Alpha rhythms in mild dements during visual delayed choice reaction time tasks: A MEG study

Brain Research Bulletin 65 (2005) 457–470

Alpha rhythms in mild dements during visual delayed choice reaction time tasks: A MEG study Claudio Babiloni a,b,c,∗ , Emanuele Cassetta c , Paola Chiovenda c , Claudio Del Percio a,b , Matilde Ercolani c , Davide Vito Moretti a , Filomena Moffa c , Patrizio Pasqualetti b,c , Vittorio Pizzella d,e , Gian Luca Romani d,e , Franca Tecchio c,f , Filippo Zappasodi c,f , Paolo Maria Rossini b,c,g a

Dipartimento di Fisiologia Umana e Farmacologia, Universit`a La Sapienza, P.le Aldo Moro 5, 00185 Roma, Italy b IRCCS S Giovanni di Dio, Via Pilastroni, Brescia, Italy c AFaR.-Dip. di Neuroscienze, S. Giovanni Calibita, Fatebenefratelli Isola Tiberina, Roma, Italy d Dipartimento di Scienze Cliniche e Bioimmagini, Universit` a G. D’Annunzio, Chieti, Italy e ITAB-Fondazione “Universit` a G. D’Annunzio”, Chieti, Italy f Istituto di Scienze e Tecnologie della Cognizione-CNR, Roma, Italy g Clinica Neurologica, Universit` a Campus Biomedico, Roma, Italy Received 30 July 2004; received in revised form 26 October 2004; accepted 12 January 2005 Available online 28 March 2005

Abstract Can simple delayed response tasks affect latency and amplitude of magnetoencephalographic midline alpha rhythms (6–12 Hz) in early dementia? We recruited 15 mild Alzheimer’s disease (AD) and 10 vascular dementia (VaD) patients (paired mini mental state exam of 17–24). The control groups comprised 18 young and 22 elderly normal subjects. In the first task, a simple “cue” stimulus (one bit) was memorized along a brief delay period (3.5–5.5 s) up to a “go” stimulus triggering (right or left) button press. In the second task, the “cue” stimulus remained available along the delay period. Event-related reduction in power of the alpha rhythms indexed the cortical activation (event-related desynchronization, ERD) for the trials associated with correct behavioral responses. Behavioral performances to both tasks were lower in the AD and VaD patients than in the normal subjects. In particular, just four AD and five VaD patients executed a sufficient amount of correct responses for the alpha ERD analysis, so they were included in a unique group. In both tasks, the alpha ERD peak was later in latency in the demented and normal elderly subjects than in the normal young subjects. Furthermore, the alpha ERD peak was stronger in amplitude in the demented patients than in the normal subjects. These results suggest that simple delayed response tasks during physiological recordings are quite difficult for patients even at an early dementia stage. Such difficulty may induce the abnormal amount of the related cortical activation in dementia as revealed by the alpha ERD. © 2005 Elsevier Inc. All rights reserved. Keywords: Delayed response task; Mild Alzheimer’s disease (AD); Vascular dementia (VaD); Magnetoencephalography (MEG); Alpha event-related desynchronization (ERD)

1. Introduction It is well known that physiological aging affects cognitive performances. Compared to young subjects, normal elderly ∗

Corresponding author. Tel.: +39 06 49910989; fax: +39 06 49910917. E-mail address: [email protected] (C. Babiloni). URL: http://hreeg.ifu.uniroma1.it/ (C. Babiloni). 0361-9230/$ – see front matter © 2005 Elsevier Inc. All rights reserved. doi:10.1016/j.brainresbull.2005.01.014

subjects present a slowing down of information processing including a decline of “on-line” short-term retention or manipulation of information [32,41,44,65,110,112]. Physiological aging is also associated with a power decline of electroencephalographic alpha rhythms (6–12 Hz) recorded during relaxed wakefulness [25,28,29,36,66,77,84]. Such an alpha power decline is more pronounced in elderly subjects suffering from mild cognitive impairments [45,56,57,61,64,114].

458

C. Babiloni et al. / Brain Research Bulletin 65 (2005) 457–470

In that case, the resting alpha rhythms have an intermediate magnitude with respect to those observed in normal elderly and dementia subjects [37,61,63]. Physiological aging affects not only the resting but also the event-related alpha rhythms. Short-term memory (STM) demands induce a typical reduction of alpha power (8–13 Hz), the so-called event-related desynchronization (ERD), indicating a cortical activation [92]. The topography of alpha ERD is more circumscribed in elderly than young subjects during STM demands [33,34]. Furthermore, the amplitude of alpha ERD is reduced as a function of physiological aging [46,48,78,91]. The effects of aging on alpha rhythms are not surprising. In resting adults, alpha rhythms reflect the activity of dominant oscillatory neural networks and represent a very global functional feature of working brain [68]. Alpha rhythms are mainly modulated by thalamo-cortical and cortico-cortical interactions, which facilitate/inhibit (i) the transmission of sensorimotor information between subcortical and cortical pathways and (ii) the retrieval of semantic information from cortical storage [24,92,105]. In the resting condition, alpha rhythms showing high voltage are usually associated with healthy brain functioning. Low-band alpha rhythms (6–10 Hz) would be mainly related to global attentional readiness, whereas high-band alpha rhythms (10–12 Hz) would reflect the oscillation of specific neural systems for the elaboration of sensorimotor or semantic information [67,69,70]. With reference to physiological aging, dementia has severe effects on cognitive functions such as attention, STM, long-term memory, and linguistic skills. Furthermore, both vascular dementia (VaD) and Alzheimer’s disease (AD) patients frequently experience deficits in frontal executive functions including focused attention, span capacity of information management, decision processes based on instructions and past experience, control of impulsive responses, and selection/running of proper responses [19,96]. An issue of interest is, hence, the definition of an experimental set up for a simple assessment of these executive functions during acquisition of neurophysiological data. To this aim, a promising approach is the use of the so-called delayed response tasks [49–52] in which subject has to retain a “cue” stimulus and a related motor response across a delay period up to an imperative “go” stimulus. This is a quite simple experimental condition that can probe also STM, in the case in which “cue” stimulus disappears during the delay period. The advantage of this approach for the study of pathological aging is the simplicity of the STM demands, given that “cue” stimulus can be retained by phonological and somatomotor coding even when the overt demand is visuo-spatial (i.e. where was the position of the “cue” stimulus?). Delayed response tasks induce visible changes in the alpha rhythms of normal young [4,18,40,85] and elderly [5] subjects. In young adults, alpha ERD reduces its amplitude with the STM variant of delayed responses [4,18,85]. In the same condition, frontal alpha ERD is lower in amplitude in normal young than elderly subjects, in line with the notion

of a lower engagement of frontal executive circuits in young people [5]. To our knowledge, no clear evidence is currently available on alpha ERD of demented patients during delayed response tasks, possibly for the difficulty of obtaining a sufficient number of correct behavioral responses in the framework of electroencephalographic recordings. In the present study, we tested whether two very simple delayed response tasks could affect midline alpha ERD in demented patients compared to normal young and elderly subjects. In the STM variant of delayed response task, subjects had to retain a very simple visual “cue” stimulus (just one bit) for a few seconds. In the no short-term memory (NSTM) variant, the “cue” stimulus was available up to the “go” stimulus. The rationale for so low-demanding cognitive tasks is that neurophysiological studies are based on many repetitions of short trials, in order to increase the signal-to-noise ratio of the data. The simplicity of the task is supposed to minimize fatigue/distraction and to maximize the amount of correct behavioral responses whose neural correlates are compared between control and demented subjects. Here a further reduction of fatigue/distraction for the patients was ensured by the use of magnetoencephalography (MEG). In contrast to electroencephalography, MEG technique includes a very quick and comfortable procedure for the montage of recording sensors, very suitable for the neurophysiological study of cognitive functions in aged people. In particular, the main scientific issues were the following. Can demented patients perform simple delayed response tasks with a sufficiently large amount of correct behavioral responses for the subsequent alpha ERD analysis? Can that analysis reveal changes in the latency and amplitude of the alpha ERD in demented patients compared to control subjects? In these patients, can a so slight STM load (just one bit) affect the latency and amplitude of the alpha ERD?

2. Subjects and methods 2.1. Subjects Experiments were performed on 18 normal young subjects (Nyoung, mean age 25 years ± 9 standard deviation, S.D.), 22 normal elderly subjects (Nold, age 72.1 years ± 9 S.D.), 15 mild AD patients (age 73.9 years ± 9 S.D.) and 10 vascular dementia patients (VAD, age 75.1 years ± 5 S.D.). Enrolled subjects or caregivers gave their informed written consent according to the declaration of Helsinki. The local institutional ethics committee approved general procedures.

3. Diagnostic criteria All subjects underwent general medical, neurological, and psychiatric assessments. AD and VaD patients were rated with a variety of standardized diagnostic instruments that included mini mental state

C. Babiloni et al. / Brain Research Bulletin 65 (2005) 457–470

459

from 24 to 17. The mean (± standard error, S.E.) of MMSE value was 20.7 (±0.5 S.E.) for AD patients and 20.8 (±0.9 S.E.) for VaD patients. No inter-condition statistical difference in the MMSE value was found by ANOVA analysis for repeated measures (F(1,23) = 0.0051, p = 0.94). Antidepressant (three AD and two VaD subjects) and/or antihypertensive (three AD and three VaD subjects) were suspended for 24–48 h before MEG recordings. One AD subject was regularly treated with Donepezil (AChE inhibitor) at the time of the study from 2 months. Table 1 reports for demented subjects the individual score of neuropsychological tests (MMSE, CDRS, GDS, HIS, IADL) and the type of medication (antidepressant, antihypertensive, and Donepezil if any). Nold subjects were recruited mainly among patients’ spouses. All normal subjects underwent physical and neurological examinations as well as cognitive screening (including MMSE and GDS). Subjects affected by chronic systemic illnesses, such as diabetes mellitus or organ failure, were excluded, as were subjects receiving psychoactive drugs. Subjects with a history of present or previous neurological or psychiatric disease were excluded as well. The mean of MMSE score was 28.5 (±0.3 S.E.). All had a GDS score lower than 14.

exam (MMSE) [43], clinical dementia rating scale (CDRS) [62], geriatric depression scale (GDS) [113], Hachinski ischemic scale (HIS) [98], and instrumental activities of daily living (IADL) [75]. Notably, patients were not included in the study if there was evidence of concomitant extra-pyramidal symptoms, reversible dementias or major depression (all patients had GDS score lower than 14). In particular, we carefully excluded from the study patients with strong fluctuations in cognitive performance, suggesting a possible Lewy body dementia, and patients showing mixed features of dementia such as Alzheimer and vascular. In addition, all patients underwent neuroimaging diagnostic procedures (CT or MRI) in order to exclude other causes of cognitive decline. Final diagnosis of AD was performed according to NINCDS-ADRDA [80] and DSM IV criteria. Instead, diagnosis of VaD was performed according to NINDS-AIREN criteria [97]. VaD patients had Hachinski ischemic scores ≥4 [98]. All VaD patients underwent MRI scan, in order to select only cases showing widespread subcortical vascular involvement. VaD patients with major vascular cortical impairment (stroke) were excluded due to high spatial variability of related brain lesions. In line with the aims of the present study, selected AD and VaD patients had a MMSE score ranging

Table 1 Individual score of neuropsychological tests and type of medication (antidepressant, antihypertensive, and Donepezil if any) for the present Alzheimer disease (AD) and vascular dementia (VaD) patients Subjects

MMSE

CDRS

GDS

AD subjects #1 #2 #3 #4 #5 #6 #7 #8 #9 #10 #11 #12 #13 #14 #15

20 22 23 23 21 24 22 17 19 20 22 19 20 21 18

3 3 3 3 4 4 3 4 3 3 3 4 3 3 3

4 4 4 7 4 13 4 10 13 4 4 5 4 5 4

VaD subjects #1 #2 #3 #4 #5 #6 #7 #8 #9 #10

21 24 24 23 18 17 22 20 22 17

3 3 3 3 4 3 3 4 3 4

4 4 13 4 12 5 6 3 4 4

HIS

IADL

Antidepressant

Antihypertensive

Donepezil

3 3 3 2 2 2 0 3 2 4 3 3 3 3 4

6 6 5 5 5 4 5 2 7 5 6 4 5 5 5

No No No No No Yes Yes Yes No No No No No No No

No No Yes No No No No Yes Yes No No No No No No

No No No No No No No No Yes No No No No No No

9 10 2 9 9 10 9 9 8 7

4 4 7 4 4 4 4 5 3 4

No No Yes No No No No No No Yes

Yes No Yes Yes No No No No No No

No No No No No No No No No No

The neuropsychological tests included mini mental state exam (MMSE), clinical dementia rating scale (CDRS), geriatric depression scale (GDS), Hachinski ischemic scale (HIS), and instrumental activities of daily living (IADL). Subjects included in the alpha event related desynchronization (ERD) analysis were emphasized in bold.

460

C. Babiloni et al. / Brain Research Bulletin 65 (2005) 457–470

Fig. 1. Delayed response tasks. The sequence of events for the condition of short-term memory (STM) was as follows: (i) a cross at the center of the monitor as a visual warning stimulus lasting 1 s, (ii) a couple of vertical bars as a visual “cue” stimulus lasting 2 s, (iii) a blank screen as a delay period lasting 3.5–5.5 s, (iv) a circle at the center of the monitor as a “go” stimulus lasting 1 s, and (v) a finger movement as a motor response. Subjects had to press either left button of a homemade device if the taller bar was at the left monitor side or right button if the taller bar was at the right monitor side. The two buttons of the device were distant about 10 cm from each other and were electronically connected to the mouse of the computer giving the visual stimuli. Compared to the STM task, the condition with no memory load (no STM, NSTM) had visual cue stimuli lasting up to the “go” stimulus.

3.1. Delayed response tasks The recruited subjects were seated in a comfortable reclining armchair, placed in a dimly lit, sound-damped, and magnetically shielded room (Vacuumschmelze GMBH). Before the entrance in the magnetically shielded room, the subjects were demagnetized using strong oscillating magnetic fields. A mirror was placed in front of them to reflect a computer monitor placed out of the magnetically shielded room. The distance between the subjects and the mirror was about 100 cm. Fig. 1 illustrates the main features of the two simple delayed response tasks used. The STM variant of the delayed response task comprised the following sequence of events: (i) baseline stimulus (a cross at the center of the monitor; 1.2 cm of height and width); (ii) visual warning stimulus (the cross was surrounded by a circle for 1 s); (iii) visual “cue” stimulus (two vertical bars; 3.5 cm of width and 4.3–12.2 cm of height for 2 s); (iv) delay period (blank screen for 3.5–5.5 s); (v) “go” stimulus (the circle appeared again for 1 s); and (vi) right finger movement to press the right or left button of a custom-made device compatible with MEG recordings. Subjects had to press left button if the taller bar (“cue” stimulus) was at the left screen side, whereas they had to click the right button if the taller bar was at the right screen side. The two buttons of the device were distant about 10 cm from each other and were electronically connected to the mouse of the computer giving the visual stimuli. In the control condition (no STM, NSTM), the visual “cue” stimulus was maintained up to the “go” stimulus.

Two trial blocks for each condition were pseudo-randomly intermingled (block = 50 single trials; 2 min pause). Subjects were told in advance if the block was NSTM or STM. The global luminance of the monitor was kept uniform across the two conditions (i.e. the changes of the scene were limited to the form) as directly measured on the screen (Tektronik J17 & J1800 Series LumaColor Photometer). The subjects were free to use any memorization strategy including visuo-spatial imagery, somatomotor preparation, and mental “phonological” coding and rehearsal, in order to maximize the number of correct behavioral responses. They were told that motor responses had to be started by 2.5 s after the onset of the “go” stimulus. Some methodological remarks are of extreme importance in the present study. We carefully designed the training of the subjects for this experiment, to minimize the number of incorrect behavioral responses (indeed, the analysis had to be focused on the MEG data related to the correct responses). One day before the recording session, a training of about two sessions (about 1 h each) was performed outside and inside the magnetically shielded room, to familiarize the subjects with the apparatus and the tasks. The day of the recording session, another training of about 10 min inside magnetically shielded room refreshed the previous training sessions before the beginning of the MEG recording. Furthermore, during the recording session, an experimenter inside the magnetically shielded room remembered the instructions and motivated the patients in the case of passive or inattentive behavior. 3.2. MEG recordings MEG data were recorded (0.1–60 Hz bandpass; 256 Hz sampling frequency) with a 28-channel apparatus including 16 first-order axial gradiometers (1.8 cm coil diameter and 8 cm baseline), 9 peripheral magnetometers (pick-up area 81 mm2 ), which were coupled to low noise dc-SQUIDs. The 25 sensors were regularly distributed on a spherical surface having a diameter of about 16 cm. This surface was centered 3 cm anterior to vertex or Cz scalp site (10–20 system). Globally, the sensor array covered well the scalp area around the fronto-parietal midline. The exact position of the magnetic sensors was identified by using six firmly taped coils whose 3D-positions were digitized (Polhemus Isotrak) at the beginning of the recording session with reference to four subjects’ anatomical landmarks (nasion, two preauricular points, and vertex). Electrooculogram (0.1–60 Hz bandpass; 256 Hz sampling frequency) and surface electromyographic activity of bilateral extensor digitorum muscles (1–60 Hz bandpass; 256 Hz sampling frequency) were also collected via disk electrodes filled with conductive jelly respectively placed at the outer canthi and in belly tendon montages. Electrooculogram monitored blinking or eye movements, whereas electromyogram monitored the voluntary movements as well as involuntary mirror movements and muscle activations.

C. Babiloni et al. / Brain Research Bulletin 65 (2005) 457–470

3.3. Off-line preliminary MEG data analysis The collected MEG data were segmented in single trials each spanning from −2.5 to +11.5 s, the zerotime being the onset of the “cue” stimulus (i.e. vertical bars). Two independent experimenters (inter-rater reliability higher than 95%) discarded the MEG single trials associated with: (i) eye movement or blinking during the delay period, (ii) mirror or involuntary finger movements of the non-operant side, (iii) inadvertent motor acts, (iv) slight muscle activation of the operant side before the “go” stimulus (i.e. anticipated response), (v) movement beginning after 2.5 s from the onset of the “go” stimulus (i.e. delayed response), (vi) no response in all MEG trials and (vii) wrong indication of the “cue” position (i.e. wrong response). 3.4. Determination of individual alpha sub-bands Power spectrum MEG analysis was based on a standard FFT approach (256 sample length, no overlap ratio, linear detrend) using Welch technique and Hanning windowing function. Fig. 2 shows frequency spectrum of MEG data of a normal young subject enrolled in the present study (subj. #1). For the determination of the individual alpha sub-bands, an anchor frequency was selected according to literature guidelines [67,68,70]. This is the so-called individual alpha frequency (IAF) peak, defined as the frequency showing the higher power density at the 6–13 Hz range. With reference to the IAF peak, the frequency bands of interest were

461

as follows: alpha 1 as IAF − 4 to IAF − 2 Hz, alpha 2 as IAF − 2 Hz to IAF, and alpha 3 as IAF to IAF+2 Hz. It should be remarked that the present analysis of individual alpha sub-bands has previously disclosed invaluable information on brain rhythmicity [4,6,12,68,73,83]. 3.5. Computation of event-related desynchronization (ERD) We followed the well-known procedure for the computation of alpha ERD [90,93,94]. Artifact-free MEG segments were bandpassed (±1 Hz) at the epicenter of the alpha subbands (Bartlett function), squared, averaged across 125 ms periods (8 samples/s), and averaged across all single trials. The alpha ERD was defined as the percentage decrease of instant power density at the “event” compared to a ”pre-event” baseline (from −2 to −1 s). In particular, the ERD analysis was performed at the individual alpha 1, alpha 2 and alpha 3 sub-bands. A spline interpolating function [8,10] determined individual alpha ERD values at a theoretical position of the 25 sensors. This procedure provided alpha ERD values exactly at the same scalp sites across the experimental subjects, thus overcoming the spatial errors due to the individual shift of the positioning of the sensors. This was preferable to negligible estimation errors introduced by the spline function. These sensors were displaced over a 3D scalp model approximating each “realistic” individual head model. This template model was constructed based on the magnetic resonance data of 152 subjects, digitized at Brain Imaging Center of the Montreal Neurological Institute (SPM96). 3.6. Measurement of alpha ERD latency and amplitude The alpha 1, alpha 2 and alpha 3 ERD waveforms were analyzed to disclose the ERD peak, defined as the maximum ERD value across all sensors during the delay period. The sensor having the maximal alpha ERD value was considered as a reference for the latency of the alpha ERD peak. The amplitude of the alpha ERD peak was automatically measured for each experimental condition at each of the 25 sensors (topographical map of the alpha ERD). 3.7. Statistical analysis

Fig. 2. Typical frequency spectrum computed from magnetoencephalographic (MEG) data. Frequency bands of interest were alpha 1, alpha 2 and alpha 3, which were computed according to a well-known procedure based on the detection of individual alpha frequency (IAF) peak (see Section 2).

Statistical comparisons were performed by ANOVA for repeated measures. Mauchly’s test evaluated the sphericity assumption and, in case of a violation, the correction of the degrees of freedom was made by Greenhouse–Geisser procedure. Duncan test was used for post-hoc comparisons only for the statistically significant interaction of the ANOVA analysis (p < 0.05). In particular, two ANOVA analyses were performed for the evaluation of the behavioral responses, namely the percentage of correct responses and the percentage of the type of errors. Furthermore, two ANOVA analyses were performed for the

462

C. Babiloni et al. / Brain Research Bulletin 65 (2005) 457–470

evaluation of the latency and amplitude of the alpha ERD. For readers’ convenience, details of these ANOVA designs will be reported in Section 4.

4. Results 4.1. Behavioral responses For the evaluation of percentage of the correct behavioral responses, ANOVA analysis used group (Nyoung, Nold, AD, and VaD) as between-subjects factor and condition (NSTM, STM) as within-subjects factor. There was a main statistical effect for group (F(3,61) = 23.7; p < 0.0001). Duncan post-hoc testing indicated that, during both NSTM and STM conditions, the percentage of the correct responses was significantly higher in the Nyoung group (NSTM, 97.8 ± 0.5% S.E.; STM, 97.9 ± 0.5% S.E.) than in the Nold group (NSTM, 71.7% ± 5.7% S.E.; STM, 72.1 ± 5.1% S.E.; p = 0.003). Furthermore, percentage of the correct responses was significantly higher in these normal groups than in the AD (NSTM, 38.2 ± 8.8% S.E.; STM, 35.4 ± 8.5% S.E.; p = 0.0002) and VaD (NSTM, 37 ± 8.9% S.E.; STM, 38.2 ± 7.6% S.E.; p = 0.0003) groups. Of note, the removal of the small subgroup of treated patients did not change the statistical results on percentage of the correct behavioral responses (p < 0.05). Table 2 reports the percentage mean value (±S.E.) and the real mean value (±S.E.) of the kind of errors in NSTM and STM conditions for Nyoung, Nold, AD and VaD groups. For the evaluation of the percentage of the kind of errors (%), ANOVA analysis used group (Nyoung, Nold, AD, and VaD) as a between-subjects factor as well as condition (NSTM, STM) and error type (wrong responses, anticipated response

Fig. 3. Means across subjects (±S.E.) of the behavioral errors as provided by a statistical ANOVA interaction between the factors group (Nyoung, Nold, AD, VaD) and error type (wrong responses, anticipated responses with respect to the “go” stimulus, delayed responses). The results of Duncan posthoc testing are indicated by asterisks. Nyoung, normal young subjects; Nold, normal elderly subjects; AD, Alzheimer’s disease patients; VaD, vascular dementia patients.

with respect to the go stimulus, delayed response) as withinsubjects factors. Of note, all subjects executed practically always the motor response for each MEG trial. Therefore, “no response” was not included as error type in the ANOVA analysis. There was a statistical interaction between group and error type (F(6,122) = 5.65; p = 0.0001). Fig. 3 illustrates the means (±S.E.) representing this significant statistical interaction. Duncan post-hoc testing indicated that, regardless of the condition (NSTM, STM), there were more

Table 2 Percentage mean value (±S.E.) and real mean value (±S.E.) of kind of errors in NSTM and STM conditions for Nyoung, Nold, AD and VaD groups Error type NSTM Wrong response Percentage value Real value Anticipated response Percentage value Real value Delayed response Percentage value Real value STM Wrong response Percentage value Real value Anticipated response Percentage value Real value Delayed response Percentage value Real value

Nyoung

Nold

AD

0.5 (±0.2) 1 (±0.4)

7.2 (±2.9) 14.2 (±26.7)

15.9 (±5) 30.3 (±7.9)

12.5 (±5) 18.4 (±5.9)

3.1 (±1.6) 5.6 (±2.7)

4.1 (±1.1) 8.5 (±2.3)

8.2 (±4.1) 17.1 (±6.8)

1.6 (±0.3) 2.9 (±0.6)

18.1 (±3.8) 36.9 (±9.9)

41.9 (±7.5) 93.7 (±20.2)

42.3 (±11.4) 100.6 (±32.3)

0.8 (±0.2) 1.4 (±0.4)

6.9 (±2.5) 12.9 (±4.4)

15 (±3.9) 27.5 (±6.3)

14.4 (±4.8) 20.9 (±5.8)

0.4 (±0.2 0.7 (±0.3)

3.9 (±2.3) 6.1 (±3.2)

5 (±1.5) 8.9 (±9.5)

6.3 (±3) 14.2 (±6.2)

0.9 (±0.3) 1.6 (±0.5)

17.2 (±3.9) 34.6 (±9.7)

44.5 (±6.9) 94.4 (±18)

40 (±9.7) 95.3 (±30.2)

0 (±0) 0 (±0)

VaD

The kinds of errors were the following: anticipated responses (slight muscle activation of the operant side before the “go” stimulus), delayed response (movement beginning after 2.5 s from the onset of the “go” stimulus), and wrong response (wrong indication of the “cue” position).

C. Babiloni et al. / Brain Research Bulletin 65 (2005) 457–470

wrong responses in the AD (NSTM, 15.9 ± 5% S.E.; STM, 15 ± 3.9% S.E.) and VaD (NSTM, 12.5 ± 5% S.E.; STM, 14.4 ± 4.8% S.E.) groups than in the Nyoung group (NSTM, 0.5 ± 0.2% S.E.; STM, 0.8 ± 0.2% S.E.; p = 0.02). Furthermore, there were more delayed responses in the AD (NSTM, 41.9 ± 7.5% S.E.; STM, 44.5 ± 6.9% S.E.) and VaD (NSTM, 42.3 ± 11.4% S.E.; STM, 40 ± 9.7% S.E.) groups than in the Nold (NSTM, 18.1 ± 3.8% S.E.; STM, 17.2 ± 3.9% S.E.; p = 0.000001) and Nyoung (NSTM, 1.6 ± 0.3% S.E.; STM, 0.9 ± 0.3% S.E.; p = 0.00001) groups. Regarding the physiological aging, there were more delayed responses in the Nold group than in the Nyoung group (p = 0.009). Finally, the AD and VaD groups executed more delayed responses than anticipated (AD: NSTM, 4.1 ± 1.1% S.E.; STM, 5 ± 1.5% S.E.; VaD: NSTM, 8.2 ± 4.1% S.E.; STM, 6.3 ± 3% S.E.; p = 0.000004) and wrong responses (p = 0.000005), while Nold groups executed more delayed responses than anticipated responses (NSTM, 3.1 ± 1.6% S.E.; 3.9 ± 2.3%; S.E. p = 0.025). Of note, the removal of the small sub-group of treated patients did not change the statistical results on the percentage of the kind of errors (p < 0.05). Summarizing, statistical behavioral differences were observed neither between NSTM and STM conditions nor between AD and VaD groups. The effects of the pathological aging were indicated by the worse performances of the AD and VaD groups compared to the Nyoung and Nold groups. The effects of the physiological aging were indicated by the greater amount of delayed responses of the Nold group compared to the Nyoung group. For AD and VaD patients, the most frequent type of error was the delayed response (movement beginning after 2.5 s from the onset of the “go” stimulus). Of note, regardless of the error due to long reaction time, percentage of correct choices of the higher bar was well above the chance level (50%) in dementia patients. 4.2. Temporal evolution of the alpha ERD peak The number of artifact-free MEG single trials with correct responses was insufficient for the alpha ERD analysis in 7 Nold, 11 AD, and 5 VaD subjects (i.e. lower than 20% of the recorded single trials). Therefore, only the MEG data of 18 Nyoung subjects, 15 Nold subjects, 4 AD patients, and 5 VaD patients were further considered for the alpha ERD analysis. For obvious numerical reasons, the AD and VaD patients were included into a unique group (demented subjects, DEM). We were encouraged to do so since the AD and VaD groups did not statistically differ as MMSE score and behavioral performance to the delayed response tasks. Noteworthy, all selected patients were characterized by a rate of correct localization of the “cue” stimuli significantly higher than the threshold of chance level (50%; p < 0.05). Of note, two out of nine subjects of the DEM group used antidepressant (one AD and one VaD) and/or antihypertensive (one VaD). In these subjects, antidepressant and antihypertensive were suspended for 24–48 h before MEG recordings.

463

In the Nyoung group, the mean of the individual artifactfree MEG data included 61 (±5 S.E.) single trials for the NSTM condition and 62 (±5 S.E.) single trials for the STM condition. In the Nold group, these were 51 (±7 S.E.) single trials for the NSTM condition and 50 (±6 S.E.) single trials for the STM condition. In the DEM group, there were 47 (±7 S.E.) single trials for the NSTM condition and 42 (±7 S.E.) single trials for the STM condition. No inter-condition statistical difference in the number of single trials was found by ANOVA analysis for repeated measures (p > 0.15). The individual alpha frequency (IAF) peak was computed in the selected MEG single trials. In the Nyoung group, the mean across subjects of the IAF was 10 Hz (±1 S.E.) for both NSTM and STM conditions. In the Nold group, the mean IAF was 9.9 Hz (±1.5 S.E.) for the NSTM condition and 9.5 Hz (±1.1 S.E.) for the STM condition. In the DEM group, the mean IAF was 10.3 Hz (±0.9 S.E.) for the NSTM condition and 10.1 Hz (±0.9 S.E.) for the STM condition. To account for the differences in the mean IAF, the statistical analysis of the alpha ERD peak used this parameter as a covariate. Fig. 4 shows alpha ERD waveforms in Nyoung, Nold, and DEM subjects during the NSTM and STM conditions. These waveforms refer to the single trials with correct behavioral responses and represent the alpha ERD averaged across all MEG sensors. It was clearly observed that: (i) the alpha ERD peaked during the delay period; (ii) the amplitude of the alpha ERD was stronger in DEM than Nold and Nyoung subjects; (iii) the latency of the alpha ERD was earlier in Nyoung than DEM and Nold subjects. Table 3 reports the mean (±S.E.) latency of the alpha ERD peak in the Nyoung, Nold, DEM groups. ANCOVA analysis used group (Nyoung, Nold, DEM) as a between-subjects factor as well as condition (STM, NSTM) and band (alpha 1, alpha 2, alpha 3) as within-subjects factors. The percentage of the correct behavioral responses and the IAF were used as covariates. There was a main effect group (F(2,35) = 8.23; p = 0.01), indicating that the latency of the alpha ERD peak was earlier in the Nyoung group than in the Nold (p = 0.0009) and DEM (p = 0.006) groups. This was true for both condiTable 3 Mean latency (±S.E.) of the peaks of alpha 1, alpha 2, and alpha 3 eventrelated desynchronization (ERD) Nyoung

Nold

DEM

NSTM Alpha 1 Alpha 2 Alpha 3

2420 (±100) 2380 (±60) 2580 (±120)

3780 (±320) 3790 (±280) 3210 (±300)

3540 (±380) 3390 (±530) 2810 (±320)

STM Alpha 1 Alpha 2 Alpha 3

2750 (±130) 2570 (±120) 2570 (±110)

3750 (±320) 3380 (±310) 3980 (±220)

3100 (±290) 3260 (±450) 3280 (±290)

These peaks refer to two very simple delayed response tasks. In the shortterm memory (STM) variant of delayed response task, subjects had to retain a simple visual “cue” stimulus for a few seconds up to the “go” stimulus triggering a hand motor response. In the no short-term memory (NSTM) variant, the “cue” stimulus was available up to the “go” stimulus.

464

C. Babiloni et al. / Brain Research Bulletin 65 (2005) 457–470

Fig. 4. The grand average waveforms of the alpha 1, alpha 2 and alpha 3 ERD (event-related desynchronization) during the NSTM and STM conditions in young (Nyoung), elderly (Nold) and demented (DEM) subjects. These waveforms refer to the single trials with correct behavioral responses and represent the alpha ERD averaged across all MEG sensors.

tions and for the three alpha sub-bands. Of note, the removal of the small sub-group of treated patients did not change the statistical results on the latency of the alpha ERD peak (p < 0.05). 4.3. Amplitude of the alpha ERD peak Fig. 5 maps the amplitude of the mean alpha ERD peak during the delay period. For both conditions, the amplitude of the alpha ERD peak appeared to be higher in the DEM group than in the Nyoung and Nold groups. For the statistical analysis, the amplitude of the alpha ERD peak was averaged across all 25 sensors. The mean alpha ERD peak served as an input for an ANCOVA analysis using group as a between-subjects factor (Nyoung, Nold, DEM) as well as condition (NSTM, STM) and band (alpha 1, alpha 2, alpha 3) as within-subjects factors. The percentage of the correct behavioral responses, the IAF, and the baseline alpha power were used as covariates. This ANCOVA analysis showed a main effect group (F(2,33) = 6.17; p = 0.005), indicating that the amplitude of the alpha ERD peak was stronger in the DEM group than in the Nold (p = 0.04) and Nyoung (p = 0.002) groups. This was true for both conditions and for the three alpha sub-bands. Of note, the removal of the small sub-group of treated patients did not change the statistical results on the amplitude of the alpha ERD peak (p < 0.05) (Fig. 6).

5. Discussion 5.1. Methodological remarks The present study aimed at evaluating the latency and amplitude of the midline alpha ERD peak in demented patients

as a rough expression of the global cortical activation induced by very simple tasks affecting executive functions, namely the delayed response tasks. It is important to stress that the following four issues were beyond the scopes of this study. The first issue was the precise source localization of the midline alpha ERD. We used a simple topographical mapping of the midline alpha ERD, in line with preceding successful studies in normal subjects engaged with STM tasks [4,5,46,78,85]. As an advantage, the alpha ERD mapping requires no computational assumption such as the number of active equivalent dipoles modeling brain sources or the regularization parameters of linear inverse source estimation [4,11,58–60,95,101,104]. As a limitation, the alpha ERD mapping does not localize the generators of the recorded MEG data. Indeed, surface MEG maxima do not spatially correspond to the location of an underlying (tangential) generator. Rather, it is positioned halfway between the inward and outward MEG maxima. The second issue beyond the scope of the present study was the dissociation of the two main processes occurring during the delay period, namely the retention and the motor preparation [99,100]. Although we did not manipulate this variable, we think that the motor preparation had a minor influence on the present results. Firstly, the delay period between the “cue” and “go” stimuli varied trial-by-trial (3.5–5.5 s), in order to discourage a pre-stimulus motor preparation. Secondly, we observed a reduction of the alpha ERD along the delay period rather than an increase up to the motor performance. Notably, the typical trend of the motor preparation is a progressive increase of the alpha ERD up to the movement execution [60,92]. On the whole, it is probable that the present alpha ERD mainly depended on the retention processes, in accordance with previous evidence showing that the

C. Babiloni et al. / Brain Research Bulletin 65 (2005) 457–470

465

Fig. 6. Means across subjects (±S.E.) of the alpha ERD peak amplitude as provided by the ANCOVA design (the percentage of the correct responses, the IAF, and the baseline alpha band power as covariates). In particular, these means refer to a main effect group (Nyoung, Nold, DEM). The results of Duncan post-hoc testing are indicated by asterisks.

alpha ERD does not differ during the delay period followed by hand versus eye movements [85]. The third and fourth issues beyond the scope of the present study were respectively the dissociation of visuo-spatial versus linguistic features of the cue stimulus [47,79] and the evaluation of the effects of the different visual stimulations during the STM versus NSTM conditions [85]. The mentioned experimental manipulations would have lengthened the MEG recording sessions in excess, causing fatigue and distraction especially in demented patients. This would have prevented the main scope of the present study, that is to keep as low as possible the cognitive demands, fatigue, and disattention across the whole experimental session. 5.2. The simple delayed response tasks were quite difficult for the demented patients

Fig. 5. 3D color maps of alpha 1, alpha 2 and alpha 3 ERD peak during the delay period of the NSTM and STM conditions in the normal young (Nyoung), normal elderly (Nold) and demented (DEM) subjects. The demented subjects included four AD and five VaD patients having a sufficient amount of artifact-free MEG single trials associated with correct behavioral responses for the alpha ERD analysis. Color scale: maximum ERD and ERS (event-related synchronization) are coded in white and violet, respectively. The maximal ERD peak values of the maps were −36% for the alpha 1, −55% for the alpha 2, and −56% for the alpha 3.

For the two delayed response tasks (NSTM, STM), we computed in the demented patients a low rate of correct behavioral responses defined as right localization of the “cue” stimulus and responses executed by 2.5 s from the onset of the “go” stimulus. In particular, there were more incorrect responses in AD (35%) and VaD (37%) patients as compared to both normal elderly (71%) and young (97%) subjects. In general, the most frequent type of error was the delayed response even in the frequent cases in which the choice of the bar was correct, possibly reflecting a slowing of decisional and visuomotor processes in the demented patients with respect to the normal subjects. These results fit the well-known decline of cognitive functions along physiological [2,78,86,88,109] and pathological [16,17,22,26,27,42,53,54,87,102,107] aging. Furthermore, the present results confirmed previous evidence reporting deficits of VaD and AD patients in frontal executive functions including focused attention, span capacity of information management, decision processes based on instructions and past experience, control of impulsive responses, and selection/running of proper responses [3,19,96].

466

C. Babiloni et al. / Brain Research Bulletin 65 (2005) 457–470

Above explanation in terms of “executive functions” should be evaluated with caution. Indeed, an open issue of the present study is whether the delayed choice reaction time paradigm can test “working memory” functions typically probed by “delayed match-to-sample” and “n-back” paradigms. With respect to these paradigms, “delayed response” tasks include no on-line probe stimulus to be matched with the memorized cue and the global mental load seems to be quite low [49–52]. Subject has to retain an association of cue stimulus-motor response along a brief delay period ending with a go stimulus. Human “working memory” functions encompass (i) selective attention to current episodes, (ii) storage and rehearsal of visuo-spatial and phonological information, and (iii) active “executive” processes such as information manipulation and motor selection/inhibition [14,15]. During the present “delayed response” task, subjects retain one out of two possible responses (“left” or “right” side) and operate mild “executive” processes such as to refrain from impulse acts and to avoid proactive interference effects across trials. Of note, a recent EEG study considered “delayed response” tasks as probing “working memory” [18]. Future studies should address this issue comparing alpha ERD in “delayed match-to-sample”, “n-back” and “delayed response” paradigms paired as mental load. In the present study, an interesting result was the lack of statistical differences in behavioral performances between the STM and NSTM conditions in our demented patients. A reasonable explanation is that the behavioral performances were affected by general executive functions common to the two conditions rather than by the slight and brief memory retention of the STM condition (i.e. just one bit for a few seconds). In that sense, a slight memory load seems to be ineffective to probe memory retention in pathological aging, at least in the framework of a MEG (electroencephalographic) recording. 5.3. Alpha ERD during delayed response tasks in dementia An important result was that only few AD and VAD patients had a sufficient amount of correct behavioral responses and artifact-free MEG single trials for alpha ERD analysis, even for the very simple cognitive demands of the present study. For this reason, these patients were included into a unique group (note that the two sub-groups of patients had a similar performance profile). Noteworthy that procedure required a careful evaluation of individual profiles of AD and VaD patients. Indeed, previous studies [9,103] have shown difference in alpha power between AD and VaD patients during resting condition (i.e. no task). In particular, these studied have reported that: (i) global and occipital alpha (6.5–12 Hz) EEG power was significantly lower in AD when compared to VaD patients during resting eyes closed and eyes opened conditions [103], (ii) occipital low-band alpha (8–10.5) power was lower in AD when compared to VaD subjects during resting eyes closed condition [9]. With reference to these studies,

the present research did not investigate power of resting alpha rhythms. Rather, we computed the percentage variations of alpha rhythms across cognitive tasks with respect to baseline alpha power, namely the alpha ERD. However, effects of resting alpha rhythms were taken into account using the power of baseline alpha rhythm as covariate in the statistical analysis. During the delay period of both NSTM and STM conditions, the alpha ERD peak was later in latency in the demented patients and normal elderly subjects than in the young subjects. Furthermore, the alpha ERD peak was stronger in amplitude in the demented patients than in the normal young and elderly subjects. The lack of statistical alpha ERD differences between the two conditions (STM, NSTM) would indicate that, as for the behavioral performance, the general executive difficulty of the two conditions predominated with respect to the brief and slight memory load of the STM condition. The present alpha ERD results suggest an abnormal amount of the cortical activation (i.e. excitation and/or disinhibition) associated with the executive functions possibly engaged by the delayed response tasks. This agrees with previous functional neuroimaging studies on cognition showing a greater cortical activation in AD patients compared to normal elderly subjects [13,20,21,53,55,111]. In particular, such a cortical hyper-activation has been shown during auditoryverbal memory [13,20,21,111], semantic memory [55], and match-to-sample [53] tasks. The present results agree with previous evidence showing a relationship between cognitive load and alpha band ERD [23,34,35,71,106,108], namely increasing processing resources were related to increasing alpha ERD. Also, alpha ERD has been linked to memory performance [68,72] and to individual’s level of general intelligence [81,82]. More specifically for this research, elderly populations have been characterized by enlarged alpha ERD pattern [30,34,74]. Furthermore, it has been reported cortical hyper-activation in AD patients as revealed by abnormal central EEG rhythms or evoked potentials during sensory stimulation or voluntary movements [7,38]. Finally, hyper-reactivity of primary motor cortex has been observed in AD patients after transcranial magnetic stimulation [1,31,39,76,89].

6. Conclusions Can simple delayed response tasks affect amplitude and latency of midline alpha rhythms (6–12 Hz) in early dementia? Behavioral performances to both NSTM and STM tasks were lower in the AD and VaD patients than in the normal young and elderly subjects. In particular, just four AD and five VaD patients executed a sufficient amount of correct responses for the alpha ERD analysis, so they were included in a unique group. In the two tasks, the alpha ERD peak was later in latency in the demented patients and normal elderly subjects than in the young subjects. Furthermore, the alpha

C. Babiloni et al. / Brain Research Bulletin 65 (2005) 457–470

ERD peak was stronger in amplitude in the demented patients than in the normal subjects. These results suggest that simple delayed response tasks during physiological recordings are quite difficult for patients even at an early dementia stage, so that just about 30% of these patients could be studied from a neurophysiological point of view. Such difficulty may induce the abnormal amplitude of the related cortical activation in demented patients as revealed by the alpha ERD during the delayed response tasks. However, it should be remarked that the slight STM load (just one bit) used in the present study does not represent a useful procedure to specifically assess the neural concomitants of the successful STM processes in early dementia. Acknowledgements We thank Fabio Babiloni, Filippo Carducci, Febo Cincotti, Florinda Ferreri, Flavia Pauri and Alessandro Vasale for their precious help in the development of the present study. We also thank Prof. Fabrizio Eusebi for his continuous support. The study was granted by the Association Fatebenefratelli for Research.

References [1] G. Alagona, R. Bella, R. Ferri, A. Carnemolla, A. Pappalardo, E. Costanzo, G. Pennini, Transcranial magnetic stimulation in Alzheimer disease: motor cortex excitability and cognitive severity, Neurosci. Lett. 314 (2001) 57–60. [2] P.A. Allen, D.J. Madden, T. Weber, L.C. Crozier, Age differences in short-term memory: organization or internal noise? J. Gerontol. 47 (1992) 281–288. [3] E. Arnaiz, O. Almkvist, Neuropsychological features of mild cognitive impairment and preclinical Alzheimer’s disease, Acta Neurol. Scand. 107 (2003) 34–41. [4] C. Babiloni, F. Babiloni, F. Carducci, S. Cappa, F. Cincotti, C. Del Percio, C. Miniussi, D.V. Moretti, S. Rossi, K. Sosta, P.M. Rossini, Human cortical responses during one-bit short-term memory. A high-resolution EEG study on delayed choice 1 reaction time tasks, Clin. Neurophysiol. 115 (2004) 161–170. [5] C. Babiloni, F. Babiloni, F. Carducci, S. Cappa, F. Cincotti, C. Del Percio, C. Miniussi, D.V. Moretti, S. Rossi, K. Sosta, P.M. Rossini, Human cortical rhythms during visual delayed choice reaction time tasks. A high-resolution EEG study on normal aging, Behav. Brain Res. 153 (2004) 261–271. [6] C. Babiloni, F. Babiloni, F. Carducci, F. Cincotti, C. Del Percio, S. Della Penna, R. Franciotti, S. Pignotti, V. Pizzella, P.M. Rossini, E. Sabatini, K. Torquati, G.L. Romani, Human alpha rhythms during visual delayed choice reaction time tasks. A MEG study, Hum. Brain Mapp. 24 (2004) 184–192. [7] C. Babiloni, F. Babiloni, F. Carducci, F. Cincotti, C. Del Percio, G. De Pino, S. Maestrini, A. Priori, P. Tisei, O. Zanetti, P.M. Rossini, Movement-related electroencephalographic reactivity in Alzheimer disease, Neuroimage 12 (2000) 139–146. [8] F. Babiloni, C. Babiloni, L. Fattorini, F. Carducci, P. Onorati, A. Urbano, Performances of surface Laplacian estimators: a study of simulated and real scalp potential distributions, Brain Topogr. 8 (1995) 35–45. [9] C. Babiloni, G. Binetti, E. Cassetta, D. Cerboneschi, G. Dal Forno, C. Del Percio, F. Ferreri, R. Ferri, B. Lanuzza, C. Miniussi, D.V.

[10]

[11]

[12]

[13]

[14] [15] [16]

[17]

[18]

[19]

[20]

[21]

[22]

[23]

[24] [25] [26]

[27]

[28]

467

Moretti, F. Nobili, R.D. Pascual-Marqui, G. Rodriguez, G.L. Romani, S. Salinari, F. Tecchio, P. Vitali, O. Zanetti, F. Zappasodi, P.M. Rossini, Mapping distributed sources of cortical rhythms in mild Alzheimer’s disease. A multicentric EEG study, Neuroimage 22 (2004) 57–67. F. Babiloni, F. Carducci, F. Cincotti, C. Del Gratta, V. Pizzella, G.L. Romani, P.M. Rossini, F. Tecchio, C. Babiloni, Linear inverse source estimate of combined EEG and MEG data related to voluntary movements, Hum. Brain Mapp. 14 (2001) 197–209. C. Babiloni, F. Carducci, F. Cincotti, P.M. Rossini, C. Neuper, G. Pfurtscheller, F. Babiloni, Human movement-related potentials vs desynchronization of EEG alpha rhythm: a high-resolution EEG study, Neuroimage 10 (1999) 658–665. C. Babiloni, C. Miniussi, F. Babiloni, F. Carducci, F. Cincotti, C. Del Percio, G. Sirello, C. Fracassi, A.C. Nobre, P.M. Rossini, Sub-second “temporal attention” modulates alpha rhythms. A highresolution EEG study, Brain Res. Cogn. Brain Res. 19 (2004) 259–268. L. Backman, J.L. Andersson, L. Nyberg, B. Winblad, A. Nordberg, O. Almkvist, Brain regions associated with episodic retrieval in normal aging and Alzheimer’s disease, Neurology 52 (1999) 1861–1870. A. Baddeley, The fractionation of working memory, Proc. Natl. Acad. Sci. U.S.A. 93 (1996) 13468–13472. A. Baddeley, Working memory, CR Acad. Sci. III 321 (1998) 167–173. A. Baddeley, S. Bressi, S. Della Sala, R. Logie, H. Spinnler, The decline of working memory in Alzheimer’s disease. A longitudinal study, Brain 114 (1991) 2521–2542. A. Baddeley, R. Logie, S. Bressi, S. Della Sala, H. Spinnler, Dementia and working memory, Quart. J. Exp. Psychol. A 38 (1986) 603–618. M.C.M. Bastiaansen, D. Posthuma, P.F.C. Groot, E.J.C. De Geus, Event-related alpha and theta responses in a visuo-spatial working memory task, Clin. Neurophysiol. 113 (2002) 1882–1893. K.A. Bayles, Effects of working memory deficits on the communicative functioning of Alzheimer’s dementia patients, J. Commun. Disord. 36 (2003) 209–219. J.T. Becker, M.A. Mintun, K. Aleva, M.B. Wiseman, T. Nichols, S.T. DeKosky, Alteration in functional neuroanatomical connectivity in Alzheimer’s disease: Positron emission tomography of audito verbal short-term memory, Ann. NY Acad. Sci. 777 (1996) 239–242. J.T. Becker, M.A. Mintun, K. Aleva, M.B. Wiseman, T. Nichols, S.T. DeKosky, Compensatory reallocation of brain resources supporting verbal episodic memory in Alzheimer’s disease, Neurology 46 (1996) 692–700. S. Belleville, I. Peretz, D. Malenfant, Examination of the working memory components in normal aging and in dementia of the Alzheimer type, Neuropsychologia 34 (1996) 195–207. F. Boiten, J. Sergeant, R. Geuze, Event-related desynchronization: the effects of energetic and computational demands, Electroenceph. Clin. Neurophysiol. 82 (1992) 302–309. C.H. Brunia, Neural aspects of anticipatory behavior, Acta Psychol. (Amsterdam) 101 (1999) 213–242. E.W. Busse, W.D. Obrist, Significance of focal electroencephalographic changes in the elderly, Postgrad. Med. 34 (1963) 179–182. G.A. Carlesimo, L. Fadda, S. Lorusso, C. Caltagirone, Verbal and spatial memory spans in Alzheimer’s and multi-infarct dementia, Acta Neurol. Scand. 89 (1994) 132–138. G.A. Carlesimo, M. Mauri, A.M. Graceffa, L. Fadda, A. Loasses, S. Lorusso, C. Caltagirone, Memory performances in young, elderly, and very old healthy individuals versus patients with Alzheimer’s disease: evidence for discontinuity between normal and pathological aging, J. Clin. Exp. Neuropsychol. 20 (1998) 14–29. G.G. Celesia, EEG and event-related potentials in aging and dementia, J. Clin. Neurophysiol. 3 (1986) 99–111.

468

C. Babiloni et al. / Brain Research Bulletin 65 (2005) 457–470

[29] W. Christian, The electroencephalogram in advanced age, Nervenarzt 55 (1984) 517–524. [30] P. Derambure, L. Defebvre, K. Dujardin, J.L. Bourriez, J.M. Jacquesson, A. Destee, J.D. Guieu, Effect of aging on the spatiotemporal pattern of event related desynchronization during a voluntary movement, Electroenceph. Clin. Neurophysiol. 89 (1993) 197–203. [31] V. Di Lazzaro, A. Oliviero, P.A. Tonali, C. Marra, A. Daniele, P. Profice, E. Saturno, F. Pilato, C. Masullo, J.C. Rothwell, Noninvasive in vivo assessment of cholinergic cortical circuits in AD using transcranial magnetic stimulation, Neurology 59 (2002) 392–397. [32] A.R. Dobbs, B.G. Rule, Adult age differences in working memory, Psychol. Aging 4 (1989) 500–503. [33] K. Dujardin, J.L. Bourriez, J.D. Guieu, Event-related desynchronization (ERD) patterns during verbal memory tasks: effect of age, Int. J. Psychophysiol. 16 (1994) 17–27. [34] K. Dujardin, J.L. Bourriez, J.D. Guieu, Event-related desynchronization (ERD) patterns during memory processes: effects of aging and task difficulty, Electroenceph. Clin. Neurophysiol. 96 (1995) 169–182. [35] K. Dujardin, P. Derambure, J.L. Bourriez, J.D. Guieu, Eventrelated desynchronization in the functional exploration of cognitive processes, in: G. Pfurtscheller, F.H. Lopes Da Silva (Eds.), Event-Related Desynchronization. Handbook of Electroencephalography and Clinical Neurophysiology, Elsevier, Amsterdam, 1999, pp. 179–193. [36] C.L. Ehlers, D.J. Kupfer, Effects of age on delta and REM sleep parameters, Electroenceph. Clin. Neurophysiol. 72 (1989) 118–125. [37] S. Elmstahl, I. Rosen, Postural hypotension and EEG variables predict cognitive decline: results from a 5-year follow-up of healthy elderly women, Dement. Geriatr. Cogn. Disord. 8 (1997) 180–187. [38] R. Ferri, S. Del Gracco, M. Elia, S.A. Musumeci, R. Spada, M.C. Stefanini, Scalp topographic mapping of middle-latency somatosensory evoked potentials in normal aging and dementia, Neurophysiol. Clin. 26 (1996) 311–319. [39] F. Ferreri, F. Pauri, P. Pasqualetti, F. Fini, G. Dal Forno, P.M. Rossini, Motor cortex excitability in Alzheimer’s Disease: a transcranial magnetic stimulation study, Ann. Neurol. 53 (2003) 102–108. [40] S.R. Filipovic, M. Jahanshahi, J.C. Rothwell, Uncoupling of contingent negative variation and alpha band event-related desynchronization in a go/no-go task, Clin. Neurophysiol. 112 (2001) 1307– 1315. [41] J.E. Fisk, P. Warr, Age and working memory: the role of perceptual speed, the central executive, and the phonological loop, Psychol. Aging 11 (1999) 316–323. [42] C. Flicker, S.H. Ferris, B. Reisberg, A two-year longitudinal study of cognitive function in normal aging and Alzheimer’s disease, J. Am. Geriatr. Soc. 41 (1993) 1029–1032. [43] M.F. Folstein, S.E. Folstein, P.R. McHugh, ‘Mini Mental State’: a practical method for grading the cognitive state of patients for clinician, J. Psychiatr. Res. 12 (1975) 189–198. [44] P.W. Foos, Adult age differences in working memory, Psychol. Aging 4 (1989) 269–275. [45] T. Frodl, H. Hampel, G. Juckel, K. Burger, F. Padberg, R.R. Engel, H.J. Moller, U. Hergerl, Value of event-related P300 subcomponents in the clinical diagnosis of mild cognitive impairment and Alzheimer’s disease, Psychophysiology 39 (2002) 175–181. [46] A. Gevins, M.E. Smith, Neurophysiological measures of working memory and individual differences in cognitive ability and cognitive style, Cereb. Cortex 10 (2000) 829–839. [47] A. Gevins, M.E. Smith, J. Le, H. Leong, J. Bennett, N. Martin, L. McEvoy, R. Du, S. Whitfield, High resolution evoked potential imaging of the cortical dynamics of human working memory, Electroenceph. Clin. Neurophysiol. 98 (1996) 327–348. [48] A. Gevins, M.E. Smith, L. McEvoy, D. Yu, High-resolution EEG mapping of cortical activation related to working memory: effects

[49]

[50]

[51]

[52] [53]

[54]

[55]

[56]

[57]

[58]

[59]

[60]

[61]

[62]

[63]

[64]

[65] [66] [67]

of task difficulty, type of processing, and practice, Cereb. Cortex 7 (1997) 374–385. P.S. Goldman-Rakic, Circuitry of primate prefrontal cortex and regulation of behavior by representational memory, in: V.B. Mountcastle, F. Plum (Eds.), Handbook of Physiology; Nervous System, vol. V: Higher Functions of the Brain, Part 1, American Physiological Society, Bethesda, 1987, pp. 373–417. P.S. Goldman-Rakic, Topography of cognition: parallel distributed networks in primate association cortex, Annu. Rev. Neurosci. 11 (1988) 137–156. P.S. Goldman-Rakic, Prefrontal cortical dysfunctions in schizophrenia: the relevance of working memory, in: B.J. Carroll, J.E. Barrett (Eds.), Psychopatholology and the Brain, Raven Press, New York, 1991, pp. 1–23. P.S. Goldman-Rakic, Cellular basis of working memory, Neuron 14 (1995) 477–485. C.L. Grady, M.L. Furey, P. Pietrini, B. Horwitz, S.I. Rapoport, Altered brain functional connectivity and impaired short-term memory in Alzheimer’s disease, Brain 124 (2001) 739–756. D. Grossi, J.T. Becker, C. Smith, L. Trojano, Memory for visuospatial patterns in Alzheimer’s disease, Psychol. Med. 23 (1993) 65–70. M. Grossman, P. Koenig, G. Glosser, C. DeVita, P. Moore, J. Rhee, J. Detre, D. Alsop, J. Gee, Neural basis for semantic memory difficulty in Alzheimer’s disease: an fMRI study, Brain 126 (2003) 292–311. M. Grunwald, F. Busse, A. Hensel, F. Kruggel, S. Riedel-Heller, H. Wolf, T. Arendt, H.J. Gertz, Correlation between cortical theta activity and hippocampal volumes in health, mild cognitive impairment, and mild dementia, J. Clin. Neurophysiol. 18 (2001) 178–184. M. Grunwald, F. Busse, A. Hensel, F. Kruggel, S. Riedel-Heller, H. Wolf, T. Arendt, H.J. Gertz, Theta-power differences in patients with mild cognitive impairment under rest condition and during haptic tasks, Alzheimer Dis. Assoc. Disord. 16 (2002) 40– 48. R. Hari, N. Forss, Magnetoencephalography in the study of human somatosensory cortical processing, Phil. Trans. R. Soc. London B: Biol. Sci. 354 (1999) 1145–1154. R. Hari, S.L. Joutsiniemi, J. Sarvas, Spatial resolution of neuromagnetic records: theoretical calculations in a spherical model, Electroenceph. Clin. Neurophysiol. 71 (1988) 64–72. R. Hari, R. Salmelin, J.P. Makela, S. Salenius, M. Helle, Magnetoencephalographic cortical rhythms, Int. J. Psychophysiol. 26 (1997) 51–62. C. Huang, L.O. Wahlund, T. Dierks, P. Julin, B. Winblad, V. Jelic, Discrimination of Alzheimer’s disease and mild cognitive impairment by equivalent EEG sources: a cross-sectional and longitudinal study, Clin. Neurophysiol. 11 (2000) 1961– 1967. C.P. Hughes, L. Berg, W.L. Danziger, L.A. Cohen, R.L. Martin, A new clinical rating scale for the staging of dementia, Br. J. Psychiatr. 140 (1982) 1225–1230. V. Jelic, S.E. Johansson, O. Almkvist, M. Shigeta, P. Julin, A. Nordberg, B. Winblad, L.O. Wahlund, Quantitative electroencephalography in mild cognitive impairment: longitudinal changes and possible prediction of Alzheimer’s disease, Neurobiol. Aging 21 (2000) 533–540. V. Jelic, M. Shigeta, P. Julin, Quantitative electroencephalography power and coherence in Alzheimer’s disease and mild cognitive impairment, Dementia 7 (1996) 314–323. R.W. Keefover, Aging and cognition, Neurol. Clin. 16 (1998) 635–648. D.W. Klass, R.P. Brenner, Electroencephalography of the elderly, J. Clin. Neurophysiol. 12 (1995) 116–131. W. Klimesch, Memory processes, brain oscillations and EEG synchronization, Int. J. Psychophysiol. 24 (1996) 61–100.

C. Babiloni et al. / Brain Research Bulletin 65 (2005) 457–470 [68] W. Klimesch, EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis, Brain Res. Rev. 29 (1999) 169–195. [69] W. Klimesch, M. Doppelmayr, T. Pachinger, H. Russegger, Eventrelated desynchronization in the alpha band and the processing of semantic information, Brain Res. Cogn. Brain Res. 6 (1997) 83–94. [70] W. Klimesch, M. Doppelmayr, H. Russegger, T. Pachinger, J. Schwaiger, Induced alpha band power changes in the human EEG and attention, Neurosci. Lett. 244 (1998) 73–76. [71] W. Klimesch, H. Schimke, G. Pfurtscheller, Alpha frequency, cognitive load, and memory performance, Brain Topogr. 5 (1993) 241–251. [72] W. Klimesch, F. Vogt, M. Doppelmayr, Interindividual differences in alpha and theta power reflect memory performance, Intelligence 27 (2000) 347–362. [73] V. Kopruner, G. Pfurtscheller, L.M. Auer, Quantitative EEG in normals and in patients with cerebral ischemia, Prog. Brain Res. 62 (1984) 29–50. [74] E. Labyt, D. Devos, J.L. Bourriez, F. Cassim, A. Deste´e, J.D. Guieu, L. Defebvre, P. Derambure, Motor preparation is more impaired in Parkinson’s disease when sensorimotor integration is involved, Clin. Neurophysiol. 114 (2003) 2423–2433. [75] M.P. Lawton, E.M. Brodie, Assessment of older people: self maintaining and instrumental activity of daily living, J. Gerontol. 9 (1969) 179–186. [76] J. Liepert, K.J. Bar, U. Meske, C. Weiller, Motor cortex disinhibition in Alzheimer’s disease, Clin. Neurophysiol. 112 (2001) 1436–1441. [77] O.N. Markand, Alpha rhythms, J. Clin. Neurophysiol. 7 (1990) 163–189. [78] L.K. McEvoy, E. Pellouchoud, M.E. Smith, A. Gevins, Neurophysiological signals of working memory in normal aging, Brain Res. Cogn. Brain Res. 11 (2001) 363–376. [79] L.K. McEvoy, M.E. Smith, A. Gevins, Dynamic cortical networks of verbal and spatial working memory: effects of memory load and task practice, Cereb. Cortex 8 (1998) 563–574. [80] G. McKhann, D. Drachman, M. Folstein, R. Katzman, D. Price, E.M. Stadlan, Clinical diagnosis of Alzheimer’s disease: report of the NINCDS- ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s disease, Neurology 34 (1984) 939–944. [81] A.C. Neubauer, H.H. Freudenthaler, G. Pfurtscheller, Intelligence and spatio-temporal patterns of event related desynchronization (ERD), Intelligence 20 (1995) 249–266. [82] A.C. Neubauer, R.H. Grabner, H.H. Freudenthaler, J.F. Beckmann, J. Guthke, Intelligence and individual differences in becoming neurally efficient, Acta Psychol. 116 (2004) 55–74. [83] E. Niedermayer, The normal EEG in waking adult, in: E. Niedermayer, F. Lopez da Silva (Eds.), Electroencephalography: Basic Principles, Clinical Application and Related Fields, Williams and Wilkins, Baltimore, 1993, pp. 131–152. [84] W.D. Obrist, The electroencephalogram of normal aged adults, Electroenceph. Clin. Neurophysiol. Suppl. 6 (1954) 235–244. [85] Y.C. Okada, S. Salenius, Roles of attention memory and motor preparation in modulating human brain activity in a spatial working memory task, Cereb. Cortex 8 (1998) 80–96. [86] P. Palladino, R. De Beni, Working memory in aging: maintenance and suppression, Aging 11 (1999) 301–306. [87] F. Pasquier, L. Grymonprez, F. Lebert, M. Van der Linden, Memory impairment differs in frontotemporal dementia and Alzheimer’s disease, Neurocase 7 (2001) 161–171. [88] L. Ppelosi, L.D. Bplumhardt, Effects of age on working memory: an event-related potential study, Cogn. Brain Res. 7 (3) (2001) 321–334. [89] G. Pennisi, G. Alagona, R. Ferri, S. Greco, D. Santonocito, A. Pappalardo, R. Bella, Motor cortex excitability in Alzheimer disease: one year follow-up study, Neurosci. Lett. 329 (3) (2002) 293–296.

469

[90] G. Pfurtscheller, A. Aranibar, Evaluation of event-related desynchronization (ERD) preceding and following voluntary self-paced movement, Electroenceph. Clin. Neurophysiol. 46 (1979) 138– 146. [91] G. Pfurtscheller, W. Klimesch, Functional topography during a visuoverbal judgment task studied with event-related desynchronization mapping, J. Clin. Neurophysiol. 9 (1992) 120–131. [92] G. Pfurtscheller, F. Lopez da Silva, Event-related EEG/MEG synchronization and desynchronization: basic principles, Clin. Neurophysiol. 110 (1999) 1842–1857. [93] G. Pfurtscheller, C. Neuper, Event-related synchronization of mu rhythm in the EEG over the cortical hand area in man, Neurosci. Lett. 174 (1994) 93–96. [94] G. Pfurtscheller, C. Neuper, D. Flotzinger, M. Pregenzer, EEGbased discrimination between imagination of right and left hand movement, Electroenceph. Clin. Neurophysiol. 103 (1997) 642–651. [95] K. Portin, S. Vanni, V. Virsu, R. Hari, Stronger occipital cortical activation to lower than upper visual field stimuli. Neuromagnetic recordings, Exp. Brain Res. 124 (1999) 287–294. [96] G.C. Roman, D.R. Royall, Executive control function: a rational basis for the diagnosis of vascular dementia, Alzheimer Dis. Assoc. Disord. 13 (Suppl. 3) (1999) 69–80. [97] G.C. Roman, T.K. Tatemichi, T. Erkinjuntti, J.L. Cummings, J.C. Masdeu, J.H. Garcia, L. Amaducci, J.M. Orgogozo, A. Brun, A. Hofman, et al., Vascular dementia: diagnostic criteria for research studies. Report of the NINDS-AIREN International Workshop, Neurology 43 (1993) 250–260. [98] W.G. Rosen, R.D. Terry, P.A. Fuld, R. Katzman, A. Peck, Pathological verification of ischemic score in differentiation of dementias, Ann. Neurol. 7 (1980) 486–488. [99] J.B. Rowe, R.E. Passingham, Working memory for location and time: activity in prefrontal area 46 relates to selection rather than maintenance in memory, Neuroimage 14 (2001) 77–86. [100] J.B. Rowe, I. Toni, O. Josephs, R.S. Frackowiak, R.E. Passingham, The prefrontal cortex: response selection or maintenance within working memory, Science 288 (2000) 1656–1660. [101] S. Salenius, M. Kajola, W.L. Thompson, S. Kosslyn, R. Hari, Reactivity of magnetic parieto-occipital alpha rhythm during visual imagery, Electroenceph. Clin. Neurophysiol. 95 (1995) 453– 462. [102] D.P. Salmon, E. Granholm, D. McCullough, N. Butters, I. Grant, Recognition memory span in mildly and moderately demented patients with Alzheimer’s disease, J. Clin. Exp. Neuropsychol. 11 (1989) 429–443. [103] M. Signorino, E. Brizioli, L. Amadio, N. Belardinelli, E. Pucci, F. Angeleri, An EEG power index (eyes open vs. eyes closed) to differentiate Alzheimer’s from vascular dementia and healthy ageing, Arch. Gerontol. Geriatr. 22 (1996) 245–260. [104] L. Stenbacka, S. Vanni, K. Uutela, R. Hari, Comparison of minimum current estimate and dipole modeling in the analysis of simulated activity in the human visual cortices, Neuroimage 16 (2002) 936–943. [105] M. Steriade, R.R Llinas, The functional states of the thalamus and the associated neuronal interplay, Physiol. Rev. 68 (1988) 649– 742. [106] A. Stipacek, R.H. Grabner, C. Neuper, A. Fink, A. Neubauer, Sensitivity of human EEG alpha band desynchronization to different working memory components and increasing levels of memory load, Neurosci. Lett. 353 (2003) 193–196. [107] L. Trojano, L. Chiacchio, G. De Luca, D. Grossi, Exploring visuospatial short-term memory defect in Alzheimer’s disease, J. Clin. Exp. Neuropsychol. 16 (1994) 911–915. [108] W. Van Winsum, J. Sergeant, R. Gueze, The functional significance of event related desynchronization of alpha rhythms in attentional and activating tasks, Electroenceph. Clin. Neurophysiol. 58 (1984) 519–524.

470

C. Babiloni et al. / Brain Research Bulletin 65 (2005) 457–470

[109] R. West, F.I. Craik, Influences on the efficiency of prospective memory in younger and older adults, Psychol. Aging 16 (2001) 682–696. [110] A. Wingfield, E.A. Stine, C.J. Lahar, J.S. Aberdeen, Does the capacity of working memory change with age? Exp. Aging Res. 14 (1988) 103–107. [111] J.L. Woodard, S.T. Grafton, J.R. Votaw, R.C. Green, M.E. Dobraski, J.M. Hoffman, Compensatory recruitment of neural resources during overt rehearsal of word lists in Alzheimer’s disease, Neuropsychology 12 (1998) 491–504. [112] D.S. Woodruff, The Neuropsychology of Aging, Blackwell, Oxford, 1997.

[113] J.A. Yesavage, T.L. Brink, T.L. Rose, O. Lum, V. Huang, M. Adey, V.O. Leirer, Development and validation of a geriatric depression screening scale: a preliminary report, J. Psychiatr. Res. 17 (1983) 37–49. [114] R. Zappoli, A. Versari, M. Paganini, G. Arnetoli, G.C. Muscas, P.F. Gangemi, M.G. Arneodo, D. Poggiolini, F. Zappoli, A. Battaglia, Brain electrical activity (quantitative EEG and bit-mapping neurocognitive CNV components), psychometrics and clinical findings in presenile subjects with initial mild cognitive decline or probable Alzheimer-type dementia, Ital. J. Neurol. Sci. 16 (1995) 341– 376.