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Published Online: 21 March 2022

Neuropsychiatric Symptoms in Dementia: Considering a Clinical Role for Electroencephalography

Publication: The Journal of Neuropsychiatry and Clinical Neurosciences

Abstract

Objective:

Degenerative dementia is characterized by progressive cognitive decline and neuropsychiatric symptoms. People with Alzheimer’s disease (AD), the most common cause of dementia, show synaptic loss and disruption of functional brain networks along with neuritic plaques and neurofibrillary tangles. Electroencephalography (EEG) directly reflects synaptic activity, and among patients with AD it is associated with slowing of background activity. The purpose of this study was to identify associations between neuropsychiatric symptoms and EEG in patients with dementia and to determine whether EEG parameters could be used for clinical assessment of pharmacological treatment of neuropsychiatric symptoms in dementia (NPSD) with galantamine or risperidone.

Methods:

Seventy-two patients with EEG recordings and a score ≥10 on the Neuropsychiatric Inventory (NPI) were included. Clinical assessments included administration of the NPI, the Mini-Mental State Examination (MMSE), and the Cohen-Mansfield Agitation Inventory (CMAI). Patients underwent EEG examinations at baseline and after 12 weeks of treatment with galantamine or risperidone. EEG frequency analysis was performed. Correlations between EEG and assessment scale scores were statistically examined, as were EEG changes from baseline to the week 12 visit and the relationship with NPI, CMAI, and MMSE scores.

Results:

Significant correlations were found between NPI agitation and delta EEG frequencies at baseline and week 12. No other consistent and significant relationships were observed between NPSD and EEG at baseline, after NPSD treatment, or in the change in EEG from baseline to follow-up.

Conclusions:

The limited informative findings in this study suggest that there exists a complex relationship between NPSD and EEG; hence, it is difficult to evaluate and use EEG for clinical assessment of pharmacological NPSD treatment.
Dementia affects 35 million people worldwide (1). Alzheimer’s disease (AD) is the most common cause of dementia (2), and its prevalence is expected to double every 20 years (3). Neuropsychiatric symptoms in dementia (NPSD) consist of an array of behavioral, emotional, and psychological symptoms (4, 5) that affect up to 90% of all patients, irrespective of dementia subtype (5). NPSD, including agitation, depression, and sleep disorders, affects both patient and family (6, 7). NPSD can occur among people with mild dementia, but these symptoms tend to increase with the progression of the neurodegenerative process (8).
The pathogenesis of NPSD is not understood but is believed to be multifactorial, involving neurochemical, neuropathological, and genetic factors such as neurotransmitter function and neurofibrillary tangles (911). Moderate improvements in apathy and social interactions have been reported after treatment with acetylcholinesterase inhibitors (3, 10, 12). Antipsychotic agents have some effect on aggressive behavior and psychotic symptoms (11), but the development of new pharmacological target strategies and therapies against NPSD is urgently needed (1315). Among people with AD, potential targets for intervention are loss of synapses and synapse function (16, 17), which are among the earliest pathophysiological findings (18, 19). Reduced number of synaptic contacts is associated with episodic memory impairment (2023) and disturbed neuronal network function (24).
Electroencephalography (EEG) reflects synaptic activity directly (25). EEG changes, assessed with both basic quantitative analyses and newer network analyses, are present among people with AD. These changes consist of a slowing of background activity (26), in contrast to the steady, fast frequency of activity seen in healthy aging (27). Among patients with AD, EEG slowing has been shown to be correlated with cognitive decline (28) and decreased performance on memory tasks (29, 30). Regions showing a relationship between the measures of activity are considered to show functional connectivity, and changes in EEG functional connectivity have been found among people with AD. Mainly in the faster frequency bands, the coupling between brain regions was found to be lower among people with AD compared with control subjects (31). Among healthy control subjects, patients with mild cognitive impairment, and patients with AD, lower functional connectivity is associated with poorer scores on cognitive tasks such as the Mini-Mental State Examination (MMSE) (3234).
EEG has been described as a reliable tool in dementia research and diagnosis (35). EEG activity is traditionally divided into four frequency bands: delta (from 1 to <4 Hz), theta (from 4 to <8 Hz), alpha (from 8 to 13 Hz), and beta (>13–30 Hz) (36, 37). A slowing of activity in higher frequencies (alpha and beta bands) and an increase of activity in lower frequencies (delta and theta bands) are often seen among patients with AD (37). The application of EEG is of specific interest with people with AD because it contributes to differential diagnosis and prognosis of disease progression (35).
EEG changes vary among different dementia types; among people with mild AD and normally aging people compared with those with vascular dementia (VaD), there is reduction in magnitude of central, parietal, temporal, and limbic alpha 1 sources (8–10.5 Hz) (38). Distributed theta sources are largely abnormal among individuals with VaD but not among those with mild AD (38).
EEG might add important information related to drug effectiveness (35). Among patients with AD, successful pharmacological treatment with acetylcholinesterase inhibitors improves cognitive functions slightly and affects resting state EEG rhythms (39), with a decrease in delta or theta rhythms and an increase in dominant alpha rhythms (39). One study did not find any significant pharmaco-EEG effects from risperidone among healthy individuals (40). In another study of patients with schizophrenia, risperidone treatment induced widespread changes in interhemispheric power asymmetry (41). Overall clinical improvement was related to absolute power changes in the beta frequency band and power asymmetry in the theta and delta bands; these relationships were most expressed in the anterior areas (41).
It is known that neuropsychiatric symptoms are present in the early stages of dementia and that the NPSD spectrum worsens with disease progression (42). Data on the possible relationship between NPSD and EEG are scarce. Such relationships might be used for prediction of specific NPSD burden and for tailoring patient-specific interventions, both pharmacological and nonpharmacological. The aim of this study was to identify possible associations between NPSD (assessed with the Neuropsychiatric Inventory [NPI]) and EEG among patients with dementia and to determine whether EEG parameters could be used for clinical assessment of pharmacological treatment of NPSD with galantamine or risperidone.

Methods

Patients and Study Design

Patients were recruited from a clinical randomized controlled trial conducted at the Memory Clinic of the Department of Geriatric Medicine at Karolinska University Hospital, Stockholm, between January 2003 and September 2005. This trial was originally designed to compare the efficacy of galantamine and risperidone in treatment for NPSD of patients with probable dementia (12). For inclusion, patients were required to have probable dementia or mild cognitive impairment according to DSM-IV criteria (43) as well as NPSD, defined as an NPI (44) score greater than or equal to 10, where symptoms had been present for a minimum of 2 weeks upon assessment. All patients lived in their own homes, and their respective caregivers provided information about their history. Patients were excluded if they had a diagnosis of schizophrenia or another psychiatric disorder or a history of seizures, active peptic ulcer, or clinically significant hepatic, renal, or metabolic disturbances or if they did not have a caregiver.
One hundred forty-five patients were screened; of these, 100 were considered for participation in the study. Fifty percent of participants received treatment with galantamine; the other 50% received treatment with risperidone. EEG examinations were performed at baseline, before treatment (recording 1), and after 12 weeks of treatment (recording 2). After the baseline recording, 28 patients were excluded from further EEG analysis because they were unable to keep awake (even for shorter periods) during the recording or because of unmanageable excessive muscle artifacts contaminating the EEG. For similar reasons, further exclusions were made after recording 2. When comparing the recordings, we analyzed data from only 65 patients who had satisfactory recordings on both occasions. The study was conducted according to good clinical practices and the ethical principles of the Declaration of Helsinki. Both patients and caregivers gave written informed consent before enrolment. The regional ethics committee at the Karolinska Institutet in Stockholm approved the study.

Clinical Assessment

Somatic, neurological, and psychiatric examinations were assessed by a specialist in geriatric psychiatry. Clinical parameters and blood, cerebral spinal fluid (CSF), and urine samples were collected, and brain imaging (computerized tomography) was performed. Cognition was rated using the MMSE (45). The Clinical Dementia Rating (CDR) scale (46) was used to evaluate global cognitive functioning.
NPSD were rated using the NPI (44). The NPI consists of 12 domains (each domain score ranges from 0 to 12, based on the product of the symptom’s frequency [0–4] and severity [0–3] scores, with a maximum total score of 144 points) rated by the caregiver, on which a high score indicates a high load of NPSD (44). Agitation was further assessed with the Cohen-Mansfield Agitation Inventory (CMAI) (47), which consists of 29 items related to agitation. Items are rated from 1 to 7 on the basis of frequency. The sum of the frequency scores renders the total CMAI score (ranging from 29 to 203 points). Moreover, the total CMAI score was divided into four domains: physical aggressive behavior, physical nonaggressive behavior, verbal aggressive behavior, and verbal nonaggressive behavior.

EEG Recording and Analysis

EEG was recorded with Nervus (Natus Nicolet NicOne), with a sampling frequency of 256 Hz, at baseline and 12 weeks after initiation of drug therapy. The 21-channel recordings were performed with surface electrodes placed according to the international 10–20 system (48). The reference electrode was positioned between Pz and Cz, and the signals were digitally filtered between 0.5 Hz and 70 Hz. Patients were comfortably seated in a reclining chair, and wakefulness was monitored by the technician, who tried to keep the patient relaxed and awake.
Although many patients were excluded, as described earlier, prominent artifacts from eye movements, muscle activity, or both in frontal and temporal leads were common, and further analysis of EEG was restricted to the central and posterior leads (electrodes at C3, P3, T5, and O1 [left side] and C4, P4, T6, and O2 [right side]).
Between two and five 10-second epochs of EEG free from artifacts, with the subjects as awake as possible and with their eyes closed, were selected for Fast Fourier transform analysis. Overlapping 2-second blocks were analyzed with a Hamming window and a frequency resolution of 0.25 Hz. Epochs did not include the first 4 seconds after periods of eye openings. Mean values of absolute and relative power were calculated for each frequency band (delta, theta, alpha, and beta) for each electrode. The average power for all electrodes was calculated for each frequency band and also separately for the group of electrodes on the left side and on the right side. For statistical correlations, the relative power for the various frequency bands was used.
Visual inspection of the recordings was carried out by an experienced specialist in clinical neurophysiology, who graded abnormalities of background activity on a scale ranging from 0 to 3 (none, mild, moderate, severe) with respect to slowing of background activity. The frequency of the posterior dominant activity as well as the amount of other slow activity was considered, and abnormalities were grouped into slight slowing of posterior dominant activity and slight increase of slow activity, mainly theta activity (mild); posterior-dominant activity 6–7 Hz, clear increase of theta and delta activity (moderate), or both; and posterior dominant activity ≤6 Hz, high amount of delta activity (severe), or both.
Moreover, recordings at baseline were compared with those made after 12 weeks (for 65 patients) with respect to absolute and relative power for the different frequency bands as well as mean frequency.

Statistical Analysis

Basic descriptive statistics were calculated for all variables. The clinical outcome scores did not follow a normal distribution; hence, we used the nonparametric Spearman’s rank-order correlation test to statistically examine correlations.
Correlations between EEG mean power and MMSE, CDR, NPI, and CMAI scores were statistically examined using Spearman’s rank-order correlation coefficient. EEG changes from baseline to week 12 visit and their relation to the parameters described previously were explored in the same manner. Subgroup analyses were performed for responder and nonresponder patients and by the two treatments. A responder was defined as a subject for whom NPI score at follow-up divided by NPI score at baseline was less than 70%; otherwise, the participant was considered a nonresponder. No adjustments were made for multiple comparisons in this exploratory study. A p<0.05 was considered statistically significant, and R version 3.6.1 was used for all statistical analyses.

Results

Of 100 patients, 65 had complete information on the variables of interest for the purposes of the comparative study between recordings 1 and 2. These 65 patients were diagnosed with AD (31%), mixed AD (28%), VaD (19%), mild cognitive impairment (14%), frontotemporal dementia (5%), unspecified dementia (3%), and Parkinson’s disease with dementia (2%). Of these patients, 63% were female, and the mean age at enrollment was 78.1 years (SD=7.7). One-fifth of the patients had a family history of dementia; 54% were randomized to galantamine, and 46% were randomized to risperidone. Patients’ demographic data are summarized in Table 1.
TABLE 1. Demographic and clinical characteristics of patients with neuropsychiatric symptoms and probable dementia (N=65)
CharacteristicN%
Sex  
 Female4163.1
 Male2436.9
Diagnosis at baseline  
 Alzheimer’s disease2030.8
 Frontotemporal dementia34.6
 Mixed Alzheimer’s disease1827.7
 Mild cognitive impairment913.8
 Parkinson’s dementia11.5
 Unspecified dementia23.1
 Vascular dementia1218.5
Mean age at diagnosis (years) (mean±SD)78.17.7
Heredity  
 Yes1320
 No5280
Treatment for neuropsychiatric symptoms  
 Galantamine3553.8
 Risperidone3046.2
At baseline, visual inspection of the EEG (N=72) before further exclusion revealed normal activity among 22 patients. Mild abnormalities (slowing of background activity) were found among 26 patients, moderate abnormalities among 21 patients, and severe abnormalities among three patients.
EEG findings and correlations with clinical data for the average of all electrodes are presented in Tables 2–5.
TABLE 2. Clinical outcomes, outcomes from the EEG evaluation, and correlations between clinical outcomes and EEG analysis at baseline among patients with neuropsychiatric symptoms and probable dementiaa
 EEG at baseline
 αβδθ
VariableNMeanrprprprp
Mini-Mental State Examination6521.0–0.120.35–0.210.090.080.520.010.95
Clinical Dementia Rating Scale657.90.060.640.040.760.030.84–0.050.68
Neuropsychiatric Inventory          
 Total6552.6–0.090.48–0.090.480.250.04–0.020.88
 Delusions654.7–0.160.200.220.070.210.10–0.060.62
 Hallucinations653.2–0.180.15–0.050.710.180.150.140.28
 Agitation654.2–0.100.42–0.140.270.260.04–0.110.39
 Depression655.5–0.060.660.010.940.160.190.000.99
 Anxiety655.90.080.550.001.000.070.58–0.080.51
 Euphoria650.60.030.80–0.190.140.020.890.090.46
 Apathy658.1–0.080.55–0.120.320.210.09–0.020.87
 Disinhibition653.1–0.140.28–0.130.300.190.140.030.84
 Irritability654.4–0.150.22–0.160.220.270.03–0.020.86
 Aberrant motor behavior654.20.050.67–0.050.670.030.78–0.100.45
 Nighttime behavior654.60.020.90–0.080.520.010.930.070.56
 Appetite654.00.090.490.010.94–0.120.340.030.82
Cohen-Mansfield Agitation Inventory          
 Total6546.50.010.91–0.060.650.180.15–0.190.13
 Physical aggressive behavior6511.8–0.220.08–0.190.130.270.030.050.70
 Physical nonaggressive behavior6516.80.060.650.080.540.110.38–0.310.01
 Verbal aggressive behavior655.0–0.200.11–0.250.040.330.010.070.56
 Verbal nonaggressive behavior6512.9–0.020.89–0.060.620.170.17–0.120.35
EEG at baseline          
 Alpha650.32
 Beta650.14
 Delta650.24
 Theta650.30
a
Spearman’s correlation coefficients between clinical outcomes and EEG outcomes are presented. Bold denotes statistically significant correlation.
TABLE 3. Correlations between clinical and EEG outcomes at follow-up among patients with neuropsychiatric symptoms and probable dementiaa
 EEG at follow-up
 αβδθ
VariableNMeanrprprprp
Mini-Mental State Examination6522.9–0.130.32–0.220.080.090.480.160.20
Clinical Dementia Rating scale657.20.140.280.110.39–0.120.36–0.110.40
Neuropsychiatric Inventory          
 Total6515.50.070.550.070.610.040.730.020.85
 Delusions651.50.000.980.200.110.050.690.030.82
 Hallucinations650.20.000.97–0.140.260.080.530.160.19
 Agitation650.6–0.150.23–0.120.360.250.040.140.26
 Depression651.80.090.450.260.03–0.070.57–0.090.49
 Anxiety651.40.150.230.140.26–0.090.460.010.95
 Euphoria650.2–0.160.21–0.190.130.120.330.250.05
 Apathy654.80.130.320.020.880.070.58–0.100.42
 Disinhibition650.7–0.100.44–0.110.390.060.660.310.01
 Irritability650.9–0.020.880.030.830.010.950.140.27
 Aberrant motor behavior651.10.040.77–0.130.320.060.630.030.79
 Nighttime behavior650.80.060.62–0.100.440.030.830.110.38
 Appetite651.6–0.110.38–0.140.280.140.280.070.57
Cohen-Mansfield Agitation Inventory          
 Total6540.2–0.040.76–0.010.950.120.360.000.98
 Physical aggressive behavior6511.20.040.78–0.120.350.120.340.100.43
 Physical nonaggressive behavior6514.6–0.050.710.040.730.170.17–0.070.57
 Verbal aggressive behavior653.8–0.180.15–0.100.430.250.050.120.33
 Verbal nonaggressive behavior6510.60.000.97–0.060.660.050.710.060.65
EEG at follow-up          
 Alpha650.31
 Beta650.15
 Delta650.25
 Theta650.29
a
Spearman’s correlation coefficients between clinical outcomes and EEG outcomes are presented. Bold denotes statistically significant correlation.
TABLE 4. Spearman’s correlation for changes at baseline and follow-up between Neuropsychiatric Inventory (NPI) domains and EEG activities in total and by responder and nonresponder after treatment of neuropsychiatric symptomsa
 EEG
 αβδθ
Variablerprprprp
NPI total–0.020.900.000.980.050.67–0.070.56
 Responder–0.060.64–0.010.920.110.41–0.100.44
 Nonresponder
Delusions–0.160.21–0.100.440.250.04–0.110.39
 Responder–0.150.390.010.950.380.02–0.260.12
 Nonresponder0.030.87–0.090.66–0.130.530.230.25
Hallucinations–0.200.12–0.120.350.160.190.040.74
 Responder0.250.20–0.170.39–0.060.760.020.92
 Nonresponder
Agitation0.020.89–0.060.640.090.49–0.150.24
 Responder–0.040.830.010.970.190.26–0.220.18
 Nonresponder
Depression–0.180.160.160.200.030.830.140.27
 Responder–0.100.490.100.52–0.080.610.200.19
 Nonresponder–0.110.650.030.910.080.730.050.82
Anxiety–0.020.900.160.21–0.040.75–0.100.42
 Responder–0.060.690.070.650.020.91–0.050.76
 Nonresponder0.090.710.450.05–0.190.43–0.190.42
Euphoria0.060.64–0.170.18–0.040.770.090.45
 Responder0.380.46–0.460.350.230.66–0.230.66
 Nonresponder–0.170.20–0.150.250.120.370.130.33
Apathy0.080.52–0.130.32–0.070.570.090.49
 Responder–0.170.32–0.250.140.080.640.150.38
 Nonresponder0.120.53–0.160.410.050.800.040.84
Disinhibition0.020.88–0.080.530.080.52–0.170.18
 Responder0.110.59–0.050.79–0.030.88–0.060.75
 Nonresponder0.110.500.120.47–0.160.35–0.050.75
Irritability–0.020.85–0.040.780.040.76–0.020.87
 Responder–0.050.750.010.950.060.72–0.090.58
 Nonresponder0.300.120.150.43–0.200.30–0.300.12
Aberrant motor behavior0.050.70–0.030.790.150.23–0.150.24
 Responder0.190.330.130.50–0.040.84–0.210.26
 Nonresponder–0.110.520.020.910.170.32–0.020.91
Nighttime behavior–0.130.31–0.090.490.120.350.100.41
 Responder0.020.890.050.780.050.75–0.120.46
 Nonresponder0.130.53–0.280.170.070.72–0.090.65
Appetite0.020.850.050.67–0.020.870.010.94
 Responder0.140.46–0.030.86–0.100.62–0.020.93
 Nonresponder0.020.91–0.170.330.050.770.120.48
a
Spearman’s correlation coefficients between clinical outcomes and EEG outcomes are presented. Bold denotes statistically significant correlation.
TABLE 5. Mean changes in Neuropsychiatric Inventory (NPI) score and EEG outcomes from baseline to follow-up in total and by responder and nonresponder after treatment of neuropsychiatric symptomsa
 NPIEEGEEG
 Meanαβδθ
VariableNBaselineFollow-uppBaselineFollow-uppBaselineFollow-uppBaselineFollow-uppBaselineFollow-upp
NPI total6552.615.5<0.0010.320.310.470.140.150.110.240.250.690.300.290.40
 Responder6152.413.3<0.0010.320.310.330.140.160.160.240.250.520.300.290.39
 Nonresponder455.048.30.2940.320.340.880.090.110.630.260.220.380.330.331.00
Delusions654.71.5<0.001
 Responder377.00.9<0.0010.290.290.890.160.180.110.260.250.550.290.280.61
 Nonresponder281.82.10.0700.370.340.240.110.120.660.210.250.200.310.300.48
Hallucinations653.20.2<0.001
 Responder287.30.3<0.0010.270.280.340.140.160.110.280.260.450.320.300.37
 Nonresponder370.10.10.370.330.060.140.150.460.210.240.200.280.280.71
Agitation654.20.6<0.001
 Responder396.90.8<0.0010.310.300.490.130.150.100.270.270.940.290.280.72
 Nonresponder260.20.20.340.320.720.150.160.470.200.220.710.310.300.50
Depression655.51.8<0.001
 Responder456.81.4<0.0010.300.300.970.140.150.500.260.260.990.300.290.38
 Nonresponder202.62.80.5410.380.330.170.130.160.070.210.230.530.280.280.79
Anxiety655.91.4<0.001
 Responder457.20.5<0.0010.320.310.530.140.150.340.240.250.590.300.300.80
 Nonresponder203.13.40.3670.330.310.780.130.160.150.250.260.860.290.270.23
Euphoria650.60.20.048
 Responder66.00.30.0040.350.290.310.080.130.310.260.300.440.310.280.56
 Nonresponder590.10.10.4720.320.310.730.140.160.200.240.240.900.300.290.48
Apathy658.14.8<0.001
 Responder358.82.6<0.0010.310.290.210.130.150.150.250.270.270.300.290.40
 Nonresponder307.27.30.7450.330.330.820.140.150.460.230.230.600.290.290.79
Disinhibition653.10.7<0.001
 Responder276.71.0<0.0010.310.300.660.130.150.100.260.260.590.290.300.60
 Nonresponder380.60.50.3240.330.320.470.140.160.490.230.240.340.300.280.18
Irritability654.40.9<0.001
 Responder377.51.0<0.0010.300.290.650.130.150.110.270.270.910.300.280.41
 Nonresponder280.50.80.3260.350.330.530.140.150.540.210.220.690.300.300.81
Aberrant motor behavior654.21.1<0.001
 Responder308.41.4<0.0010.320.310.560.130.160.130.280.270.520.270.270.86
 Nonresponder350.60.90.0830.330.310.590.140.150.350.210.230.280.320.310.36
Nighttime behavior654.60.8<0.001
 Responder397.10.8<0.0010.320.320.810.130.150.090.230.230.760.320.300.20
 Nonresponder260.80.81.0000.330.290.130.150.160.630.260.280.390.260.270.76
Appetite654.01.6<0.001
 Responder297.21.0<0.0010.330.320.570.130.130.780.210.220.780.330.330.95
 Nonresponder361.42.20.0410.320.300.530.150.170.090.260.270.740.270.260.34
a
Bold denotes statistically significant mean change.
Clinical outcomes, outcomes from the EEG evaluation, and correlations between the clinical outcomes and EEG analysis at baseline are presented in Table 2. No statistically significant correlations were observed between MMSE, CDR, NPI, and CMAI scores and the alpha EEG level at baseline. A negative correlation was observed for the CMAI domain of verbal aggressive behavior and beta activity. A significant positive correlation was observed between NPI total score, agitation, and irritability; CMAI physical and verbal aggressive behavior; and delta activity. Only physical nonaggressive behavior had a significant correlation to theta activity. At follow-up, only the correlations between NPI agitation, CMAI verbal aggressive behavior, and delta activity persisted (Table 3). The weak correlations between NPI total and delta activity, irritability and delta activity, and physical aggressive behavior and delta activity found at baseline were no longer observed at follow-up. In addition, weak correlations were found at follow-up between depression and beta activity, between euphoria and theta activity, and between disinhibition and theta activity. The significant correlation of agitation with EEG delta frequencies is reproducibly present at baseline and at follow-up for the NPI agitation domain score and some factors of the CMAI.
Table 4 shows the changes in correlations from baseline to follow-up for the NPI domains and the EEG activities in total and by responders and nonresponders after treatment of NPSD. A significant positive correlation was observed only for NPI delusions and delta activity, which was also true for delusions among patients who were defined as responders, but not for those among patients defined as nonresponders. However, among nonresponders, a positive correlation was observed for NPI anxiety and beta activity.
Table 5 describes the mean changes in scores on the NPI domains between baseline and follow-up and the relative power for the various frequency bands of the EEG at baseline and at follow-up in total and by responders and nonresponders after treatment of NPSD. For all NPI domains, a positive statistically significant result was observed over time only in the responder group, except for NPI appetite scores, for which nonresponder patients also had a significant change over time. The same pattern was observed for patients prescribed galantamine and those prescribed risperidone (data not shown). For EEG, very similar power values were found at baseline and follow-up. In some cases, tendencies to increase or decrease existed, but none of the mean changes in EEG activities from baseline to follow-up were significant. For those items for which a weak correlation with the power in a frequency band was found only at baseline or only at follow-up, a (nonsignificant) tendency to change in power was noted for only three combinations (NPI total and delta activity, NPI depression and beta activity, and NPI irritability and delta activity), but the changes were seen only for nonresponders. All NPI domains except NPI total score were significantly different between responders and nonresponders at baseline, a finding that was not observed among the EEG activities at baseline (data not shown).
When EEGs from left and right sides were analyzed separately, the findings were consistent with the results described here.

Discussion

This study is among the few to evaluate possible relationships between NPSD, EEG, and pharmacological treatment with galantamine or risperidone for NPSD. Although there were clinical improvements, the power spectrum of EEG was mainly unaltered, and therefore the few significant changes in EEG relative power should be interpreted critically, given the large number of comparisons. Overall, there were scarce, and vague, relations between NPSD scores and EEG power at baseline, as well as after treatment. The significant correlation of agitation with EEG delta frequencies present at baseline and at week 12 on the NPI agitation domain score and some factors of the CMAI warrants further investigation.
Patients were mainly included in the study on the basis of NPSD and cognitive level, and they were diverse, with several different diagnoses represented. It is possible that differences exist in disease expression and severity among patients (42). The diversity of the study group is also reflected in the various EEG outcomes at baseline, with recordings ranging from normal to severely abnormal. However, looking at dementia symptom severity using the CDR Scale, patients obtained a similar, rather low, CDR Scale score (1.4 [SD=0.6]), suggesting a fairly comparable cohort in this respect. The study population comprised patients experiencing high NPSD burden (mean NPI total score=54 [SD=25]). Because NPSD such as delusions, hallucinations, and aggression are known to increase with disease progression, relationships with EEG would most likely be more evident with higher NPSD burden and more severely affected patients (42).
Although great care was taken to exclude (parts of) recordings in which the patients were drowsy, variations in wakefulness could not be ruled out as a confounding factor. In this study (originally conducted as a randomized controlled trial), patients also received other drugs apart from the study drugs. This might have contributed to the EEG changes, either directly or indirectly, by inducing drowsiness on EEG and may therefore have made interpretations and proper data collection more difficult. However, EEG results showed enough similarity between baseline and follow-up to make this an unlikely main factor.
Our study also has other potential sources of error that should be taken into account. NPSD fluctuate in severity over time (4). The evaluation of NPSD and analysis of EEG were limited to two definite occasions, thus representing only a brief moment in these dynamic relationships. However, because the EEG recordings and the assessment of the scales evaluating NPSD were temporally performed in connection with each other, our study should present a reliable picture of the cross-sectional relationship between NPSD and EEG.
Several EEG recordings were excluded from analysis because of insufficient quality (excessive muscle artifacts) or inability to maintain adequate wakefulness, which made the diagnostic subgroups of this study smaller. Hence, the findings mainly apply to patients with dementia experiencing a high burden of NPSD. The results are less applicable to patients with less severe NPSD and patients in the study’s smaller diagnostic subgroups.
In summary, although treatment resulted in improvement in NPSD, significant treatment effects were not shown in EEG. Moreover, we found no significant conclusive relationships, apart from vague or inconsistent ones, between NPSD and EEG at baseline or after NPSD treatment. However, the study group included patients with several distinct disorders and with both normal EEG recordings as well as recordings exhibiting various degrees of abnormality; thus, it is possible that some effects of treatment in EEG were obscured by overall changes from the underlying disease or other pharmacological treatment.
Further investigation is needed. Although in this study we analyzed EEG via visual analysis and spectral band measurements, next steps, along with increasing the sample size, include more analytic approaches. A large number of features are now used that can better analyze slowing, complexity, and synchronization of EEG activity among patients with dementia (49). An additional avenue for analysis would be the creation of deep learning models trained from the data set. Although this approach reveals little about the actual differences between states, if a model is created that distinguishes the two states in both the training and the validation sets, the distinction could be proven to exist. This could be the evidence to spur further research.

Conclusions

Our study’s lack of robust findings implicates a complex relationship between NPSD and EEG, hence making it difficult to evaluate and use EEG in a clinical setting for assessing pharmacological NPSD treatment. However, because this was a hypothesis-generating study with a relatively small cohort, more studies are needed to explore these relationships and should include other measures of EEG activity, such as coherence. Moreover, the inclusion of blood and CSF levels of galantamine and risperidone in future studies would enable more precise pharmaco-EEG studies. Tools for the development and assessment of individualized treatments for NPSD are urgently needed.

Acknowledgments

The authors thank the patients and their caregivers and the staff at the outpatient memory clinic and inpatient wards at the Geriatric Clinic, Karolinska University Hospital. The authors also thank Jonas Selling (Statsoft) for providing statistical software and advice on statistical analysis, as well as Marie Lärksäter, R.N., and Ann Christine Tysen-Bäckström, R.N., for clinical aid during the study.

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Information & Authors

Information

Published In

Go to The Journal of Neuropsychiatry and Clinical Neurosciences
Go to The Journal of Neuropsychiatry and Clinical Neurosciences
The Journal of Neuropsychiatry and Clinical Neurosciences
Pages: 214 - 223
PubMed: 35306829

History

Revision received: 29 July 2021
Received: 14 September 2021
Revision received: 14 September 2021
Accepted: 20 September 2021
Published online: 21 March 2022
Published in print: Summer 2022

Keywords

  1. Alzheimer’s disease
  2. Neuropsychiatric Symptoms in Dementia
  3. Pharmacological Treatment
  4. EEG

Authors

Details

Simon Holmgren, M.D.
Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Division of Clinical Geriatrics, Karolinska Institutet, Stockholm (Holmgren, Aarsland, Freund-Levi); Department of Neurophysiology, Karolinska University Hospital, Huddinge, Sweden (Andersson); Department of Clinical Neuroscience, Karolinska Institutet, Stockholm (Berglund); Institute of Psychiatry, Psychology and Neuroscience, Division of Old Age Psychiatry, Kings College London (Aarsland, Freund-Levi); Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway (Aarsland); Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Science, University of Nevada, Las Vegas (Cummings); Department of Psychiatry and Geriatrics, University Hospital Örebro, Sweden (Freund-Levi); and School of Medical Sciences, Örebro University, Sweden (Freund-Levi).
Thomas Andersson, M.D., Ph.D.
Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Division of Clinical Geriatrics, Karolinska Institutet, Stockholm (Holmgren, Aarsland, Freund-Levi); Department of Neurophysiology, Karolinska University Hospital, Huddinge, Sweden (Andersson); Department of Clinical Neuroscience, Karolinska Institutet, Stockholm (Berglund); Institute of Psychiatry, Psychology and Neuroscience, Division of Old Age Psychiatry, Kings College London (Aarsland, Freund-Levi); Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway (Aarsland); Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Science, University of Nevada, Las Vegas (Cummings); Department of Psychiatry and Geriatrics, University Hospital Örebro, Sweden (Freund-Levi); and School of Medical Sciences, Örebro University, Sweden (Freund-Levi).
Anders Berglund, Ph.D.
Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Division of Clinical Geriatrics, Karolinska Institutet, Stockholm (Holmgren, Aarsland, Freund-Levi); Department of Neurophysiology, Karolinska University Hospital, Huddinge, Sweden (Andersson); Department of Clinical Neuroscience, Karolinska Institutet, Stockholm (Berglund); Institute of Psychiatry, Psychology and Neuroscience, Division of Old Age Psychiatry, Kings College London (Aarsland, Freund-Levi); Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway (Aarsland); Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Science, University of Nevada, Las Vegas (Cummings); Department of Psychiatry and Geriatrics, University Hospital Örebro, Sweden (Freund-Levi); and School of Medical Sciences, Örebro University, Sweden (Freund-Levi).
Dag Aarsland, M.D., Ph.D.
Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Division of Clinical Geriatrics, Karolinska Institutet, Stockholm (Holmgren, Aarsland, Freund-Levi); Department of Neurophysiology, Karolinska University Hospital, Huddinge, Sweden (Andersson); Department of Clinical Neuroscience, Karolinska Institutet, Stockholm (Berglund); Institute of Psychiatry, Psychology and Neuroscience, Division of Old Age Psychiatry, Kings College London (Aarsland, Freund-Levi); Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway (Aarsland); Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Science, University of Nevada, Las Vegas (Cummings); Department of Psychiatry and Geriatrics, University Hospital Örebro, Sweden (Freund-Levi); and School of Medical Sciences, Örebro University, Sweden (Freund-Levi).
Jeffrey Cummings, M.D., Sc.D.
Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Division of Clinical Geriatrics, Karolinska Institutet, Stockholm (Holmgren, Aarsland, Freund-Levi); Department of Neurophysiology, Karolinska University Hospital, Huddinge, Sweden (Andersson); Department of Clinical Neuroscience, Karolinska Institutet, Stockholm (Berglund); Institute of Psychiatry, Psychology and Neuroscience, Division of Old Age Psychiatry, Kings College London (Aarsland, Freund-Levi); Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway (Aarsland); Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Science, University of Nevada, Las Vegas (Cummings); Department of Psychiatry and Geriatrics, University Hospital Örebro, Sweden (Freund-Levi); and School of Medical Sciences, Örebro University, Sweden (Freund-Levi).
Yvonne Freund-Levi, M.D., Ph.D. [email protected]
Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Division of Clinical Geriatrics, Karolinska Institutet, Stockholm (Holmgren, Aarsland, Freund-Levi); Department of Neurophysiology, Karolinska University Hospital, Huddinge, Sweden (Andersson); Department of Clinical Neuroscience, Karolinska Institutet, Stockholm (Berglund); Institute of Psychiatry, Psychology and Neuroscience, Division of Old Age Psychiatry, Kings College London (Aarsland, Freund-Levi); Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway (Aarsland); Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Science, University of Nevada, Las Vegas (Cummings); Department of Psychiatry and Geriatrics, University Hospital Örebro, Sweden (Freund-Levi); and School of Medical Sciences, Örebro University, Sweden (Freund-Levi).

Notes

Send correspondence to Dr. Freund-Levi ([email protected]).
Trial registration: Karolinska University Hospital identifier study ID 20100039.

Competing Interests

Janssen Pharmaceuticals and the funding foundations were not involved in patient or data collection, and they had no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the study; the decision to submit the article for publication; or in review and approval of the study.

Funding Information

This study is an academically initiated study financed by an independent grant from Janssen Pharmaceuticals, Stockholm. This work was also supported by grants from the Petrus and Augusta Hedlunds Foundation, the Gun och Bertil Stohnes Foundation, the Loo and Hans Osterman Foundation, the Demensförbundet, Brain Foundation “Hjärnfonden” (grant FO2018-0315), “Särfond 31 Forskning Senil demens,” Region Örebro län, “Stiftelsen for Gamla Tjänarinnor,” and Demensfonden, Stockholm.Dr. Cummings owns the copyright to the Neuropsychiatric Inventory. All other authors report no financial relationships with commercial interests.

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