Ageing Research Reviews 72 (2021) 101499
Contents lists available at ScienceDirect
Ageing Research Reviews
journal homepage: www.elsevier.com/locate/arr
Review
Efficacy of non-invasive brain stimulation on global cognition and
neuropsychiatric symptoms in Alzheimer’s disease and mild cognitive
impairment: A meta-analysis and systematic review
Johannes Teselink a, b, 1, Kritleen K. Bawa a, b, c, 1, Grace KY Koo a, b, c, Krushnaa Sankhe a, b, Celina
S. Liu a, b, c, Mark Rapoport d, Paul Oh e, Susan Marzolini e, f, Damien Gallagher a, d,
Walter Swardfager b, c, e, f, Nathan Herrmann a, b, d, Krista L. Lanctôt a, b, c, d, e, f, *
a
Neuropsychopharmacology Research Group, Sunnybrook Research Institute, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada
Hurvitz Brain Sciences Program, Sunnybrook Research Institute, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada
c
Department of Pharmacology & Toxicology, University of Toronto, 1 King’s College Circle, Toronto, ON, M5S 1A8, Canada
d
Department of Psychiatry, Faculty of Medicine, University of Toronto, 250 College Street, 8th floor, Toronto, ON, M5T 1R8, Canada
e
KITE Toronto Rehabilitation Institute, University Health Network, 347 Rumsey Rd, East York, ON, M4G 2V6, Canada
f
Heart and Stroke Foundation Canadian Partnership for Stroke Recovery, Sunnybrook Research Institute, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada
b
A R T I C L E I N F O
A B S T R A C T
Keywords:
Cognitive impairment
Non-invasive brain stimulation
Repetitive transcranial magnetic stimulation
Transcranial direct current stimulation
Neuropsychiatric symptoms
Alzheimer’s disease
Background: Non-invasive brain stimulation (NIBS) techniques have shown some promise in improving cognitive
and neuropsychiatric symptoms (NPS) in people with Alzheimer’s disease (AD) and its prodromal stage, mild
cognitive impairment (MCI). However, data from clinical trials involving NIBS have shown inconsistent results.
This meta-analysis investigated the efficacy of NIBS, specifically repetitive transcranial magnetic stimulation
(rTMS), and transcranial direct current stimulation (tDCS) compared to sham stimulation on global cognition and
NPS in people with AD and MCI.
Method: Multi-session randomized sham-controlled clinical trials were identified through MEDLINE, PsycINFO,
and Embase until June 2021. Standardized mean difference (SMD) and 95% confidence interval (CI) between the
active and sham treatments were calculated using random-effects meta-analyses. Included studies reported
outcome measures for global cognition and/or NPS. Heterogeneity, from different NIBS techniques, disease
populations, or tests used to assess global cognition or NPS, was measured using chi-square and I2, and inves­
tigated using subgroup analyses. Possible effects of covariates were also investigated using meta-regressions.
Result: The pooled meta-analyses included 19 studies measuring global cognition (Nactive=288, Nsham=264), and
9 studies investigating NPS (Nactive=165, Nsham=140). NIBS significantly improved global cognition (SMD=1.14;
95% CI=0.49,1.78; p = 0.001; I2 = 90.2%) and NPS (SMD=0.82; 95% CI=0.13, 1.50; p = 0.019; I2 = 86.1%)
relative to sham stimulation in patients with AD and MCI. Subgroup analyses found these effects were restricted
to rTMS but not tDCS, and to patients with AD but not MCI. Meta-regression showed that age was significantly
associated with global cognition response (Nstudies=16, p = 0.020, I2 = 89.51%, R2 = 28.96%), with larger effects
sizes in younger populations. All significant meta-analyses had large effect sizes (SMD ≥0.8), suggesting clinical
utility of NIBS in the short term. There remained substantial heterogeneity across all subgroup analyses and
meta-regressions (all I2 > 50%). Egger’s tests showed no evidence of publication biases.
Conclusion: rTMS improved global cognition and NPS in those with AD. Further studies in MCI and using tDCS
will help to fully evaluate the specific NIBS techniques and populations most likely to benefit on global cognition
and NPS measures. Additional research should investigate the long term clinical utility of NIBS in these
populations.
* Correspondence to: Sunnybrook Health Sciences Centre, 2075 Bayview Ave., Room FG 21, Toronto ON M4N 3M5, Canda.
E-mail address: Krista.Lanctot@sunnybrook.ca (K.L. Lanctôt).
1
These authors contributed equally.
https://doi.org/10.1016/j.arr.2021.101499
Received 7 May 2021; Received in revised form 7 October 2021; Accepted 19 October 2021
Available online 23 October 2021
J. Teselink et al.
Ageing Research Reviews 72 (2021) 101499
1. Introduction
effective in addressing cognitive and behavioural dysfunction in AD or
MCI. Several reviews and meta-analyses have shown positive effect of
NIBS in neurodegenerative disorders (Freitas et al., 2011; Xu et al.,
2019) however, potential limitations in methodology and the impact of
stimulation parameters were not addressed. This systematic review and
meta-analysis aimed to investigate the combined and comparative effi­
cacy of randomized, placebo-controlled trials of rTMS and tDCS in
multi-session randomized, placebo-controlled trials on measures of
global cognition and NPS in AD and MCI. We aim to review evidence
that rTMS and tDCS will significantly improve global cognition and
neuropsychiatric symptoms in patients with AD and MCI when
compared to sham stimulation.
Dementia is diagnosed in an estimated 10 million people annually,
and 60–70% of those cases are attributed to Alzheimer’s disease (AD)
(WHO, 2020). Along with significant deteriorations in cognition and
function, those with AD also commonly experience neuropsychiatric
symptoms (NPS) (Masopust et al., 2018; Weiler et al., 2020). Current
management approaches have shown limited efficacy (Masopust et al.,
2018), and often do not alter the underlying disease progression (Weiler
et al., 2020). Non-invasive interventions, which may impact neuro­
plasticity, are increasingly being evaluated as symptom and
disease-modifying strategies for AD dementia and its precursor risk
states such as mild cognitive impairment (MCI).
Non-invasive brain stimulation (NIBS) techniques, such as trans­
cranial direct current stimulation (tDCS) and repetitive transcranial
magnetic stimulation (rTMS), have been increasingly used to modify
cognition in participants with neurodegenerative disorders (Hsu et al.,
2015; Birba et al., 2017). NIBS can transiently and non-invasively
modulate neuronal activity and cortical excitability, making it an
intriguing candidate for mitigating cognitive dysfunction and NPS in
AD. tDCS applies a constant, low electrical current between electrodes
over the scalp, which modulates cortical activity (Stagg et al., 2018). Its
neural excitatory properties have been found to be effective at reducing
depressive symptoms in patients with depression (Brunoni et al., 2016).
Some studies have shown improved working memory performance and
enhanced episodic verbal memory with anodal tDCS in healthy in­
dividuals (Ross et al., 2011; Andrews et al., 2011; Martin et al., 2013;
Park et al., 2014), depressed individuals (Wolkenstein and Plewnia,
2013; Zimerman and Hummel, 2010), and more recently in MCI and AD
(Liu et al., 2020). However, a review by Cai et al. (2019) found that tDCS
showed beneficial effects in mild to moderate AD for single sessions, but
sub-analyses suggested that effects were not sustained for repeated
sessions. That review did not, however, consider rTMS or patients with
MCI. Similarly, a recent review suggested that tDCS showed beneficial
effects in patients with dementia, but did not address MCI due to limited
data (Cruz Gonzalez et al., 2018). That paper did consider multi-session
tDCS, but did not consider rTMS in their analysis.
rTMS induces local neuronal depolarisation, resulting in cortical
activity modification by the repetitive delivery of a high intensity
magnetic field over a target area of the brain via the scalp (Zimerman
and Hummel, 2010; Weiler et al., 2020). rTMS is particularly suited to
sustain excitatory effects, providing a rationale for research on cognition
(Rossi et al., 2009). Interestingly, rTMS is an approved treatment for
patients with major depressive disorder (MDD) in several countries,
including Canada and USA. The high comorbidity depression has with
AD has guided research trials to co-opt rTMS for AD treatment. Studies
have shown rTMS can improve cognition in AD, while also reducing
depression (Heath et al., 2018). A review by Chou et al. (2020) found a
significant effect of rTMS on cognition in MCI and AD, but sub-analyses
also noted discrepancies, with high frequency rTMS over the left
dorsolateral prefrontal cortex (DLPFC) but not the right DLPFC signifi­
cantly improving memory functions. But that review did not include
tDCS or address NPS as an outcome. The mechanisms of action for rTMS
and tDCS are not clear, but evidence suggests that they similarly miti­
gate neurological symptoms by modulating neuronal plasticity in the
brain through altering the excitability of the cortical neurons (Gome­
s-Osman et al., 2018). Moreover, both rTMS and tDCS have been shown
to increase levels of BDNF, a neurotrophic factor important for neuronal
plasticity, which decreases in AD brains (Makowiecki et al., 2014; Cocco
et al., 2018). This suggests that both rTMS and tDCS may be similar in
their mechanism of action and may result in similar effects on stimu­
lating cortical activity in patients with AD or MCI. Current research has
offered little coordination in addressing cognitive outcomes and
neuropsychiatric symptoms in this a highly complex and variable dis­
ease, often resulting in contradictory findings (Nilsson et al., 2017).
There remains little direction on whether tDCS and rTMS might be
2. Methods
2.1. Data sources & search strategy
The Preferred Reporting Items for Systematic Review and MetaAnalyses (PRISMA) guidelines were followed for the methodology of
this review. All published articles before June 2021 were searched using
MEDLINE, PsycINFO, and Embase databases for original articles. A
sample search strategy for Embase has been included in a supplementary
table. To ensure that studies met inclusion and exclusion criteria and
were accurately extracted, three independent reviewers were involved
in the assessment, data extraction, and analyses of all retrieved articles.
Reference lists of identified studies were searched for relevant studies
not identified through the database search.
2.2. Study selection
Inclusion criteria consisted of: (1) a patient population with a diag­
nosis of AD or MCI as described by accepted standardized clinical
criteria (i.e - DSM, NINCDS-ADRDA, or Petersen’s criteria for MCI), a
diagnosis by medical specialist (i.e - psychiatrist, neurologist or geria­
trician), and with imaging/biomarkers (i.e - CT or MRI showing brain
atrophy) if applicable. Furthermore, included studies where concomi­
tant pharmacological therapies were considered were controlled for
with 2–3 months of stable cholinesterase inhibitors or no medication
prior to start of the study, with no history of psychoactive agents (i.e benzodiazepines) (2) must have administered multi-session non-inva­
sive brain stimulation including tDCS, TMS, or rTMS, (3) inclusion of a
blinded, sham condition, (4) and reported an outcome of cognition and/
or NPS before and after stimulation. Searches were limited to the English
language and humans. Non-primary articles including reviews, edito­
rials, conference abstracts, case studies, and protocols were excluded
from analyses. Primary studies not involving the patient population (e.
g., Parkinson’s Disease, Primary Progressive Aphasia, or Huntington’s
Disease), missing a sham condition, not involving non-invasive brain
stimulation techniques, or missing outcomes of interest were excluded.
To quantitatively meta-analyse the results, at least three papers were
required reporting measurable global cognition and NPS outcomes (pretreatment and post-treatment scores, and/or difference scores). Studies
reporting other outcomes, including outcomes related to specific
cognitive domains, functionality etc. were qualitatively summarized in
the systematic review table.
2.3. Data extraction
The pre- and post-treatment means and standard deviations (SD) for
outcomes including global cognition and NPS were extracted for each
included study. In addition, participant characteristics (mean age,
gender proportion, years of education, baseline Montreal Cognitive
Assessment (MoCA) and Mini Mental State Examination (MMSE) scores,
medications used) and study characteristics (study design, tDCS and
rTMS parameters) were also recorded. Corresponding authors for the
included records were contacted for missing data. Primary articles with
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Ageing Research Reviews 72 (2021) 101499
variables that had missing data/un-usable for all outcomes of interest
were not included in the meta-analysis but summarized qualitatively.
Planned subgroup analyses were based on the type of NIBS technique
used (rTMS or tDCS), the patient population (AD or MCI), tests used to
evaluate outcomes, whether the studies used adjunctive cognitive
training along with NIBS, and region of brain stimulation, if data from at
least three studies were available for analysis. Meta-regression analyses
were performed to assess the relationships between age, percent female,
years of education, stimulation frequency, and stimulation duration
with global cognition and NPS, if data from at least ten studies were
available. Publication bias was assessed quantitatively using the Egger’s
test and qualitatively using funnel plots. All statistical analyses were
performed using STATA software (V.16).
2.4. Methodological quality assessment
Risk of bias in each study was assessed using items adapted from the
Newcastle-Ottawa Scale and the Cochrane Collaboration’s risk of bias
assessment tool. The items were designed to assess whether the studies
contained methodological sources of bias that may influence the metaanalyses. The quality of each study was assessed by two independent
raters and the final decision regarding the inclusion of the study was
reached by consensus. A sensitivity analysis was conducted by removing
studies with high potential risk of bias.
3. Results
2.5. Statistical analyses
3.1. Literature search findings
Due to the anticipation of high variability between studies, a random
effects meta-analysis model was used to investigate the effect of NIBS on
global cognition and NPS. Variability and heterogeneity across studies
due to different study designs, patient populations, NIBS techniques, and
the assessments used to measure global cognition and NPS were ex­
pected. Standardized mean differences (SMD) and 95% confidence in­
tervals were calculated and reported for each primary outcome. The
SMD depicts the effect size of the intervention on the outcome, with an
effect size of ≥ 0.8 considered large and of potential clinical significance.
Studies reporting any outcome measures for global cognition
[MMSE, MoCA, Alzheimer’s Disease Assessment Scale–Cognitive Sub­
scale (ADAS-Cog)]or NPS [Neuropsychiatric Inventory (NPI), Geriatric
Depression Scale (GDS), Cornell Depression Scale (CDS), Apathy Eval­
uation Scale (AES), Behavioural Pathology in Alzheimer’s Disease
(BEHAVE-AD)] were included in the meta-analysis. The MMSE is a
commonly used dementia screening tool which includes a 30-point
questionnaire used to measure cognitive impairment, with higher
scores indicating better cognition (Arevalo-Rodriguez et al., 2015)
(Creavin et al., 2016) (Kang et al., 2018). The MoCA is another cognitive
screening test that is used to detect MCI. It is a 30-point cognitive test
that is shown to have a higher sensitivity and specificity in detecting
cognitive decline when compared to the MMSE (Kang et al., 2018). The
ADAS-Cog is a neuropsychological assessment that is used to assess
cognitive domains which include memory, language, praxis and orien­
tation, with higher scores indicating worse performance (Kueper et al.,
2018). For studies reporting more than one global cognition outcome,
the ADAS-Cog was included in the pooled meta-analysis due to its
comprehensive nature. When evaluating the neuropsychiatric symp­
toms, the NPI was chosen as the outcome in case of a study reporting
more than one neuropsychiatric outcome, because it measures a range of
NPS. The NPI is an assessment scale used to assess behavioral and mood
symptoms in people with dementia and other neurological disorders
over the previous month (Cummings et al., 1994). Two depression rating
scales were used in the studies included in the meta-analysis. The GDS is
a self-report screening test used to identify symptoms of depression in
older adults (Yesavage et al., 1983). The CDS is used to assess the
severity of depressive symptoms in older adults with depression. This
scale includes both patient and informant interviews. The AES is a
measurement tool for apathy in prodromal and preclinical AD (Alex­
opoulos et al., 1988). The AES consists of 18 items relating to apathy,
with lower scores indicating greater apathy (Marin et al., 1991).
Another assessment scale used in one of the studies was the
BEHAVE-AD. This scale assesses behavioral and psychological symp­
toms in AD patients (Reisberg et al., 1997).
The Cochran Q test was used to measure heterogeneity, and a p value
of <0.05 indicates the presence of significant heterogeneity. The I2
statistic was used to quantify the heterogeneity in each analysis with I2
> 50% indicating substantial heterogeneity (Higgins et al., 2003; Deeks
et al., 2021). To explore potential sources of heterogeneity, pre-planned
subgroup analyses and meta-regression analyses were carried out.
The literature search returned 1859 unique records of NIBS studies in
participants with AD or MCI (Fig. 1). Participant demographic charac­
teristics can be found in Table 1, with study stimulation characteristics
found in Tables 2 and 3. A total of 288 active participants and 264 sham
participants were included in the meta-analyses. 6 tDCS studies and 13
rTMS studies were assessed for study quality (Table 4).
3.2. Effects of NIBS on global cognition
A total of 19 studies (Nactive=288, Nsham=264) were included in the
pooled meta-analysis investigating the effects of active NIBS compared
to sham on global cognition in AD and MCI. NIBS (rTMS [13 studies] +
tDCS [6 studies]) significantly improved global cognition (as measured
by MMSE, MoCA, or ADAS-Cog) in patients with AD and MCI
(SMD=1.37; 95% CI=0.49,1.78; p = 0.001; I2 = 90.2%, Fig. 2).
3.3. Effects of NIBS on NPS
A total of 9 studies (Nactive=165, Nsham=140) were included in the
pooled meta-analysis investigating the effects of active versus sham
NIBS on NPS in AD and MCI. NIBS (rTMS [6 studies] + tDCS [3 studies])
significantly improved NPS in patients with AD and MCI (SMD=0.82;
95% CI=0.13, 1.50; p = 0.019; I2 = 86.1%, Fig. 3).
3.4. Publication bias and investigation of heterogeneity
Publication bias was not detected by Egger’s test or funnel plots
(Figs. S1 and S2).
Subgroup analysis in global cognition and NPS: Subgroup analyses
were used to investigate potential heterogeneity in the meta-analyses
arising from different NIBS techniques, disease populations, and tests
used to measure global cognition and NPS, adjunctive use of cognitive
training, and stimulation of different brain regions.
3.4.1. Subgroup analyses of global cognition
Subgroup analysis showed that active rTMS (SMD=1.13; 95% CI=
0.44,1.82; p = 0.004; I2 = 87.4%) but not tDCS (SMD=1.20; 95% CI=
− 0.26, 2.65; p = 0.107; I2 = 93.6%) significantly improved global
cognition (Fig. 2). Patients with AD (SMD=1.07; 95% CI= 0.39,1.75;
p = 0.002; I2 = 90.1%) but not MCI (SMD=1.55; 95% CI= − 0.69,
3.78; p = 0.175; I2 = 92.4%), showed improvement on global cognition
following active NIBS (Table 4). Subgroup analysis based on different
tests of global cognition, i.e. MMSE and ADAS-Cog, showed that patients
with AD/MCI improved on ADAS-cog (SMD=1.12; 95% CI= 0.21, 2.02;
p = 0.015; I2 = 88.6%) following NIBS but not on MMSE (SMD=0.32;
95% CI= − 0.20, 0.85; p = 0.221; I2 = 70.4%) (Table 4). When analysed
separately, patients with AD showed significant improvement on ADASCog (SMD=1.63; 95% CI= 0.53,2.20; p = 0.001; I2 = 82.6%) and
MMSE (SMD=19.3; 95% CI= 0.35,3.516; p = 0.017, I2 = 95.5%)
following rTMS (Table S1).
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Ageing Research Reviews 72 (2021) 101499
Fig. 1. Flow diagram showing the selection of studies for the systematic review and meta-analysis.
Some studies in the meta-analysis assessing the effects of NIBS on
global cognition also administered adjunctive cognitive training to the
participants. Two studies administered face-name association training
(Cotelli et al., 2014; Bagattini et al., 2020) and an additional two
administered different cognitive paradigms based on the area being
stimulated using a NeuroAD system (Rabey et al., 2013; Lee et al., 2016).
A subgroup analysis based on the administration of adjunctive cognitive
therapy revealed that patients who received cognitive training did not
significantly improve on global cognition following NIBS (SMD= 0.295;
95% CI= − 0.38, 0.97; p = 0.393; I2 = 67.3%), whereas those who did
not receive any adjunctive cognitive training improved on global
cognition following NIBS (SMD= 1.389; 95% CI= 0.578, 2.2; p = 0.019;
I2 = 91.4%) as compared to the sham group.
Additionally, the subgroup analysis based on location of stimulation
revealed that active stimulation of the left DLPFC significantly improved
performance of global cognition in patients with AD/MCI compared to
sham stimulation (SMD= 0.893; 95% CI= 0.14, 1.64; p = 0.001; I2 =
90.3%). Of the 19 studies included in the meta-analysis of global
cognition, 13 performed stimulation on left DLPFC alone, and hence
were analyzed separately in the subgroup analysis. There were too few
studies performing stimulation on other regions [left and right DLPFC
(n = 2), temporal lobe (n = 1), temporal and parietal lobe (n = 1),
DLPFC, Broca’s, Wernicke’s and parietal somatosensory association
cortex areas (n = 2)] and hence could not be analysed separately in a
subgroup analysis (Tables 2 and 3).
p = 0.04, I2 = 80.4%) but not tDCS (SMD=0.91; 95% CI= − 0.73, 2.56;
p = 0.276; I2 = 93.8%, Fig. 3). Patients with AD (SMD=0.84; 95%
CI=0.11,1.58; p = 0.024; I2 = 87.9%) showed improvements on NPS
following NIBS (Table 5). Subgroup analysis based on specific NPS
outcome measures used in different papers, i.e. scales measuring
depression or overall NPS, did not show improvement following NIBS in
patients with AD or MCI (Table 5). rTMS did not show improvement in
NPS in patients with AD when analyzed separately (Table S1).
A subgroup analysis based on the location of stimulation was also
performed for the meta-analysis assessing the effect of NIBS on neuro­
psychiatric symptoms (9 studies). In the 6 studies using stimulation of
the left DLPFC alone, there was no significant effect of NIBS compared
with sham stimulation on NPS in AD/MCI in these studies (SMD= 0.579;
95% CI= − 0.167, 1.320; p = 0.129; I2 = 83%). There were not enough
studies stimulating other areas [left and right DLPFC (n = 1), temporal
lobe (n = 1), DLPFC, Broca’s, Wernicke’s, and parietal somatosensory
association cortex areas (n = 1)] to perform a subgroup analysis.
Meta-regression analysis revealed a significant association between
age and the effect of NIBS on global cognition (Nstudies=17, p = 0.020, I2
= 89.51%, R2 = 28.96%, Fig. 4). After controlling for age, active NIBS
was still significantly associated with an improvement on global
cognition (p = 0.013). No significant effect of years of education,
percent females, stimulation frequency, or stimulation duration was
found on the effect of NIBS on global cognition.
3.4.2. Subgroup analyses of neuropsychiatric symptoms
Subgroup analysis showed that neuropsychiatric symptoms
improved following active rTMS (SMD=0.78; 95% CI= 0.03,1.53;
3.5. Risk of bias and sensitivity analysis
Three studies were found to have high potential risk of bias (Table 6).
After removing the studies with potential risk of bias, the effect of NIBS
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Ageing Research Reviews 72 (2021) 101499
Table 1
Mean ± SD for active and sham participants across all included records. Scores denoted by an * reflect MOCA score. Studies in bold were included in meta-analysis.
Age (yrs)
Female
(%)
Education
(yrs)
Baseline Cognition: MMSE
or MOCA
rTMS Studies
Active
Ahmed et al. (2012)
Brem et al. (2020)
65.9 ± 5.9
69.25 ±
6.80
73.56 ±
4.91
71.2 ± 6.1
73.91 ±
100.01
N/A
72.1 ± 7.6
65.97 ±
8.47
65.1 ± 3.5
10 (67)
12 (75)
N/A
14.25 ± 4.64
14.7 ± 3.7
21.19 ± 2.69
10 (37)
8.85 ± 3.91
23.67 ± 3.00
N/A
8 (72.7)
6.4 ± 1.3
12.45 ± 3.98
16.2 ± 2.7
27.73 ± 20.00
N/A
10 (55.6)
17 (46)
N/A
9.9 ± 4.8
5.65 ± 3.21
N/A
22.4 ± 2.9
16.13 ± 4.27
9 (60.0)
15.1 ± 4.4
24.5 ± 1.8 *
64 ± 8.5
74.3 ± 5.7
72.6 ± 8.9
0 (0)
1 (11.1)
2(28.6)
13.3 ± 1.5
N/A
N/A
26.7 ± 1.5
22.9 ± 3.4
22 ± 1.63
Sabbagh et al. (2020)
76.9 ± 4.33
38 (66.6)
N/A
21.7 ± 4.37
Rutherford et al. (2015)
Wu et al. (2015)
Yuan et al. (2021)
N/A
16 (61.5)
6 (50)
N/A
11.4 ± 2.7
11.83 ± 2.37
N/A
15.3 ± 3.1
22.83 ± 1.11 *
Zhao et al. (2017)
N/A
71.4 ± 4.9
65.08 ±
4.89
69.3 ± 5.8
10 (58.8)
4.8 ± 1.9
tDCS Studies
Boggio et al. (2012)
Bystad et al. (2016)
Cotelli et al. (2014)
de Sousa et al. (2020)
Gangemi et al. (2020)
Active
N/A
70.0 ± 8.0
77.4 ± 4.87
N/A
67.5 ± 2.8
22.2 ± 2.8
17.5 ± 6.2 *
N/A
5 (41.7)
20 (83)
N/A
N/A
N/A
N/A
5.9 ± 2.48
N/A
6.5 ± 2.0
N/A
20.0 ± 2.8
21.2 ± 2.52
N/A
14.9 ± 1.8
Im et al. (2019)
Khedr et al. (2014)
Khedr et al. (2019)
71.9 ± 9.2
68.5 ± 7.2
64.22 ±
3.64
N/A
68.39 ±
8.37
79.4 ± 7.1
74.75 ±
7.47
70.52 ±
10.2
10 (91)
5 (45)
10 (44)
6.3 ± 3.8
N/A
4.04 ± 2.83
20.1 ± 3.8
18.4 ± 3.9
N/A
N/A
6 (50)
N/A
11.83 ± 2.37
N/A
22.40 ± 2.9
15 (75)
5 (63)
5.0 ± 4.2
8.06 ± 4.93
15.0 ± 3.1
26.75 ± 1.58
10.22
8.975 ±
3.189
20.55 ± 3.73
21.61 ± 3.03 *
Bagattini et al. (2020)
Cotelli et al. (2011)
Cui et al. (2019)
Koch et al. (2018)
Lee et al. (2016)
Li et al. (2021)
Drumond Marra et al.
(2015)
Padala et al. (2018)
Padala et al. (2020)
Rabey et al. (2013)
Roncero et al. (2017)
Stonsaovapak et al.
(2020)
Suemoto et al. (2014)
Yun et al. (2016)
Average
Age (yrs)
Female
(%)
Education
(yrs)
Baseline Cognition: MMSE
or MOCA
68.3 ± 4.9
69.10 ±
5.24
73.35 ±
1.09
74.4 ± 3.8
N/A
12 (80)
5 (50)
N/A
13.90 ± 5.07
13.9 ± 9.9
22.00 ± 1.83
11 (47.8)
7.91 ± 0.67
22.77 ± 0.58
N/A
5 (50)
16.0 ± 2.0
26.50 ± 2.72
N/A
70.3 ± 4.8
64.58 ±
7.88
65.2 ± 4.1
N/A
5 (62.5)
14 (37)
4.8 ± 0.4
12.50 ±
40.07
N/A
9.9 ± 3.7
6.75 ± 4.51
13 (68.4)
12.4 ± 4.7
24.2 ± 2.3 *
64.0 ± 9.0
N/A
75.4 ±
9.07
76.7 ±
4.60
N/A
71.9 ± 4.8
64.67 ±
4.77
71.4 ± 5.2
1 (20)
N/A
3(37.5)
12 ± 0.0
N/A
N/A
24.2 ± 2.4
N/A
22 ± 1.41
21 (42)
N/A
21.3 ± 4.38
N/A
15 (57.7)
7 (58.3)
N/A
11.5 ± 2.1
11.33 ± 2.15
N/A
15.2 ± 3.2
22.0 ± 1.28 *
7 (54.8)
4.9 ± 3.5
22.8 ± 2.3
18.1 ± 7.3 *
N/A
6 (46.2)
9 (75)
N/A
N/A
N/A
N/A
8.9 ± 5.1
N/A
6.1 ± 2.1
N/A
21.2 ± 3.9
20.8 ± 2.1
N/A
15.3 ± 1.8
5 (71)
6 (54)
8 (38)
5.4 ± 5.9
N/A
3.52 ± 1.96
22.1 ± 4.6
16.9 ± 2.9
N/A
N/A
20 (90.9)
N/A
N/A
13 (65)
6 (75)
4.5 ± 3.9
5.56 ± 2.41
N/A
27.5 ± 1.14
22.45 ± 1.60 *
15.4 ± 2.6
25.12 ± 2.74
9.14
8.345 ± 5.19
Sham
Sham
N/A
75.0 ± 8.7
74.7 ± 6.1
N/A
69.01 ±
3.1
74.9 ± 5.0
67.3 ± 5.9
65.23 ±
4.52
N/A
69.68 ±
7.60
81.6 ± 8.0
73.12 ±
4.25
70.90 ±
5.56
N/A
22.8 ± 2.5
15.97 ± 4.12
20.48 ± 3.20
21.69 ± 3.12 *
Table 2
tDCS parameters of included records. Studies in bold were included in meta-analysis.
tDCS
Boggio et al. (2012)
Bystad et al. (2016)
Cotelli et al. (2014)
de Sousa et al. (2020)
Gangemi et al. (2020)
Im et al. (2019)
Khedr et al. (2014)
Khedr et al. (2019)
Roncero et al. (2017)
Stonsaovapak et al. (2020)
Suemoto et al. (2014)
Song et al. (2016)
Average
Target (Anode) Electrode Placement
Intensity (mA)
Electrode Size (cm2)
Duration (minutes)*
Frequency (# of sessions)
Temporal Lobe T3 and T4
Left Temporal lobe (T3)
Left DLPFC (F3)
Right Tempoparietal Cortex (T6)
Left Frontotemporal Lobe (F7-T3)
Left DLPFC (F3)
Left DLPFC (F3)
temporal lobe T3 and T4
Left Inferior Tempoparietal region (P3)
Right DLPFC (F4)
left DLPFC (F3)
Left DLPFC (F3)
2
2
2
1
2
2
2
2
2
2
2
2
1.91
35
35
25
35
25
28
24
35
35
25
35
25
30.16
30
30
25
20
20
30
25
20
30
20
20
30
25
5
6
10
3
10
54
10
10
10
12
6
9
12.08
on global cognition (SMD=1.16; 95% CI= 0.41,1.92; p = 0.003, I2 =
91.6%) and NPS (SMD=0.99; 95% CI= 0.06, 1.92; p = 0.037, I2 =
89.8%) was still significantly greater than that of sham (Padala et al.,
2018, 2020; Bagattini et al., 2020).
3.6. Systematic review
A total of 29 studies were included in the systematic review (Sup­
plementary Table 2), 19 of which were included in the meta-analysis.
5
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Ageing Research Reviews 72 (2021) 101499
Table 3
rTMS parameters of included records. Cross-Over* studies only had first instances (arm prior to cross-over) included in analyses. Studies in bold were included in metaanalysis.
rTMS
Ahmed et al.
(2012)
Brem et al.
(2020)
Bagattini et al.
(2020)
Cotelli et al.
(2011)
Cui et al. (2019)
Koch et al. (2018)
Lee et al. (2016)
Li et al. (2021)
Drumond Marra
et al. (2015)
Padala et al.
(2018)
Padala et al.
(2020)
Rabey et al.
(2013)
Rutherford et al.
(2015)
Sabbagh et al.
(2020)
Wu et al. (2015)
Yuan et al.
(2021)
Zhao et al. (2017)
Average
Stimulation Position
Intensity
(>5 Hz)
Stimulation type (i.e
cTBS vs rTMS standard)
Left and Right DLPFC (F3 and F4)
20
rTMS
Parietal Lobe (P3/P4) and posterior temporal
lobe (T5 and T6)
Left DLPFC
10
Duration
(min)
Pulses per
session
Total
Pulses
5
20
2000
4000
rTMS
30
60
2000
60000
20
rTMS
20
25
2000
40000
Left DLPFC
20
rTMS
20
25
2000
40,000
Right DLPFC (F4)
Precuneus
DLPFC (F3 and F4), Broca’s area (F7 - T3),
Wernicke`s areas (T7 - P3), r-pSAC and lpSAC
(T5)
Left DLPFC
Left DLPFC
10
20
10
rTMS
rTMS
rTMS
10
10
30
60
20
60
1500
1600
2400
15,000
16,000
72000
20
10
rTMS
rTMS
30
10
20
2000
2000
60000
20000
Left DLPFC
10
rTMS
10
45
3000
30,000
Left DLPFC
10
rTMS
20
20
3000
60000
Left and Right DLPFC (F3 and F4), Broca’s area
(F7 - T3), Wernicke`s areas (T7 - P3), r-pSAC and
L-pSAC (T5 and T6)
Left and Right DLPFC (F3 and F4)
20
rTMS
54
20
1300
70,200
20
rTMS
13
2000
26,000
Left and Right DLPFC (F3 and F4), Broca’s area
(F7 - T3), Wernicke`s areas (T7 - P3), r-pSAC and
L-pSAC (T5 and T6)
Left DLPFC
Left DLPFC
10
rTMS
30
1300
39,000
20
10
rTMS
rTMS
20
20
1200
400
24,000
8000
Parietal lobe (P3/P4) and posterior temporal
lobe (T5 and T6)
20
rTMS
30
30
21.29
33.45
1856.25
36512.5
15.29
Stimulation
Type
N
studies
N participants
active/sham
SMD (and 95%
CI)
I2
pvalue
a
13
194/186
1.13
(0.44,1.82)
1.20 (− 0.26,
2.65)
87.4%
0.001
93.6%
0.107
1.07 (0.39,
1.75)
1.55
(− 0.69,3.78)
90.1%
0.002
92.4%
0.175
tDCS
Diagnosis
AD
a
MCI
Outcome
MMSE
6
15
4
94/78
253/221
35/43
10
125/114
ADAS-Cog
7
128/117
MoCA
Pooled metaanalysis
2
19
35/33
288/264
a
60
3.33
4. Discussion
Table 4
Summary of subgroup-analysis for global cognition.
rTMS
Frequency
(Number of
sessions)
This meta-analysis aimed to assess the efficacy of NIBS in improving
global cognition and NPS in AD/MCI patients. Given the evidence that
suggests both rTMS and tDCS mitigate neurological symptoms by
modulating neuronal activity and excitability in the brain (Gomes-Os­
man et al., 2018), we combined rTMS and tDCS studies to examine the
effect of NIBS on global cognition and NPS in patients with AD and MCI.
Patients significantly improved on global cognition and NPS following
active NIBS compared to sham. We also performed subgroup analyses,
where greater improvement in NPS and global cognition was observed
following active rTMS compared to sham, although a similar positive but
non-significant trend was observed in the tDCS group. Active NIBS
resulted in improved global cognition and NPS in the AD population,
whereas a similar but non-significant trend was observed in MCI. When
analyzed separately, improvements in ADAS-Cog and MMSE were
observed in AD patients following rTMS treatment. Additionally, sig­
nificant improvement on global cognition was found for studies not
undergoing any adjunctive cognitive training along with NIBS and those
stimulation the left DLPFC alone.
Based on our findings, measures of global cognition showed signifi­
cant improvement with a large effect size following active NIBS. The
ADAS-Cog and MMSE are commonly used to assess global cognition in
the AD and MCI populations. The more recent use of the MoCA to assess
global cognition is supported by its specificity and sensitivity in
detecting cognitive decline in early stages of the disease (Solomon et al.,
2014), in addition to its sensitivity in detecting change in cognition over
time (Krishnan et al., 2017). A recent study showed that scores from all
three measures are highly correlated and measure similar cognitive
0.33 (− 0.20,
70.4%
0.221
0.85)
1.12 (0.21,
88.6%
0.015
2.02)
Not enough studies to meta analyze
1.14 (0.49,
90.2%
0.001
1.78)
a
Subgroup analysis that showed significant differences between active and
sham groups
Measures of outcomes other than global cognition and neuropsychiatric
symptoms, including other cognitive domains, functionality, and disease
severity, were compared pre- and immediately post-NIBS. Due to the
variable study design and statistical methods used across different
studies, findings from studies were summarized based on significant
time, group, and interaction (time x group) effects.
6
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Ageing Research Reviews 72 (2021) 101499
Fig. 2. Meta-analysis of the effects of active versus sham NIBS (rTMS and tDCS) on global cognition in AD/MCI. NIBS, specifically rTMS, significantly improved
performance on global cognition in patients with AD/MCI.
functions (Solomon et al., 2014). All studies included in the
meta-analysis reported at least one of these three measures, and changes
in these measures were used to assess the efficacy of active versus sham
NIBS on global cognition. In subgroup analyses, significant improve­
ment on both ADAS-Cog and MMSE following rTMS was observed in AD
patients; not enough papers used the MoCA to do a subgroup analysis.
This meta-analysis found significant efficacy for rTMS but not tDCS.
Although both tDCS and rTMS might produce changes to brain con­
nectivity and activity, these differences may be due to the functional
differences in the mechanism of action between the two techniques. In
particular, rTMS can induce action potentials whereas tDCS can only
increase the resting membrane potential of the cortical neurons
(Gomes-Osman et al., 2018). However, rTMS penetrates much deeper
into the skull, and with the increased spatial and temporal specificity of
rTMS, it provides a more controlled, consistent, and focused stimulation
to the brain, whereas tDCS often uses saline soaked sponges held by
elastic caps which can lead to varying levels of resistance (current)
reaching the brain and diminishing cortical stimulation (Gomes-Osman
et al., 2018). It is important to note that there were fewer studies using
tDCS than rTMS, and the decreased power of the tDCS studies may be
preventing us from seeing a significant effect of tDCS on global cognition
and NPS. Hence, more studies investigating the effect of tDCS on
cognition and NPS are needed to form definitive conclusions about the
efficacy of tDCS as a therapeutic technique and the parameters that work
best in this population. Moreover, studies in this meta-analysis had an
average follow-up period of 3 months, with follow up ranging from 0 to
6 months. While effects of NIBS on cognition and NPS showed some
sustained effects after treatment in the included articles this
meta-analysis cannot fully address the sustainability of the effects,
although the effect sizes observed in this study were large and suggest
potential clinical significance immediately after the treatment. Future
meta-analyses should investigate the period over which the effects of
NIBS are sustained after treatment in AD and MCI populations to provide
insights on if and when booster NIBS sessions should be scheduled. In
meta-regressions, stimulation duration and frequency did not signifi­
cantly affect the relationship between NIBS and global cognition. Hence,
future studies should also aim to investigate parameters of NIBS most
efficacious and economical in their effects on cognition and NPS in pa­
tients with AD and MCI. Furthermore, future studies should also look at
the effects of NIBS on other important outcomes of AD such as activities
of daily living, which can potentially demonstrate the clinical relevance
of this intervention.
We did not find beneficial effects of adjunctive cognitive training on
the efficacy of NIBS on global cognition, but these results may be due to
only four studies using cognitive training techniques. Hence there may
be too few studies to detect an additive benefit. Additionally, the two
cognitive training techniques used were different and targeted different
regions of the brain and different cognitive domains in patients with AD,
one using face name association training, whereas the other included
different tasks specific to 6 different brain regions. These studies which
targeted different regions of the brain, and, used different kinds of NIBS,
and global cognition outcomes, and were highly heterogeneous, and as a
7
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Ageing Research Reviews 72 (2021) 101499
Fig. 3. Meta-analysis of the effects of active versus sham NIBS (rTMS and tDCS) on NPS in AD/MCI. NIBS, specifically rTMS, significantly improved NPS in patients
with AD/MCI.
result the analysis may not have revealed any beneficial effects on global
cognition. Moreover, patients with AD may not benefit most with these
training techniques as they may be more effective for the prodromal
stages of the disease, such as in MCI. We also found that stimulation of
left DLPFC significantly improved global cognition but not NPS. Again,
this may be due to the small number of studies assessing NPS, in addition
to high heterogeneity. Further studies are needed to better understand
the additive effects of cognitive therapy, the kind of cognitive therapy
that may be most beneficial, and the areas of the brain that produce most
favourable results on global cognition and NPS in patients with AD and
MCI.
Our findings that NIBS improves global cognition as well as NPS in
AD and MCI populations raises the possibility that the observed
improvement in global cognition may partially be explained by im­
provements in NPS. rTMS has been shown to significantly improve NPS
(Wang et al., 2020), which is present in 33–78% of AD and MCI in­
dividuals (Geda et al., 2008). There is an association between mood
disturbances and cognitive deficits (Conradi et al., 2011; Baune et al.,
2010; Ng et al., 2019). Furthermore, NIBS stimulation protocols shown
to improve mood disturbances and cognition share similarities such that
they both commonly target the left DLPFC, and function to increase
excitability in the targeted area. Although most studies included in the
meta-analyses excluded patients with an active psychiatric diagnosis
and those on antipsychotic or antidepressant medications, it is difficult
to identify and exclude patients who show NPS but do not meet criteria
Table 5
Summary of subgroup-analysis for NPS.
Stimulation
Type
N
studies
N participants
active/sham
SMD (and
95% CI)
I2
pvalue
a
6
98/87
80.4%
0.04
tDCS
3
67/53
93.8%
0.276
Diagnosis
AD
0.78 (0.03,
1.53)
0.91 (− 0.73,
2.56)
a
8
162/135
MCI
1
3/5
Outcome
Depression
4
83/67
Apathy
2
12/15
Overall NPS
3
70/58
Pooled metaanalysis
9
165/140
rTMS
0.85 (0.11,
87.9%
0.024
1.58)
Not enough studies to meta
analyze
0.95 (− 0.27,
90.9%
0.128
2.17)
Not enough studies to meta
analyze
0.70 (− 0.64,
91.8%
0.304
2.04)
0.82 (0.13,
86.1%
0.019
1.50)
a
Subgroup analysis that showed significant differences between active and
sham groups
8
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Ageing Research Reviews 72 (2021) 101499
Fig. 4. Meta-regression looking at the association between age and the effect of NIBS on global cognition.
Table 6
Potential risk of bias table; + Low Risk; ? Unclear Risk; - High Risk.
Study
demographics
reported
nonretrospective
design
criteria used
for AD/MCI?
Randomized
treatment to
groups
Were the study staff and
interventionist blinded to
allocation?
Are Active and Sham group
similar in all characteristics?
Overall
Risk
Cotelli et al.
(2011)
Ahmed et al.
(2012)
Rabey et al.
(2013)
Khedr et al.
(2014)
Cotelli et al.
(2014)
Suemoto et al.
(2014)
Drumond Marra
et al. (2015)
Rutherford et al.
(2015)
Wu et al. (2015)
Yun et al. (2016)
Zhao et al.
(2017)
Padala et al.
(2018)
Im et al. (2019)
Khedr et al.
(2019)
Bagattini et al.
(2020)
Padala et al.
(2020)
Yuan et al.
(2021)
Lee et al. (2016)
Li et al. (2021)
?
+
+
+
?
?
?
?
+
+
+
+
+
?
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
?
+
–
+
+
?
?
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
–
–
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
–
+
+
+
–
+
+
–
+
+
+
–
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
?
+
+
+
+
for a clinical diagnosis. As a result, it is difficult to distinguish whether
the benefits of NIBS on global cognition are secondary to a beneficial
effect on NPS.
We found that NIBS significantly improve global cognition in AD but
not MCI patients. However, our meta-regression revealed a negative
association between age and the effect of NIBS on global cognition, such
that the beneficial effects of NIBS were greater in younger individuals.
The discrepancy in our findings may be explained by several factors.
First and most importantly, our finding of an overall non-significant
effect of NIBS on global cognition in the MCI population may be due
9
J. Teselink et al.
Ageing Research Reviews 72 (2021) 101499
to the limited number of available studies in this population. Specif­
ically, only four MCI studies were identified, compared to fifteen AD
studies. Second, the ceiling effect of the cognitive tests, especially the
MMSE (Spencer et al., 2013), can potentially limit the ability to detect
changes in performance before and after NIBS, particularly in the MCI
population. This may explain why another meta-analysis that only
looked at MMSE as a measure of global cognition also found NIBS to
have a positive effect in the AD population only (Chu et al., 2021).
Lastly, not all studies examining MCI populations had participants with
lower mean age compared to those in the AD studies, showing that lower
age does not necessarily correspond to the prodromal stage of the dis­
ease. This suggests that the effect of NIBS on global cognition is stronger
in younger individuals, independent of diagnosis. In line with this, the
differential effects of tDCS on functional network organization and
associative memory in young and older adults has been previously
documented (Pini et al., 2018; Leach et al., 2019). Hence, although we
found active NIBS to result in improved global cognition in the AD
population only, more studies are needed to conclude the effect in MCI.
In addition to global cognition and NPS, NIBS has also shown some
variable yet promising effect on overall disease severity (Drumond
Marra et al., 2015; Gangemi, 2020, Padala, 2020) and other specific
cognitive domains such as verbal memory (Cui et al., 2019, Bagattini,
2020), and processing speed and attention (Padala, 2018, Stonsaovapak,
2020) (Supplementary Table 2). In line with this, a previous
meta-analysis in MCI patients found NIBS to have positive benefits on
verbal fluency (Xu et al., 2019).
A few limitations should be taken into consideration when inter­
preting the results from our study. The use of different scales to measure
global cognition and NPS across different papers likely contributes to the
high heterogeneity. There were only a small number of studies on MCI
patients, and only one of these investigated the use of tDCS. More tDCS
studies, specifically in the MCI population, will be needed to confirm the
efficacy of tDCS on improving outcomes in the AD and MCI population.
rTMS may be a promising tool to improve cognition and NPS in AD.
Lastly, because our study focused on the immediate outcomes, future
studies will be needed to investigate the longer term clinical utility of
NIBS on various outcomes in the AD/MCI population.
Conceptualization, Writing – review & editing, Damien Gallagher:
Conceptualization, Writing – review & editing, Susan Marzolini:
Conceptualization, Writing – review & editing, Walter Swardfager:
Conceptualization, Supervision, Writing – review & editing, Nathan
Herrmann: Conceptualization, Visualization, Supervision, Writing –
original draft, Writing – review & editing, Krista L. Lanctôt: Concep­
tualization, Visualization, Supervision, Writing – original draft, Writing
– review & editing, Funding acquisition.
Declaration of Competing Interest
None.
Acknowledgements
The authors would like to gratefully acknowledge the following cli­
nicians/researchers including Zahra Moussavi (Rutherford et al., 2015),
Maria Cotelli et al. (2011)), Prasad R. Padala (Padala etal (2018), Eman
M. Khedr (Khedr et al., 2011; 2014), Yong-An Chung (IM et a, 2019),
Rosa Manenti (Cotelli et al., 2014), Hellen Marra (Drumond Marra et al.,
2015), and Hyeonseok Jeong (Yun et al., 2016) for their
correspondence.
Appendix A. Supporting information
Supplementary data associated with this article can be found in the
online version at doi:10.1016/j.arr.2021.101499.
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5. Conclusions
NIBS, particularly rTMS, significantly improved global cognition and
NPS, predominantly in the AD population. More tDCS and MCI studies
with larger sample sizes are needed to better evaluate the efficacy of
NIBS techniques in each patient population, and the optimal stimulation
type required to maximize beneficial outcomes.
Sources of Funding
The authors gratefully acknowledge support from The Canadian
Consortium on Neurodegeneration in Aging, The Canadian Institutes of
Health Research (of Neurosciences, Mental Health and Addiction),
Alzheimer’s Association Part the Clouds (PTCG-20–700751), and the
Sunnybrook Health Sciences Centre Department of Psychiatry.
CRediT authorship contribution statement
Johannes Teselink: Conceptualization, Investigation, Visualization,
Writing – original draft, Project administration, Software, Data curation,
Writing – review & editing.Kritleen K. Bawa: Investigation, Visualiza­
tion, Project administration, Writing – original draft, Data curation,
Software, Formal analysis, Writing – review & editing Grace Koo:
Investigation, Visualization, Data curation, Writing – original draft,
Formal analysis, Writing – review & Editing, Krushnaa Sankhe:
Investigation, Visualization, Data curation, Writing – original draft,
Writing – review & editing. Celina Liu: Conceptualization, Visualiza­
tion, Supervision, Writing – review & editing. Mark Rapoport:
10
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