T3 F3-C3 T4-T6 C4-P4 T3-T5 C3-P3 T6-O2 P4-O2 T5-O 1 P3-OI

D A T (n = 49)

M I D (n = 29)

MMSE

Age

MMSE

Age

-

0.052 0.101 - 0.018 - 0.015 0.042 - 0.012 0.026 - 0.037 - 0.137 0.048 - 0.237 -0.071 -0.283 - 0.097 - 0.252 - 0.097

0.206 0.241 0.176 0.383 0.189 0.478 0.286 0.519 0.268 0.563 0.297 0.542 0.506 0.551 0.287 0.487

0.508 0.497 0.390 0.468 0.336 0.361 0.186 0.393 0.279 0.285 0.175 0.181 0.194 0.309 0.045 0.185

0.182 0.101 0.293 0.298 0.093 0.030 0.072 - 0.199 0.038 0.152 0.337 0.080 0.235 0.256 0.312 0.242

** *** *** *** *** *** *** ***

MMSE *** *** * *** *

Age

0.481 0.328 0.488 * * 0.467 1/.431 0.414 0.395 0.477 0.384 0.408 0.397 0.453 0.428 0.401 0.420 0.408

-

0.294 0.278 0.306 1/.276 0.088 0.125 0.032 0.069 0.072 0.005 0.123 0./114 0.123 0.026 0.129 0.004

* * S i g n i f i c a n t at P < 0.01 level. * * * S i g n i f i c a n t at P < 0.005 level. C o r r e l a t i o n s a r e n o t s i g n i f i c a n t at t h e 0.01 level u n l e s s m a r k e d .

T A B L E VI P a r t i a l c o r r e l a t i o n o f a l p h a r a t i o E E G p a r a m e t e r (log(10 × a l p h a r a t i o @ 12 H z ) w i t h M M S E a n d a g e , by e l e c t r o d e site. Channels

C O N (n = 38)

Fp2-F8 Fp2-F4 Fpl-F7 Fpl-F3 F8-T4 F4-C4 F7-T3 F3-C3 T4-T6 C4-P4 T3-T5 C3-P3 T6-O2 P4-O2 T5-O l P3-O1

D A T (n = 49)

M I D (n = 29)

MMSE

Age

MMSE

Age

MMSE

0.134 0.076 0.081 - 0.075 0.221 0.045 0.236 0.037 0.072 0.070 0.176 0.051 0.035 0.089 0.044 0.072

0.004 - 0.063 0.025 - 0.183 0.076 - 0.035 - 0.027 - 0.047 - 0.031 - 0.013 0.014 - 0.037 - 0.222 - 0.097 - 0.153 - 0.141

0.201 0.300 0.079 0.196 0.101 0.330 0.062 0.258 0.265 0.347 0.153 0.330 0.425 * * * 0.466 * * * 0.291 0.440 * * *

0.192 0.336 -0.060 0.310 - 0.068 0.062 - 0.042 0.133 - 0.109 - 0.041 - 0.062 - 0.01 l - 0.117 - 0.092 - 0.045 - 0.101

0.077 0.123 0.110 0.350 0.108 0.290 - 0.002 0.206 0.179 0.295 0.064 0.185 0.281 0.175 0.140 0.259

Age

-

0.416 0.542 * * * 0.496 * * 0.470 0.034 0.310 0.144 0.442 0.135 0.059 0.031 /I. 141 0.116 0. l I 1 0.09(/ 0.209

** S i g n i f i c a n t at P < 0.005 level. * * * S i g n i f i c a n t at P < 0.001 level. C o r r e l a t i o n s a r e n o t s i g n i f i c a n t at t h e 0.01 level u n l e s s m a r k e d .

ables,

but

small

range

Among

these

the

between

power

showed

strong

power

somewhat the gion)

DAT

cognitive

relative

tive

are not

of MMSE

at less

recording showing

meaningful scores

subjects, status

Hz.

strong

(mostly

with from

associations

associations

were two

between

Slow-wave

association sites

strongest

but

of the very

group.

qEEG

All

associations 2-6

the

and

variables.

because

in this

seen

MMSE ratio

MMSE, the with

for

recording

the sites

and

rela-

variables

had

with

only

post-rolandic MMSE.

half re-

Alpha

TABLE VII P r e d i c t e d p r o b a b i l i t i e s f r o m logistic r e g r e s s i o n f o r D A T g r o u p m e m bership. N u m b e r of p a r a m e t e r s abnormal per subject

Probability of true DAT diagnosis

0 1 2 3 4

0.12 0.27 0.50 0.73 0.88

R E G I O N A L M E A S U R E S OF B R A I N F U N C T I O N IN D E M E N T I A

ratios had still weaker associations with MMSE (only 3 sites, all from the post-rolandic region), and absolute power had the weakest association, with no sites having strong correlations with MMSE. In the MID group, associations between cognitive status and q E E G were less robust than in the D A T group. Only relative power showed some strong associations with MMSE, mostly from the pre-rolandic region. Apart from one slow-wave ratio variable, no other parameters showed strong associations with MMSE.

391

A strong association also was found between the number of parameters on which a subject was classified as abnormal and a true research diagnosis of MID (X 2= 29.77, P < 0.00005). The probability of a research diagnosis of MID increased from under 10% to 97% with the number of abnormal classifications (r = 1.483, t ( 6 2 ) = 4.007, P < 0.0005). The predicted probabilities are shown in Table VIII. Logistic regression could not be performed for the comparison between the two groups of demented subjects, since all D A T subjects were correctly classified by a single variable.

Relationships between qEEG variables and age There were no significant partial correlations between age and any of the q E E G variables among the CON subjects. Among the D A T subjects in general, the relative power variables showed the strongest associations, followed by slow-wave ratios; absolute power and alpha ratios showed no association with age. Although the order of the parameters is similar to that for MMSE, the regional patterns differed. Five recording sites (all pre-rolandic) showed strong associations between age and relative power. Only 3 recording sites, again pre-rolandic, showed strong associations between slow-wave ratios and age. Finally, no sites showed strong associations between absolute power or alpha ratios and age. Associations between q E E G and age were uniformly weak in the MID group. Only two alpha ratio variables, both from the pre-rolandic region, had a statistically significant association with age; no other variables for any parameter type had any significant association.

Prediction of clinical diagnosis by multiple variables There was a strong association between the number of parameters on which a subject was classified as abnormal and a true research diagnosis of D A T (X 2 = 26.32, P < 0.00005). The probabilities of a research diagnosis of D A T from the logit model increased substantially with the number of abnormal classifications, from 12% to nearly 90% (r = 1.006, t ( 8 6 ) = 4.373, P < 0.0005). The predicted probabilities are shown in Table VII.

T A B L E VIII Predicted probabilities from logistic regression for MID group membership. N u m b e r of parameters abnormal per subject

Probability of true MID diagnosis

0

0.09 0.31 0.66 0.90 0.97

1

2 3 4

Discussion These results show that all Of the q E E G parameters studied have value in distinguishing between demented and non-demented subjects, with the strongest parameter (absolute slow-wave power) correctly classifying 7581% of subjects, and the weakest parameter (alpha ratios) classifying 63-66% of subjects. The proportions of subjects correctly classified by most parameters were comparable. One could conclude that investigators should rely upon the single parameter type that yields the greatest accuracy. Examination of the characteristics of each type of parameter, however, suggests that these parameters yield complementary information regarding subjects with dementia. The complementary nature of these different q E E G parameters is supported by several findings. First, the power values in high- and low-frequency bands were not statistically associated. This finding may indicate that the decreases in high- and increases in lowfrequency power seen in dementia may be independent processes, and that different methods for assessing power (absolute, relative, or ratios) would yield distinct information. Second, logistic regression indicates that classification of a subject as abnormal on multiple parameters greatly increases the certainty that a subject has dementia. The fact that the parameters have cumulative diagnostic significance also suggests that they contain different information about brain function. If the information from the different parameters were largely redundant, one would not expect such a broad range of predicted probabilities, nor such a pronounced cumulative effect. Third, the brain region that appeared to be most abnormal differed not only according to subject group, but also according to the type of q E E G parameter examined. The largest differences were found in either the pre-rolandic (absolute power, or alpha ratios in MID) or post-rolandic (relative power, slow-wave ratios, or alpha ratios in DAT) regions. There also were regional differences in the associations with age and

392

MMSE. It is difficult to assess the statistical significance of these regional differences. Some of the topographic differences appear large, while others appear to be only trends; no conclusions may be drawn about the significance of these differences at this time. The differences in topography among the parameters could be explained in part by the process of "power standardization," since each of the parameters except for absolute power is standardized either for total power (i.e., relative power), or for power in another frequency band (i.e., slow-wave and alpha ratios). The change in results with power standardization could reflect a change in the variance, since these parameters implicitly adjust for the total power production in an individual. Such standardization could explain shifts in the region that shows greatest discrimination depending on the parameter used. A more intriguing interpretation, however, is that different parameters are sensitive to different pathologic processes, or to different manifestations of the same process in different cortical areas. This interpretation is supported by the absence of a statistical association between power in different frequency bands. Combinations of power in more than one band into a single measure (such as relative power or a ratio) may therefore be sensitive to different processes. The differing associations between q E E G parameters and cognitive status also may suggest that these parameters measure different processes, but it is diffcult to draw clear conclusions because of the many correlations performed. Our results on topographic differences in q E E G parameters are difficult to compare with previous studies, since most investigators have reported data from one or a few q E E G channels (Coben et al. 1983, 1990) or have pooled data (Brenner et al. 1986, 1988) without assessing the topographic distribution of the parameters. One study searched for an electrophysiologic correlate of post-rolandic structural and functional pathology in D A T using alpha power and evoked potential abnormalities (Jordan et al. 1989) and found only "occasional" posterior temporo-parietal abnormalities. Stigsby and colleagues (1981) studied the mean frequency and voltage in anterior and posterior brain regions in subjects with DAT: while the frequency and voltage declined somewhat in the post-rolandic region, this decrease was not associated with decreased cerebral blood flow in the same region. Wzolek and colleagues (1992) also found only weak associations between defects in perfusion on H M P A O - S P E C T and qEEG. Reports on small numbers of patients suggest that excessive slow-wave power may be most prominent frontally (Leuchter and Walter 1989; Leuchter et al. 1991). Changes in high-frequency activity have been reported in dementia (Coben et al. 1983, 1990; Brenner et al. 1986), but the distribution of these changes has yet to be described in the literature. One earlier report

A.F. LEUCHTER ET AL.

suggested that spectral ratios may decrease most prominently in the posterior region (Leuchter et al. 1987). This change in high-frequency rhythms may occur independently of excess slowing and may occur earlier in the course of dementia (Coben et al. 1990). It also is important to note that the frequency banding used in this study differs from some previous studies. As discussed in the Methods, we believe that these bands may increase the sensitivity of q E E G to the presence of dementia. It is possible, however, that some individuals with dementia and posterior dominant rhythms in the 10-12 Hz range may best be assessed with a conventional alpha band. Each of the parameters examined in this study offers some advantages over the others in the study of elderly subjects with cognitive impairment. All of the parameters in this study appear to be useful for characterizing the neurophysiology of dementia. By combining them in future research, it may be possible to develop measures that are specific for the differential diagnosis of dementia. We are indebted to Ms. Suzanne Hodgkin, R.EEG.T., and Ms. Bonnie Delaney, R.EEG.T., who performed EEGs for this study, and to Ms. Toni Saunders, who performed quantitative analysis of the EEG data, This work was supported by Research Grant MH 40705 and Geriatric Mental Health Academic Award MH 00665 from the NIMH (Dr. Leuchter), a fellowship for Dr. Cook from the John E. Fetzer Institute, and Training Grant MH 17140 from the NIMH (Dr. Newton).

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