Ageing Research Reviews 29 (2016) 66–89
Contents lists available at ScienceDirect
Ageing Research Reviews
journal homepage: www.elsevier.com/locate/arr
Review
Non-invasive brain stimulation of the aging brain: State of the art and
future perspectives
Elisa Tattia,∗
, Simone Rossia,b
, Iglis Innocentia
, Alessandro Rossia
,
Emiliano Santarnecchia,c,d,∗
a
Department of Medicine, Surgery and Neuroscience, Neurology and Clinical Neurophysiology Section, Brain Investigation & Neuromodulation Lab. (SiBIN
Lab), University of Siena, Italy
b
Department of Medicine, Surgery and Neuroscience, Human Physiology Section, University of Siena, Italy
c
Siena Robotic and Systems Lab (SirsLab), Department of Information Engineering and Mathematics, University of Siena, Italy
d
Berenson-Allen Center for Non-Invasive Brain Stimulation, Beth Israel Medical Center, Harvard Medical School, Boston, MA, USA
a r t i c l e i n f o
Article history:
Received 12 January 2016
Received in revised form 1 May 2016
Accepted 13 May 2016
Available online 21 May 2016
Keywords:
Healthy aging
Non-invasive brain stimulation
tDCS
rTMS
Cognition
a b s t r a c t
Favored by increased life expectancy and reduced birth rate, worldwide demography is rapidly shifting
to older ages. The golden age of aging is not only an achievement but also a big challenge because of
the load of the elderly on social and medical health care systems. Moreover, the impact of age-related
decline of attention, memory, reasoning and executive functions on self-sufficiency emphasizes the need
of interventions to maintain cognitive abilities at a useful degree in old age. Recently, neuroscientific
research explored the chance to apply Non-Invasive Brain Stimulation (NiBS) techniques (as transcranial
electrical and magnetic stimulation) to healthy aging population to preserve or enhance physiologicallydeclining
cognitive functions. The present review will update and address the current state of the art on
NiBS in healthy aging. Feasibility of NiBS techniques will be discussed in light of recent neuroimaging
(either structural or functional) and neurophysiological models proposed to explain neural substrates of
the physiologically aging brain. Further, the chance to design multidisciplinary interventions to maximize
the efficacy of NiBS techniques will be introduced as a necessary future direction.
© 2016 Elsevier B.V. All rights reserved.
Contents
1. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .67
1.1. What does “aging” mean to the brain? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
1.2. Structural and functional signatures of brain wrinkles. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .67
1.2.1. Structural changes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .67
1.2.2. Task-related functional activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
1.2.3. Resting-state functional connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
Abbreviations: APOE, apolipoprotein E; BOLD, blood oxygenation level dependent; CACT, computer-assisted cognitive training; cTBS, continuous theta burst stimulation;
COMT, catechol-O-methyltransferase; CRUNCH, compensation-related utilization of neural circuits hypothesis; DA, dopamine; DLPFC, dorso-lateral pre-frontal cortex; DMN,
default mode network; DMO, dextromethorphan; ECT, electroconvulsive therapy; EEG, electroencephalography; ERP, event related potential; fMRI, functional magnetic
resonance imaging; FTT, finger-tapping task; HAROLD, hemispheric asymmetry reduction in older adults; HERA, hemispheric encoding/retrieval asymmetry; HF-rTMS, highfrequency
rTMS; ICA, independent component analysis; IFG, inferior frontal gyrus; iTBS, intermittent theta-burst stimulation; JTT, Jebsen–Taylor hand function test; LTD,
long term depression; LTP, long term potentiation; MTL, medial temporal lobe; NiBS, non-invasive brain stimulation; NREM, non-rapid eye movement sleep; PARC, parietal
cortices; PASA, posterior–anterior shift in aging; PFC, prefrontal cortex; RSFC, resting-state functional connectivity; rs-fMRI, resting-state functional magnetic imaging; rTMS,
repetitive transcranial magnetic stimulation; STAC, Scaffolding theory of aging and cognition; tACS, transcranial alternating current stimulation; TBS, theta burst stimulation;
tDCS, transcranial direct current stimulation; tES, transcranial electrical stimulation; tRNS, transcranial random noise stimulation; vRT, verbal reaction times; WM, working
memory.
∗ Corresponding authors at: Department of Medicine, Surgery and Neuroscience, Neurology and Clinical Neurophysiology Section, Brain Investigation & Neuromodulation
Lab. (SiBIN Lab), University of Siena, Italy
E-mail addresses: elisatatti@msn.com (E. Tatti), esantarn@bidmc.harvard.edu (E. Santarnecchi).
http://dx.doi.org/10.1016/j.arr.2016.05.006
1568-1637/© 2016 Elsevier B.V. All rights reserved.
E. Tatti et al. / Ageing Research Reviews 29 (2016) 66–89 67
1.2.4. Electrophysiological changes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .69
1.3. Beyond a “wear-and-tear” conception of the aging brain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
2. Search strategy and inclusion criteria of the review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
3. NiBS techniques and neuroenhancement of the healthy aging brain. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .71
3.1. Repetitive transcranial magnetic stimulation (rTMS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
3.2. Theta-burst stimulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
3.3. Transcranial electrical stimulation (tES) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
3.3.1. Transcranial direct current stimulation (tDCS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
3.3.2. Motor functioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
3.3.3. Language. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .77
3.3.4. Jogging elderly’s memory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
3.3.5. Other cognitive domains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
3.4. Further nibs-techniques. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .80
3.5. Summary of the use of NiBS neuro-enhancement protocols in the elderly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
4. Future directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
4.1. Giving real-world impact to NiBS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
4.2. The right place at the right time. Best targets and parameters of NiBS in the aging brain. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .81
4.3. Connectome-based neuroenhancement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
5. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
Conflict of interests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
Statements of funding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
1. Introduction
1.1. What does “aging” mean to the brain?
Favored by reduced birth rate and increased life expectancy,
worldwide demography is rapidly shifting to older ages. According
to a recent report by the U.S. Census Bureau, the share of people
65 and older is projected to increase more than 150% in 35 years
(He et al., 2016). In line with this projection, from 562 millions of
people that were 65 years old or older in 2012, by 2050 the “golden
age” will include a population of 1.6 billion of elders. The “golden
age” is itself aging rapidly; increase in life expectancy will raise
the number of the oldest old from 126.5 million by 2015 to 446.6
million by 2050. This snapshot underlines that the golden age is
not only an achievement, but also a big challenge that our society
must face promptly, in order to shield self-sufficiency and wellbeing
of this growing proportion of population (Ballard, 2010). As
the 2015 United States of Aging survey reported (National Council
of Aging, 2016), “keeping the mind sharp” is the main concern of
32% of older Americans. Cognition, a broad term encompassing
abilities such as memory, attention, decision-making and problem
solving, is indeed a critical component in daily functioning
(Li et al., 2001; Seidler et al., 2010) and its age-related decline
is cause of concern not only for the elderly but also for their
families, the community and the public services. Indeed, aging to
senescence encompasses variable impairment in cognitive abilities,
especially for some aspects of memory and language, visuo-spatial
and executive abilities, and several intervention modalities have
been developed to stem it (Bamidis et al., 2014). Recently, neuroscientific
research explored the chance to apply Non-Invasive
Brain Stimulation (NiBS) techniques (as transcranial electrical and
magnetic stimulation) to the aging population.
The present review will overview current scientific evidences
on age-related cognitive and neurobiological changes to discuss
the feasibility of enhancing cognitive maintenance by means of
electrical current stimulation protocols.
1.2. Structural and functional signatures of brain wrinkles
1.2.1. Structural changes
Physiological brain structural and functional adapting brain
changes follow human development from birth to death and
characterize cognitive differences during lifespan. Neurobiological
changes particularly evident in the old age include: reductions
in neurotransmitter binding potential and synaptic receptor density
and efficacy (Morrison and Baxter, 2012), change in cortical
and cerebellar metabolism (Dukart et al., 2013), grey matter atrophy,
white matter loss and ventricular enlargement (Bennett and
Madden, 2013; Bolandzadeh et al., 2012; Scahill et al., 2003). As
Hedman et al. (2012) reported in their review of 56 longitudinal
MRI studies, a volume loss of more than 0.5% yearly occurs after age
60, a change that goes along with signs of demyelination, axonal
damage, decline in callosal volume (Abe et al., 2002; Nusbaum
et al., 2001; Pfefferbaum and Sullivan, 2003; Sullivan et al., 2010;
Vernooij et al., 2008), and ventricular enlargement, especially in
the frontal horns (Apostolova et al., 2012). Interestingly, ventricular
enlargement, white matter and grey matter structural changes,
which seem to follow an “anterior-posterior gradient” with an earlier
and stronger involvement of frontal regions, correlate with
decline in cognitive performance (Chou et al., 2009; Josephs et al.,
2008; Persson et al., 2006). In particular, prefrontal white matter
hyperintensities and grey matter shrinkage have been linked to
poorer executive functions, working memory abilities and speed of
information processing and have been independently correlated to
perseverative behavior (Gunning-Dixon and Raz, 2003). Also temporal
lobes are prone to moderate age-related changes; decrease
in the hippocampus volume has been largely reported in healthy
aging whereas the entorhinal cortex, whose volume decrease characterizes
Alzheimer’s disease, seems to be unaffected by healthy
aging (Raz et al., 1998).
1.2.2. Task-related functional activity
As shown by several neuroimaging studies of task-related brain
activity, brain structural changes are accompanied by modifications
of functional neural activation patterns in the elderly; these
evidences gave rise to a flurry of theories among which reduction
of task-related hemispheric lateralization (HAROLD model) is
one among the soundest, at least for the memory domain (for a
review on this topic see Cabeza et al., 2002). However, many cognitive
domains, such as attention and memory, executive functions,
perception and visuospatial abilities, are usually disrupted with
aging and these behavioral changes are also reflected by changes
detectable using functional imaging techniques such as functional
Magnetic Resonance Imaging (fMRI).
68E.Tattietal./AgeingResearchReviews29(2016)66–89
Table 1
Summary of studies using rTMS neuro-enhancement protocols in healthy aging.
Studies N Age Education Stimulated
area
Sham Localization
of the site
Type of
coil (mm)
Type and
frequency of
stimulation
Intensity Train
duration
Treatment
duration (d)
Online/
offline
Task Cognitive
domain
Effect Result
Vidal-Pi˜neiro et al. (2014) 24 71.75 ± 6.81 / left IFG sham device Neuronavigated
stimulation
with
stereotactic
registration
Double-70 iTBS
50 Hz
80%
AMT
3 pulse trains
repeated every
200 ms during
2”
/ Offline Encoding and
retrieval of
words
Episodic
memory
iTBS did not
produce
changes in
performance
in the memory
task.
Functional
brain
TMS-induced
changes were
found
specifically
when subjects
were
performing a
deep encoding
task in local
(left IFG) and
distal areas
(OC and
cerebellum)
No behavioral
effects
Kim et al. (2012) 16 63.13 ± 4.90 12.33 ± 3.67
rTMS group
11.50 ±3.46
sham group
left DLPFC (F3) Coil held per-
pendicularly
to the scalp
surface
International
EEG 10–20
system
Double-70 HF-rTMS
10 Hz
30% of
the maximal
stimulator
output (0.6T)
13 pulse trains
of
2” repeated
every 15” (780
pulses daily)
5 days Offline Stroop-task Inhibitory
control
Daily HF-rTMS
treatment over
the left
dorsolateral
prefrontal
region
improved
reaction times
for Stroop task
trials with
conflicting
information.
No effect on
accuracy rates
↓ RT with
conflicting
information
Cotelli et al. (2010) 13 70.2
range 65–78
13.8 l/r DLPFC 3-cm-thick
piece of
plywood
applied to the
coil on Cz
SofTaxic
Evolution
Navigator
system (V. 2.0)
Double-70 HF-rTMS
20 Hz
90% MT 500 msec / Online Action-naming
task
Language vRTs for
actions were
consistently
faster during
left and right
dorsolateral
prefrontal
cortex than
during sham
stimulation.
No significant
differences
were observed
for object
naming.
↓ RT for action
naming
E. Tatti et al. / Ageing Research Reviews 29 (2016) 66–89 69
For what concern episodic memory, Cabeza et al. (Cabeza et al.,
1997) showed that, differently from young adults which displayed
typically prevailing left prefrontal cortex (PFC) activity during
encoding and right PFC activity during recall (HERA model: Cabeza
et al., 1997; Nyberg et al., 1998; Rossi et al., 2002; Silveri et al., 2014;
Tulving et al., 1994), performance of the elderly was characterized
by a bilateral pattern of PFC activity during retrieval, namely a
reduction in hemispheric lateralization. Similar data were obtained
for other memory components, such as recognition (Grady et al.,
2002; Madden et al., 1999), incidental and intentional learning
(Stebbins et al., 2002; Logan and Buckner, 2001), and other domains
such as executive functions (Nielson et al., 2002; Reuter-Lorenz
et al., 2000) and perception (Grady and Craik, 2000; Grady et al.,
1994). Reduced hemispheric lateralization is actually one of the
most discussed evidence in cognitive aging research field as it
raised many conflicting hypotheses, some suggesting a failure to
allocate functional resource in a selective manner as it happens
in younger subjects and others interpreting it as a compensatory
attempt (Grady, 2008).
Another occurring neurophysiological modification encompasses
the increased recruitment of frontal areas to the
detriment of occipital activity, a phenomenon theorized in the
posterior–anterior shift in aging (PASA) model (Davis et al., 2008).
Far from being a task-difficulty related phenomenon (Davis et al.,
2008), neuroimaging studies widely reported PASA pattern across
different cognitive domains, including attention and working
memory (Madden, 2002; Cabeza et al., 2004; Grossman et al., 2002;
Rypma and D’Esposito, 2000), visual perception and visuospatial
processing (Grady and Craik, 2000; Grady et al., 1994; Huettel
et al., 2001; Iidaka et al., 2002; Levine et al., 2000; Meulenbroek
et al., 2004) as well as episodic memory (Cabeza et al., 2004,
1997; Daselaar et al., 2003; Dennis et al., 2007; Grady et al., 2002;
Gutchess et al., 2005). Considering that structural brain changes
seem to follow the opposite direction – namely affecting prefrontal
areas first – evidences of increased task-related prefrontal activation
need more in-depth analysis. As reported by Davis et al.
(2008), prefrontal activity could be interpreted as a functional reorganization
mechanism to overcome decreased sensory processing
abilities (Gazzaley et al., 2005; Park et al., 2004), a compensatory
role that has been observed also for parietal areas (Cabeza
et al., 2004; Huang et al., 2012). Some recent studies combining
structural and functional information found significant correlation
between increased activity in some regions and structural
preservation of other areas, especially for what concerns prefrontal
activity (Braskie et al., 2009; Rosen et al., 2005). Therefore, although
increased prefrontal local functional activity could be the result of
reduced structural integrity, grey matter integrity of other areas
(e.g. medial temporal lobes), as well as preservation of white matter
long-range connections, could mediate these effects and give them
different significance in terms of behavioral performance (Madden
et al., 2009; Maillet and Rajah, 2013).
In addition to the complex relationship between functional
overactivations, structural integrity and cognitive abilities in the
aging brain, some recent evidences suggested that task-related
compensatory attempts could be a response to altered resting-state
functional connectivity networks.
1.2.3. Resting-state functional connectivity
Beside task-based fMRI approach to cognitive aging, which
often faces practical constrains, a sharp interest in brain restingstate
functional connectivity (RSFC) sprang unprecedentedly (for
an extensive review on this topic, see Ferreira and Busatto, 2013). As
indicator of synchronization of low frequency oscillations (<0.1 Hz)
between different brain areas while not engaged in any particular
activity (Biswal et al., 1995) RSFC is a feasible and reliable
methodology to study cognitive changes in aging, allowing also
large-scale data sharing thanks to its great comparability across
different research samples (Ferreira and Busatto, 2013).
Among the multiple brain networks retrieved during rest (e.g.
primary sensorimotor network, primary visual and extrastriate
visual networks, fronto-parietal attentional networks and defaultmode
network), literature highlighted preferential age-related
decrements in the default-mode network (DMN) and dorsal attentional
network connectivity, which have been tightly linked to
attention, memory and executive functions (van den Heuvel and
Hulshoff Pol, 2010; Mevel et al., 2013). For what concerns the
DMN, a network that comprises resting activity of medial prefrontal
cortex, inferior parietal lobule, hippocampus and posteromedial
cortex, evidences report decreased connectivity within this functional
organization in old people when compared to young adults
(Esposito et al., 2008; Hafkemeijer et al., 2012; Mevel et al., 2011;
Prvulovic et al., 2011). More specifically, Independent Component
Analysis (ICA) and seed-based studies revealed a specific
vulnerability of medial prefrontal cortex, posterior cingulate cortex
(Batouli et al., 2009; Koch et al., 2010; Wu et al., 2011)
and anterior cingulate cortex connectivity (Esposito et al., 2008;
Koch et al., 2010) in aging population, a decrement that probably
begins already during the middle age (Evers et al., 2012). Interestingly,
evidences of increased DMN task-based connectivity suggest
that, although overall resting connectivity within this network is
decreased in older people, failure in suppressing DMN during cognitive
engagement could potentially explain cognitive impairment,
as well as trigger compensatory activations of other networks during
cognitive tasks. Moreover, the link between DMN disruption
and cognitive impairment is further supported by studies showing
that amyloid deposits in the brain are linked to reduced resting
DMN connectivity (Sheline et al., 2010).
For what concerns fronto-parietal connectivity, studies show
a significant increase in homotopic RSFC – a sign of reduced
hemispheric asymmetry (Grieve et al., 2005) – and a consistent
anterior-posterior cortical disconnection (Andrews-Hanna et al.,
2007; Bollinger et al., 2011; Campbell et al., 2012; Grady, 2012;
Grady et al., 2010; Kalkstein et al., 2011). Overall, these evidences
highlight that long-range connections seem to be more vulnerable
to aging than short-range connections (Tomasi and Volkow, 2012)
and support the “last-in-first-out” hypothesis, namely a greater
vulnerability of late-maturing regions of the brain to aging effects
(Grieve et al., 2005; Kalpouzos et al., 2009; Terribilli et al., 2011).
Intriguingly, evidences of increased dorsal fronto-parietal network
and decreased ventral connectivity during executive tasks posit
some food for thought on the balance between resting and taskrelated
networks; given the reported anticorrelations between
DMN and task-positive networks, it may be that resting disruption
of DMN could turn into a failure to downregulate it during cognitive
tasks, thereby overactivating task-positive fronto-parietal attentional
networks to gain greater cognitive control (Reuter-Lorenz
et al., 2010).
1.2.4. Electrophysiological changes
A necessary step toward a greater understanding of the functional
role of resting and task-related functional activity in the aging
brain encompasses the study of the relationship between BOLD signal
fluctuations and the real language of neurons, namely brain
electrical activity in the form of oscillatory patterns and evoked
potentials.
Quantitative analysis of electroencephalography (EEG) is indeed
a useful approach to assess neurophysiological changes occurring
in aging; this is especially true if we consider the role of oscillatory
brain activity in the transfer of information across the brain, in neuroplastic
processes and in overall cognitive functioning (Kahana,
2006). Oneof themostreportedEEGcorrelateof physiologicalaging
is a prominent background EEG slowing (Vlahou et al., 2014) but, as
70 E. Tatti et al. / Ageing Research Reviews 29 (2016) 66–89
we will discuss, the direction of the changes in specific brain oscillatory
patterns and their overlap with pathological aging is still
mixed up.
Overall, resting alpha power (8–13 Hz) decrease has been consistently
reported and associated to an increased spectral power in
slower delta (1–3 Hz) and theta (4–7 Hz) frequency bands (Finnigan
and Robertson, 2011). A recent cross-sectional study added specific
topographic locations to these changes, reporting linear and
non linear changes in parietal, temporal and occipital alpha and
in occipital delta amplitude (Babiloni et al., 2006). Modulated by
thalamo–cortical and cortico–cortical interactions, resting alpha
rhythm is the dominant resting oscillation in the adult awake
brain and plays different key roles in the coordination of brain
networks; lower alpha frequency band (8–10.5 Hz) seems to be
related to the global level of attentional readiness and to long-range
connectivity, whereas upper alpha waves seem implicated in the
transmission of sensorimotor information as well as to semantic
memory retrieval (Lizio et al., 2011). Furthermore, alpha power has
been negatively associated to global gray matter and hippocampal
atrophy in MCI and AD patients, thereby suggesting the existence
of a continuum from healthy aging to Alzheimer’s disease and that
the characterization of alpha amplitude and coherence could be
further investigated to improve differential diagnosis (Bian et al.,
2014).
For what concerns increased delta (1–3 Hz) and theta (4–7 Hz)
power, reports are quite inconsistent, at times reporting a power
increase (Dujardin, 1994; Dujardin et al., 1995; Klass and Brenner,
1995) at others a decrease (Klimesch, 1999; Stomrud et al., 2010). As
Finnigan and Robertson (2011) suggested, theta frequency increase
could be a consequence of the slowing of the resting alpha peak
and could be considered as a candidate biomarker of the aging
brain. However, as Vlahou et al. (2014) commented in his work,
higher theta power not associated with alpha slowing could be
interpreted as a marker of healthy aging per se. This latter suggestion
seems to be confirmed by a study by Grunwald et al. (2001),
which found a negative linear correlation between theta power and
hippocampal volume reduction. Moreover, behavioral significance
of these changes are still controversial (Finnigan and Robertson,
2011; Prichep et al., 2006). In a recent study, Vlahou et al. (2014)
tried to address this issue by investigating associations between
changes in oscillatory activity and cognitive performance. Results
showed that healthy aging was associated with a widespread linear
decrease of slow wave power (<7 Hz) and that the maintenance
of a higher resting delta and theta power was positively correlated
with cognitive performances across different domains.
Taken together, these patterns of changes in slower oscillatory
activity could be considered as a compensatory attempt to control
information processing in order to accomplish task demands.
Nevertheless, a clearer characterization of the relationship between
cognitive performance and resting EEG activity on the continuum
from healthy aging to dementia is needed.
1.3. Beyond a “wear-and-tear” conception of the aging brain
Given the correlational, rather than causal, nature of the neuroimaging
and neurophysiological studies above mentioned, one
still burning question about age-related cognitive and neurophysiological
modifications is related to the actual meaning of
these functional changes, namely, whether they reflect signatures
of brain inefficiency, an adaptive compensatory attempt (Grady,
2008), or both. Far from being a definite effective countermove to
aging processes, specific patterns of activation in the elderly aging
brain could be non influential or even detrimental to cognitive performances
(Fabiani et al., 1998; Stevens et al., 2008). In a classic
study by Schacter et al. (1996), for instance, increased posterior
PFC activity in elderly during memory retrieval was not associated
with better performances. This result, interpreted as an effect of an
inefficient phonology-based strategy to retrieve items, tap into the
hypothesis that some compensatory attempts reflect nonspecific
or less efficient recruitment of neural resources during task performance
(Baltes and Lindenberger, 1997; Grady, 2008). Known
as the “Dedifferentiation theory of neural processing” (Li et al.,
2010), this concept is rooted in the idea that brain functional differentiation
during human development is reversed in the elderly,
an interpretation supported by evidences of stronger correlations
both among cognitive measures and between cognitive and sensory
functioning measures (Reuter-Lorenz et al., 1999). Irrespective of
the considered model of brain aging, a fruitful avenue to demonstrate
whether the different “activations” of the aging brain are
necessary to keep on a satisfactory performance is to target these
regions with repetitive transcranial magnetic stimulation (rTMS),
which transiently disrupts neural activity (Rossini et al., 2015).
Using this approach, Rossi et al. (2004) showed that both PFCs
contributed equally to retrieval processes in the elderly, according
with the HAROLD model, while encoding processes remained
left-lateralized for the whole life span.
On the basis of recent literature, therefore, a wear-and-tear
conception of the aging brain sounds too hasty: far from being a
bare reverse developmental process, aging encompasses dynamic
and plastic modifications aimed at preserving cognitive functioning
despite the impairment of usual neural resources. Hence, an overloaded
neural system attempts an active compensation through
the recruitment of reserve neural resources. As the “Scaffolding
Theory of Aging and Cognition” expresses (STAC – Park and
Reuter-Lorenz, 2009), compensation activity in the aging brain
subsumes some neuroplasticity mechanism that sustains cognitive
functioning by boosting activity in task-related brain regions and/or
engaging alternative brain regions through functional connectivity.
Attempted compensation seems to have a complex asymptotic
or even inverted-U relationship (Cabeza and Dennis, 2012) both
with brain decline and task demands, such that it will be greater
with higher levels of brain impairments and task demands but
will relentlessly show no change, or even a decrease, once neural
resources reach their plateau. This effect is well described
by applying the “Compensation-related utilization of neural circuits
hypothesis” (CRUNCH – Reuter-Lorenz et al., 2008) to the
elderly. As many studies reported, older adults tend to recruit
additional areas at lowers levels of task demands, wearing out
compensatory resources to meet higher levels of task difficulty
and, consequently, impairing their performances (Reuter-Lorenz
et al., 2008). Literature reports solid evidences of the compensative
nature of age-related brain activations: HAROLD and PASA
patterns described before, for example, were found positively correlated
with task performances (Cabeza et al., 2002; Davis et al.,
2008). Moreover, also large functional networks are involved in
age-related compensatory activity: by applying a split-field matching
paradigm (Banich and Belger, 1990) to a semantic memory
task, Davis et al. (2012) found a stronger functional connectivity
between left and right PFC in elderly and a positive relationship
between increased cross-hemispheric connectivity and behavioral
performance in bilateral presentation condition. Despite evidences
of the existence of non-beneficial large scale functional connectivity
changes (Andrews-Hanna et al., 2007; Rajah and McIntosh,
2008), this study is in line with findings indicating that cognitive
performances of the elderly could be sustained not only by
increased local activity but also by large scale networks changes,
such as prefrontal-medial temporal lobe (MTL) connectivity during
memory tasks (Daselaar et al., 2006; Gong et al., 2009; Li et al.,
2010; Meunier et al., 2009; St Jacques et al., 2009; Wang et al.,
2010) and frontal and occipital-parietal phase locking in high beta
band (21–30 Hz), which seems to compensate reduced alpha power
during selective attention tasks (Geerligs et al., 2012).
E. Tatti et al. / Ageing Research Reviews 29 (2016) 66–89 71
In light of these evidences, the large behavioral variability
observed in the elderly could be explained in terms of the
ratio between attempted and successful recruitment of cognitiverelated
or unrelated brain areas and networks to keep up cognitive
performances, raising the possibility of efficiently addressing these
efforts through the identification of predictors and the relative
creation of proper interventions. Recently, in the wake of the
positive results on healthy young and neuropsychiatric patients
(Miniussi et al., 2008; Nitsche et al., 2008), rising efforts have been
made to test the chance of enhancing cognitive physiologicallydeclining
functions in the aging brain through non invasive brain
stimulation (NiBS). The term NiBS encompasses different methods
aimed at inducing transient changes in brain activity and, in
turn, altering behavioral performance, by means of electrical currents
or magnetic pulses applied on the scalp; as we will describe
in greater detail in the following paragraphs, different types of
stimulation (transcranial magnetic stimulation, TMS; transcranial
alternating current stimulation, tACS; transcranial direct current
stimulation, tDCS; transcranial random noise stimulation, tRNS),
each one affecting neuronal activity with different mechanisms,
are used in cognitive neuroscience. An emerging body of research
mostly based on two non invasive brain stimulation techniques,
tDCS and rTMS, is offering promising evidences on the feasibility of
applying these methods to aging population. Therefore, the present
review updates and discusses present state of art on NiBS in healthy
aging to enhance cognitive functioning.
2. Search strategy and inclusion criteria of the review
Potentially relevant papers have been retrieved by performing
a PubMed database search without temporal restrictions. In the
attempt of focusing the search on healthy elderly population, the
terms “aging”, “ageing” or “elderly” were combined with terms
such as “healthy”. To specify the object of the present review, these
terms were combined with terms such as “repetitive transcranial
magnetic stimulation”, “transcranial direct current stimulation”,
“transcranial alternating current stimulation” (a transcranial electrical
stimulation based on the application of oscillatory potentials
aimed at modulating intrinsic brain oscillatory activity) (Groppa
et al., 2010), “transcranial random noise stimulation” (a technique
based on an increase of cortical excitability levels through the
injection of pseudo-random electrical patterns which alter the
signal-to-noise ratio of targeted brain region) (Terney et al., 2008)
and related acronyms (respectively rTMS, tDCS, tACS, tRNS). The
searches for study population and research topic were combined
with AND, resulting in the final search. Results on Google Scholar
and Science Direct databases and references of retrieved researches
were examined for relevant publications too. We intentionally
excluded studies that only focused on elderly patients with a psychiatric,
neurological or neurodegenerative disease, in which NiBS
was used as a neuromodulatory treatment option, as well as those
studies applying NiBS techniques to investigate neurophysiological
mechanisms of the aging brain. The final selection comprised
3 studies related to rTMS, 17 studies related to tDCS and 1 study
related to tRNS. No studies related to tACS and healthy aging were
retrieved on online databases.
For each transcranial Electrical Stimulation (tES) study, we
retrieved: (i) number of subjects, (ii) mean age, (iii) mean education,
(iv) experimental design (i.e. type of design-within/between
subjects, single/double blind, sham controlled, follow up), (v)
NiBS specifics (i.e. electrode size, target electrode, reference electrode,
intensity, duration, online/offline stimulation), (vi) cognitive
task specifics, (v) main results. For rTMS literature, we extracted:
(i) number of subjects, (ii) mean age, (iii) mean education, (iv)
experimental design (i.e. type of design-within/between subjects,
single/double blind, sham controlled, follow up), (v) NiBS protocol
specifics (i.e. Sham method, stimulation site localization method,
coil type, type of stimulation and related intensity, stimulation
duration), (vi) task specifics, (v) main results. A schematic overview
of the results of the reviewed studies is shown in Tables 1 and 2.
3. NiBS techniques and neuroenhancement of the healthy
aging brain
3.1. Repetitive transcranial magnetic stimulation (rTMS)
First introduced by Barker et al. in 1985 (Barker et al., 1985),
TMS is a non-invasive technique to stimulate the brain. Based on
Faraday’s principle of electromagnetic induction, TMS discharges
brief (200–300 ␮s) and powerful (0.2–4.0 T) magnetic pulses tangentially
to the skull, creating a secondary electrical current that
abruptly changes the excitability of the underlying neurons, thus
inducing neuronal firing. Trains of single TMS pulses (up to a 100-Hz
repetition rate) with a constant frequency and intensity characterize
repetitive transcranial magnetic stimulation (rTMS). According
to the frequency of stimulation, rTMS induces after effects on cortical
excitability, mainly impacting on synaptic efficiency, that may
result in a net inhibitory effect when delivered at low frequency
(1 Hz or less) or an excitatory one, when delivered at high frequency
stimulation (≥ 5 Hz) (Rossi et al., 2009). Long Term Depression
(LTD) or Long Term Potentiation (LTP) are the synaptic mechanisms
invoked to explain such lasting effects (Cooke and Bliss, 2006).
The characteristics of the rTMS/TMS induced magnetic field can
be shaped by different types of coils, making it more focal (as in
the case of a figure-eight coil) or reaching greater depths (as in
the case of deep TMS coils, e.g. the double cone or the H-coils).
Despite the use of deep TMS coils could reach potentially harming
depths up to 6 cm (Deng et al., 2013), TMS is usually restricted to
superficial cortical targets, at a depth of 2–3 cm beneath the scalp.
Noticeably, stimulation-induced effects of TMS and rTMS largely
depend not only by “technical” features such as device characteristics
(e.g. type of coil), parameters set (e.g. intensity and frequency)
and experimental procedure (e.g. coil orientation, on-line/off-line
pulses discharge, respectively during or before a task) (Thut and
Pascual-Leone, 2010); subject-related variables, including statedependency
(level of neural activity during stimulation), age and
eventual pharmacological treatments (Miniussi et al., 2010; Rossini
et al., 2010; Silvanto et al., 2008) also affect the efficacy of rTMS
stimulation. Nonetheless, some major issues make sometimes difficult
to disentangle between real and placebo-mediated effects of
rTMS protocols; clicking sounds produced by the current flow in the
TMS coil, sensory stimulation of the skin and muscular twitches
of the face due to direct stimulation of trigeminal and facial nervous
fibers could indeed influence task performances and lead to
confounding results. One approach to deal with these effects is to
hold the coil in a perpendicular orientation: this resembles similar
sensory side effects of the active TMS stimulation but do not
ward off any possible residual brain stimulation and, of course,
limits the blinding of participant and researcher. Conversely, sham
TMS coils, which implement a magnetic shield to prevent neural
stimulation, strongly attenuate skin and muscles stimulation
and require additional surface electrodes to proper control for sensory
effects (Duecker and Sack, 2015; Rossi et al., 2007). Alongside
issues related to TMS control conditions, the need of MRI scans
and neuronavigation systems to precisely target brain areas makes
the implementation of TMS protocols less handy than other NiBS
techniques.
Despite these potential limitations, a large body of placebocontrolled
evidence reports cognitive improvement after high
frequency rTMS in healthy young adults and in patients with psy-
72E.Tattietal./AgeingResearchReviews29(2016)66–89
Table 2
Summary of studies using tES neuro-enhancement protocols in healthy aging.
Studies N Age
(mean ± SD years)
Education Experimental
design
Conditions Electrode size Target
electrode
Reference
electrode
Intensity/
duration
Task Cognitive
domain
Online/
Offline
Effects Follow up Result
Ross et al.
(2011)
14 65 / Single-blind
crossover
sham
controlled
a-tDCS s-tDCS 7 × 5 cm ATL (T3/T4) Contralateral
cheek
1.5 mA
15 min
Naming of 130
photographs
of famous
people and 99
photographs
of famous
landmarks
Language Online Left tdcs increases
naming
performance for
famous people.
Only in cases in
which the names
were not
immediately
available. Right
a-tdcs improves the
ability to recall
names of places
No ↑ accuracy left
a-tDCS for
people naming
↑ accuracy
right a-tDCS
for places
naming
Meinzer et al.
(2013)
20 68.0 ± 5.7
range 60–76
/ Crossover
sham-
controlled
a-tDCS s-tDCS 5 × 7
10 × 10 cm
(reference)
Left ventral
IFG (F5)
Right
supraorbital
region
1 mA
20 min
Semantic
word-retrieval
task
Language Online (fMRI) Older participants
produced
significantly
fewer errors during
a-tDCS compared
with sham
participants. No
effect on RT
No ↑ Accuracy
l-IFG
Meinzer et al.
(2014)
18 68.38 ± 5.15 15.88 ± 4.74 Withinsubject
triple
cross-over
sham
controlled
a-tDCS c-tDCS
s-tDCS
7 × 5 cm Primary motor
cortex (C3)
Right
supraorbital
region right
motor
cortex(c4)
1 mA
30 min
Semantic
word-retrieval
task
Language Online Both active tdcs
conditions
significantly
improved
word-retrieval
compared to
sham-tDCS.
No ↑ Accuracy
l-a-tDCS
Fertonani et al.
(2014)
20 66.5± 5.5 10.5 Crossover
single-blind
sham
controlled
a-tDCS s-tDCS 7 × 5 cm Left dlPFC (F3) Right shoulder 2 mA
4–5 min for
online a-tDCS
10 min offline
a-tDCS
6–7 min
s-tDCS
Picture-
naming
task
Language Offline/online tDCS improved
naming
performance,
decreasing the
verbal reaction
times only if it was
applied online.
No ↓ Verbal RT
online a-tDCS
Hummel et al.
(2010)
10 69 ± 9.24
range 56–87
/ Counterbalanced
double-blind
design sham
controlled
a-tDCS s-tDCS 5 × 5 cm Hand knob
area of the left
M1
Contralateral
supraorbital
region
1 mA
20 min
Jebsen–Taylor
Task (JTT)
Motor Offline Significant
improvement in JTT
function with tDCS
relative to sham
that outlasted the
stimulation period
by at least 30 min
No ↓ JTT time
a-tDCS
Zimerman
et al. (2013)
10 a-tdcs 5
c-tdcs
68.5 ± 3.2
a-tdcs
71.2 ± 1.9
c-tdcs
/ Double-blind,
sham-
controlled,
cross-over
a-tDCS s-tDCS 5 × 5 cm C3 Contralateral
supraorbital
region
1 mA
20 min
Finger-
Tapping
Task
Motor Online Improvements
when training was
applied
concurrent with
a-tDCS, with effects
lasting for at least
24 h
90 min
24 h after
↑ Accuracy
online a-tDCS
Lindenberg
et al. (2013)
20 68.2 ± 5.0
range 61–77
/ Crossover
sham
controlled
dual/anodal/
sham tDCS
7 × 5 cm
10 × 10 cm
(c-tDCS)
C3 C4 (dual tDCS)
contralateral
supraorbital
region
1 mA
30 min
Choice
Reaction Time
Task
Motor Online (during
fMRI)
No effect of
stimulation
condition on the
number of errors or
reaction times;
Task-related fMRI
revealed dual but
not anodal tDCS
enhanced
connectivity of the
left dorsal posterior
cingulate cortex.
Furthermore, dual
tDCS yielded
stronger activations
in bilateral M1
compared with
anodal tDCS when
participants used
either their left or
right hand
during the motor
task.
No No behavioral
effects
E.Tattietal./AgeingResearchReviews29(2016)66–8973
Parikh and
cole (2014)
8 75 ± 8 range
63–84
/ Single-blind
crossover
sham
controlled
a-tDCS, s-tDCS
+
motor practice
(MP)
on the
Pegboard test
5 × 5 cm M1 (found
with TMS)
Right
supraorbital
region
1 mA
20 min
Key-slot task
tactile
sensibility test
isometric task
grooved
pegboard
Motor Online/Offline Practice
improved
performance on the
pegboard test, and
a-l tDCS +MP
improved
retention of this
performance gain
when tested 35 min
later, whereas
similar
performance gains
degraded in the
sham group after
35 min.
No Grooved
pegboard and
key slot task ↓
time
a/s-tDCS + MP
isometric
precision grip
task ↑ grip
force
variability
with a-tDCS
(negative)
Flöel et al.
(2012)
20 62.1 ± 9.2
range 50–80
13.2 ± 2.3 Double-blind
design sham
controlled
a-tDCS s-tDCS 7 × 5 cm
10 × 10 cm
(reference)
Right tem-
poroparietal
area (T6/P4)
Contralateral
supraorbital
region
1 mA
20 min
Object-
location
learning task
Memory/learningOnline atDCS did not alter
the learning
rates on an
object-location task
in elderly subjects.
However, delayed
free recall was
significantly
improved after
atDCS.
1 week ↑ Accuracy
delayed free
recall
Manenti et al.
(2013)
32 67.91 ± 4.72 10.75 ± 4.63 Between
subjects
single-blind
sham
controlled
a-tDCS s-tDCS 7 × 5 cm F3/F4
PARC
(P7/P3, P8/P4)
Contralateral
supraorbital
area
1.5 mA
6 min
COLFIS
encoding and
recognition of
abstract and
concrete
words
Episodic
memory
Online Only tDCS applied
over the
left regions (dlPFC
and PARC)
increased retrieval
in older sub-
jects
No ↑ Accuracy
a-tDCS on left
dlPFC and
PARC
Sandrini et al.
(2014)
36 67.17 ± 3.68 12.05 ± 4.40 Between
subjects
single-blind
a-tDCS s-tDCS 7 × 5 cm F3 Right
supraorbital
area
1.5 mA
15 min
COLFIS
encoding and
retrival of
abstract and
concrete
words
Episodic
memory
Offline A-tDCS over the left
dlPFC
(i.e., with or
without the
reminder)
strengthened
existing verbal
episodic memories
and
reduced forgetting
compared to sham
stimulation
48 h 1 month ↑ Accuracy
a-tDCS
Berryhill and
Jones (2012)
25 63.7
range 56–80
High = 16.9
Low = 13.5
Crossover
sham
controlled
a-tDCS s-tDCS 7 × 5 cm F4/F3 Contralateral
cheek
1.5 mA
10 min
Visual
and verbal
2-back WM
tasks
WM Offline Improvement in
WM performance
selectively in
participants with
higher education. In
the low education
group, tDCS,
especially to the
right PFC, impaired
visual WM
performance but
had no effect on
verbal WM
No ↑ Accuracy in
higher
education
group ↓
Accuracy on
visual WM
performance
in low
education
group
Boggio et al.
(2010)
28 69.4 ± 8.9 r
tDCS
68.9 ± 12.6 ltDCS
67.0 ± 9.0
s-tDCS
9.2 ± 5.2
r-tDCS
7.0 ± 5.2 l-
tDCS
5.2 ± 4.5
s-tDCS
Double-
blinded
between-
subjects
sham-
controlled
l a/r c-tDCS
(N = 9)
r a/l c-tDCS
(N = 10)
s-tDCS (N = 9)
7 × 5 cm F3/F4 F4/F3 2 mA
10 min
Risk task Decision
making
Online Left anodal/right
cathodal
stimulation
increased risky
choices as
compared with
sham stimulation
and right
anodal/left cathodal
stimulation.
No ↑ Risky
decisionmaking
with
left a-tDCS
74E.Tattietal./AgeingResearchReviews29(2016)66–89
Table 2 (Continued)
Studies N Age
(mean ± SD years)
Education Experimental
design
Conditions Electrode size Target
electrode
Reference
electrode
Intensity/
duration
Task Cognitive
domain
Online/
Offline
Effects Follow up Result
Harty et al.,
2014
106 72.13 (6.0) a-r
tDCS
69.41 (4.3) a-l
tDCS
69.71 (4.2) c-r
tDCS
72.08 (5.7) a-r
tDCS
range: 65–86
14.92 (3.6) a-r
tDCS
13.04 (3.5) a-l
tDCS
14.58 (3.5) c-r
tDCS
14.17 (3.6) a-r
tDCS
Single-blind
crossover
a-tDCS c-tDCS 7 × 5 cm F4/F3 Cz 1 mA
5 × 7,5 min
EAT Go/No-go
response
inhibition task
Cognitive
control
Online Anodal tDCS over right dlPFC
was associated with a
significant increase in the
proportion of performance
errors that were consciously
detected. No such
improvements were observed
with l a-tDCS
No ↑ Accuracy in
error detection
Manor et al.,
2016
37 61 ± 5 / Double-
blinded
Crossover
design
a-tDCS
s-tDCS
7 × 5 cm F3 Right
supraorbital
area
Individual
current
intensity
1.4 ± 0.4 mA
20 min
Single tasks:
−Walking
−Standing
−Counting
backward
while seated
Dual tasks:
−walking
while counting
backward
−standing
while counting
backward
Cognitive
control
Offline tDCS improved performance
in dual task conditions.
No effect on single task
conditions.
No ↓ dual task
costs
tDCS multisession
+ ct
N Age (mean
±sd years)
Education Experimental
condition
Conditions Electrode
size (cm)
Stimulated
areas
Reference
electrode
Intensity/
duration
Task Cognitive
domain
Online/
offline
Effects Follow up Result
Park et al.
(2013)
40 69.7
tDCS group
70.1 ± 3.4
sham group
69.4 ± 3.1
tDCS group
10.9± 4.6
sham group
10.9 ± 4.2
Between
subject design
sham
controlled
a-tDCS s-tDCS
10 sessions
a-tDCS/s-tDCS
combined
with CACT
cognitive
training
5 × 5 cm F3/F4
(bilateral
anodal
stimulation)
Non-dominant
arm
2 mA
30 min
Verbal 2-back
WM Digit Span
Test Verbal
Learning Test
Visual Span
Test
Visual
Learning Test
Auditory
Continuous
Performance
Test (CPT),
Auditory
Controlled CPT
Visual CPT
Visual
Controlled CPT
Word–Color
Test Trail
Making Test
WM, STM Offline The accuracy of the verbal
working memory task and
digit span forward test were
significantly improved with
a-tDCS as compared with
s-tDCS.
The effect lasted for 4 weeks
in the verbal WM
task.
7 and 28 days
after
↑ accuracy
verbal WM
and digit span
↓ RT in WM
Jones et al.
(2015)
72 64.38 ± 5.08
Range: 55–73
/ Between
subject design
Sham
controlled
a-tdcs
sham
no-training
group
4 groups10
consecutive
weekday
sessions + WM
training
5 × 7 cm a-tdcs F4
a-tdcs P4
alternating
a-tdcs F4-P4
Contralateral
cheek
1.5 mA
10 min
Trained:
Visuospatial
WM
Automated
Operation
Span
Transfer:
Digit Span
Stroop Task
Spati al 2-back
WM Offline All the groups showed
improvements ontrained and
transfer
No difference between F4 and
P4 a-tDCS
At follow up only participants
in the a-tDCS group
maintened the results
1 month ↑ accuracy
tRNS
multisession
+ ct
N Age Education Experimental
condition
Conditions Electrode
size (cm)
Stimulated
areas
Reference
electrode
Intensity/
duration
Task Cognitive
domain
Online/
offline
Effects Follow up Result
Cappelletti
et al., 2015
30 65.5 range:
60 −73
/ Single-blind
between
subject design
sham
controlled
t-RNS 7 × 5 cm P3/P4 C3/C4 / 1 mA
20 min
Numerosity
discrimination
task
Number acuity Online Numerosity discrimination
improved after training and
was maintained long term (16
weeks). Effects were
prominent in P3/P4 tRNS
condition. Transfer effects to
untrained tasks probing
inhibitory ability were found.
7 days and 16
weeks after
↑ Accuracy in
number
discrimation
E. Tatti et al. / Ageing Research Reviews 29 (2016) 66–89 75
Fig. 1. Brief overview showing the status of the literature about rTMS-based protocols for cognitive neuroenhancement in aging population. Panel (A) type of rTMS protocol
adopted; Panel (B) Comparison between number of NiBS studies published on elderly with neurological/neuropsychiatric diseases and those on healthy aging; Panel (C) % of
studies employing an Online or an Offline design.
chiatric/neurological diseases (Guse et al., 2010; Lefaucheur et al.,
2014; Miniussi et al., 2008). Regarding aging population, greater
efforts have been spent in the investigation of rTMS potential in
the treatment of cognitive symptoms of neurodegenerative diseases
and in the treatment of late-life depression (see Fig. 1, Panel B
for a comparison between rTMS applications in aging population).
Despite proofs of its feasibility in cognitive neurorehabilitation,
TMS literature on healthy aging population is mainly based on “virtual
lesion” approach (Pascual-Leone et al., 2000; Walsh and Cowey,
2000; Walsh and Rushworth, 1998; Rossini et al., 2007), namely
attempting to find causal nexus between functional activity and a
particular cognitive function, and on the impact of TMS on cortical
excitability of the aging brain.
As we found in online databases, three studies investigated the
potential of rTMS in enhancing cognitive functioning of the healthy
elderly (Table 1); one elegant study by Cotelli et al. (2010) extended
their previous study on the neuroenhancement of naming ability in
Alzheimer’s disease (AD) (Cotelli et al., 2008, 2006) in a group of 13
healthy elderly subjects. They applied 20 Hz rTMS during an actionnaming
task and found reduced verbal reaction times (vRT) for
action naming during both left and right dorsolateral prefrontal cortex
(DLPFC) stimulation. Differently from previous results with AD
patients, in which also an improvement in accuracy was found, the
only vRTs improvement in healthy aging shown in this study was
explained in terms of “ceiling” performances”, an effect that could
hide beneficial rTMS-effects on accuracy in healthy aging population.
A similar result was obtained in another high-frequency rTMS
(HF-rTMS) study on inhibitory control (Kim et al., 2012). A five days
10 Hz HF-rTMS treatment over the left DLPFC was conducted in
16 healthy elderly females. A significant positive improvement in
reaction times in Stroop task trials with conflicting information was
found, a result in line with earlier findings on elderly clinical population
(Boggio et al., 2011; Moser et al., 2002; Rektorova et al.,
2005). Most notably, given that the cognitive task was performed
the day after stimulation, this result stands for the possibility of
eliciting long-lasting effect in the aging brain. Recently, the idea
that multiple HF-rTMS sessions could elicit longer lasting cognitive
effects (Eisenegger et al., 2008) has been systematically reviewed,
providing sound evidence that multiple sessions (10–15) of HFrTMS
(10–20 Hz) applied over the left DLPFC is most likely to cause
significant neuroenhancement (Guse et al., 2010). Despite the work
by Kim et al. designed only five stimulating sessions with no follow
up, this study confirmed that multiple HF-rTMS trains applied
to DLPFC are able to induce an effect that lasts longer than single
session stimulation in the healthy aging brain.
3.2. Theta-burst stimulation
Recently, a new promising paradigm of rTMS has been introduced
to produce long lasting effects in neuronal activity in a
shorter time of intervention (i.e., few minutes): theta burst stimulation
(TBS) uses bursts of high frequency stimulation (3 pulses at
50 Hz repeated at 200 ms intervals) that could be applied intermittently
(iTBS) or continuously (cTBS) to induce, respectively, LTP and
LTD effects of synaptic activity (Di Lazzaro et al., 2008; Huang et al.,
2005). According to the model proposed by Huang et al. (2011),
both patterns of stimulation trigger mixed excitatory and inhibitory
effects whose sum produce different changes in cortical excitability
by the modification of synaptic strength. iTBS seems to elicit LTPlike
effects by keeping short-latency facilitation effects dominant
on the inhibitory ones; conversely, cTBS enhances longer-latency
inhibitory effects resulting in LTD-like effects (Suppa et al., 2016).
However, the mechanisms underlying cortical excitability changes
induced by TBS protocols are still controversial as some authors
refer to the modulation of NMDA receptors (Di Lazzaro et al., 2008;
Huang et al., 2005), others to GABAergic receptors (Harrington
and Hammond-Tooke, 2015; Thickbroom, 2007) and still others
hypothesize a modulation of the expression of transcription factors,
such as nerve-growth-factor-induced protein A (NGFI-A) (AydinAbidin
et al., 2008).
Evidences of the efficacy of TBS in the modulation of cognitive
functioning are in their infancy: based on the finding that inhibitory
cTBS is able to impair accuracy in working memory (Morgan et al.,
2013) and emotional control (Volman et al., 2011), neuroenhancement
of cognitive functions could be theoretically possible by using
excitatory iTBS paradigms. However, iTBS approaches aimed at
enhancing cognitive functions gave rise to still inconsistent results:
Restle et al. found speech repetition facilitation after iTBS (Restle
et al., 2012), but Läppchen et al. (2015) found surprisingly significantly
poorer motor performances in healthy young subjects
after daily applications of intermittent theta burst stimulation during
a four-day training program. The only study retrieved on the
application of iTBS on aging population (Vidal-Pi˜neiro et al., 2014),
assessed the effect of 600 iTBS pulses (3 pulses of 50 Hz at an intensity
of 80% AMT, repeated every 200 ms) over left inferior frontal
gyrus (IFG) on episodic memory. iTBS did not affect memory performance
but it influenced functional activity in a task-dependent
manner: when subjects were performing deep semantic encoding,
increased blood oxygenation level dependent (BOLD) signal
in local (left IFG) and distant related areas (occipital cortex and
cerebellum) was found. This result, discussed more in depth in Section
4.3, highlights that iTBS can modulate brain networks (Halko
et al., 2014). Lack in behavioral changes could be attributed to
unilateral IFG stimulation, instead. Based on theoretical models
discussed in the introductive part of the present review, the bilaterality
of task-dependent activity observed in healthy aging suggests
that bi-hemispheric stimulations could be more effective to induce
cognitive neuroenhancement in older subjects. This suggestion is
further motivated by some studies arguing on the possibility of
inducing bihemispheric plasticity by means of unilateral iTBS (Di
Lazzaro et al., 2008; Dickins et al., 2015). Indeed, the great individual
variability on the plasticity effects triggered by iTBS makes the
76 E. Tatti et al. / Ageing Research Reviews 29 (2016) 66–89
understanding of the effects of iTBS more difficult (Lopez-Alonso
et al., 2014). Therefore, further studies aimed at identifying the role
of intervenient variables-such as the interaction between attention
and age (Dickins et al., 2015) are needed.
3.3. Transcranial electrical stimulation (tES)
tES encompasses a series of techniques in which weak currents
are applied on the scalp to target cortical brain regions (Paulus et al.,
2013). Typical dose parameters of tES methods are: the number of
the electrodes, their shape, size and position, current waveform,
its density and stimulation duration. tES is typically applied on the
subject’s head by means of at least two surface electrodes soaked
with saline water or an electroconductive gel and linked to a current
waveform generator. Given the reduced electric conductivity
of the skull, which requires high voltages in order to depolarize
neurons (a painful procedure usually performed under anesthesia,
mostly known as electroconvulsive therapy-ECT), non-invasive tES
uses subthreshold electrical currents and ground their potential
on the capability of shifting intrinsic neuronal excitability rather
than eliciting neuronal firing (Radman et al., 2009; Paulus, 2011).
Indeed, excitatory tES has a bimodal polarization effect, that is cell
membrane depolarization limited to 2 mV (Fröhlich and Schmidt,
2013) and the hyperpolarization of apical dendrites (Bikson et al.,
2004). On the basis of the waveform of the electrical current and its
polarity, different non-invasive tES protocols can be implemented,
namely: transcranial direct current stimulation (tDCS), transcranial
alternating current stimulation (tACS) and transcranial random
noise stimulation (tRNS) (Nitsche et al., 2008; Paulus, 2011) (Fig. 2).
Compared to rTMS/TBS, low-intensity tES protocols have the
advantage of being handier and portable. Indeed, low costs associated
with the creation of tES protocols, make this technique more
feasible both at home or in clinical settings. TES produces only
mild transient adverse effects, such as itching, tingling, or reddening
of the scalp (Poreisz et al., 2007), and it allows a more reliable
sham condition than rTMS/TBS, thereby guaranteeing more robust
double-blind designs. However, data are still too few to discern
whether cost-effectiveness is in favor of rTMS or tES.
Despite these pros, it must be noted that tES methodic is less
accepted than rTMS in the clinical settings due to its lack in spatial
specificity (mostly related to the use of bipolar montages and bigsized
pad electrodes). In light of this major limit, some technological
advancement, such as the use of ring and concentric electrodes
(Datta et al., 2008; Nitsche et al., 2007) have been recently made;
moreover, computational forward models developed to predict
the electrical field induced by specific neuromodulation designs
(Bikson et al., 2012) are providing useful information to customize
electrode montages in order to reach higher spatial focality and to
gain insight on the neurophysiological effects of brain stimulation.
However, it must be mentioned that anatomical differences across
subjects could still account for differences in the induced electrical
field and, lastly, to different behavioral outcomes. For the purposes
of our review, this issue is particularly relevant; in line with the
observations made on tES in patients with stroke (Liew et al., 2014;
Wagner et al., 2007) and with the previously reviewed structural
changes in the aging brain (e.g. ventricular enlargement), the distribution
of the current applied could be significantly different in the
aging brains, a fact that should be taken into account in the design of
the electrode montage on aging population. Currently, more realistic
models accounting of anatomical differences have been created
and, probably, future studies on the effects of tES in healthy aging
should resort to individual MRIs in order to reach more controlled
neuromodulatory effects on the aging brain (Wagner et al., 2007).
Despite these limits, current evidences strongly support the
utility of tES interventions to enhance cognitive functions. In the
following paragraphs, specific tES methodic will be individually
reviewed in order to discuss their feasibility to enhance cognitive
abilities of the aging brain.
3.3.1. Transcranial direct current stimulation (tDCS)
tDCS is undoubtedly the most known and studied technique
of the tES realm. Based on the application of direct continuous
electric currents on the scalp, tDCS elicits both neuronal membrane
depolarisation or hyperpolarisation on the base of its polarity,
respectively anodal and cathodal (Liebetanz et al., 2002; Nitsche
et al., 2003, 2004, 2005; Priori et al., 1998) and in line the concept
of homeostatic metaplasticity (Siebner et al., 2004). So, in contrast
to TMS, which could elicit action potentials, tDCS can be considered
a pure neuromodulatory technique altering spontaneous
firing rate of neurons and synaptic responses to afferent inputs
(Miniussi and Ruzzoli, 2013). These neuronal changes are attributed
both to short-term modification of resting membrane potentials
and, in the case of long-term effect, to NMDA-dependent mechanisms
related to an increase in intracellular Ca2+ levels (Liebetanz
et al., 2002). In support to the role of NMDA-receptors in tDCS
after-effects, Liebetanz et al. (2002) showed that both anodal and
cathodal tDCS after-effects could be prevented by the intake of
the NMDA-receptor antagonist dextromethorphan (DMO), while
carbamazepine (a sodium-channel blocker) has similar suppressing
effects limited to anodal stimulation. Other pharmacological
approaches have also demonstrated that tDCS effects involve glutamatergic,
serotoninergic, GABAergic and dopaminergic system
modifications (see Mederos et al., 2012 for a review on neurobiological
mechanisms of tDCS).
According to these premises, tDCS could be an excellent protocol
to heal cognitive functioning of the elderly, promoting plastic
effects that could increase beneficial compensatory activity or
reduce inefficient neural activity. Broad attractively proofs were
still provided by research on healthy young adults and on pathological
conditions, where tDCS-based cognitive neuroenhancement
yielded to promising results in several cognitive domains (Brunoni
et al., 2012; Miniussi et al., 2008). In recent years, research on
tDCS effects on cognition of the elderly impressively increased.
Our online research on tDCS-based cognitive neuroenhancement
of healthy aging population resulted in 17 studies (Table 2).
3.3.2. Motor functioning
Based on a qualitative analysis, modulation of motor control is
currently the most studied aspect with tDCS in the aging brain.
This is not surprising, given the impact of motor function on daily
self-sufficiency of the elderly, and due to the fact that motor cortex
excitability has been the “historical” target to measure tDCS effects
(Bastani and Jaberzadeh, 2012). Although evidences on the neurophysiological
effects of tDCS are pivotal in the understanding of the
behavioral outcomes of tDCS-based interventions, the present section
will only focus on the possibility of enhancing motor abilities
in the elderly but we refer to the recent meta-analysis by Horvath
et al. (2015) for an extensive overview on this topic.
The first non-invasive neuromodulatory attempt on motor
functioning of healthy elderly is by Hummel et al. (2010). They
examined the effect of a single session of anodal tDCS (a-tDCS)
over the left primary motor cortex on a task that mimics activities
of daily living (Jebsen–Taylor hand function test-JTT; Jebsen
et al., 1969). They found that, compared to sham, the application
of a-tDCS (1 mA for 20 min) improved significantly JTT function, an
effect that outlived by approximately 30 min and that was most
prominent for fine manipulative motor tasks. An intriguingly evidence
of this study was that improvement was increasingly more
evident the older the subjects were, although this effect probably
just indicates that the oldest-old have greater leeway to improvement
because they are not able to reach ceiling performances.
Moving toward more complex forms of motor control, Zimerman
E. Tatti et al. / Ageing Research Reviews 29 (2016) 66–89 77
Fig. 2. Non-invasive tES paradigms. By means of the application of subthreshold direct currents, tDCS modulates spontaneous neuronal firing frequency eliciting both neuronal
membrane depolarisation or hyperpolarisation on the basis of its polarity (anodal stimulation of the left primary motor cortex is shown in the figure), tRNS encompasses the
application of random noise electrical patterns which alter the signal-to-noise ratio of targeted brain region and, supposedly, increase cortical excitability. Finally, tACS is
based on the application of oscillatory electrical potentials aimed at modulating intrinsic brain oscillatory activity.
et al. (2013) examined whether a-tDCS resulted in long-lasting
improvement of skilled motor performance on a finger-tapping
task (FTT), with 20 min of online stimulation during a motor
skill acquisition training being able to induce approximately 24 hlasting
effect on accuracy. Noticeably, the neuroenhancement
produced by this protocol did not literally improved motor ability
per se, but rather the retention of motor sequences. This evidence
supports the role of primary motor cortex in the encoding of motor
skills, thus the possibility of combining a-tDCS in primary motor
cortex with training to enhance motor functioning. Similarly, a
sham-controlled crossover study by Parikh and Cole (2014) further
tested the possibility of improving fine motor abilities through the
combination of a-tDCS of primary motor cortex and motor practice.
Results showed that, although a-tDCS did not immediately
affect performance on the Grooved Pegboard task, it produced a
longer retention of the improvement (at least 35 min) than motor
practice alone. Taken together, results by Zimerman et al. (2013)
and Parikh and Cole (2014) support the idea that, when combined
with motor training, short-term single session neuroenhancement
is able to produce lasting-effects in motor functioning of the elderly.
For what concern motor adaption, another ability usually declining
with aging (King et al., 2013), Hardwick and Celnik, (2014)
tested the possibility of stimulating the cerebellum to enhance
motor functioning in the elderly. Delivered via 5 × 5 cm electrodes
with an intensity of 2 mA, a-tDCS was applied for 15 min during
a “center out” reaching task. Compared to subjects who received
sham stimulation, participants who received the active condition
adapted faster to the introduction of a sudden 30◦ cursor rotation.
This evidence demonstrates that cerebellum may be a suitable target
to improve spatial readjustment during movements in aging
subjects. As studies currently reviewed performed, tDCS protocols
have been usually applied to a single hemisphere, using a “neutral”
reference electrode for cathodal stimulation (even though the
effect of scalp “return” electrodes should not be considered negligible
in tDCS protocols in general). Given complex bi-hemispheric
compensatory activity occurring with aging, an intriguingly possibility
is to test cognitive effects of dual stimulation in both
hemispheres. Lindenberg et al. (2013), made this attempt comparing
the effect of unilateral and bilateral primary motor cortex
stimulation on functional brain activity and motor behavioral performance.
For unilateral a-tDCS, the anodal electrode was placed
on left M1 and the cathodal on the contralateral supraorbital area.
For dual tDCS, the cathodal electrode was placed over right M1.
Although no behavioral improvement on finger tapping task was
detected both for dual and uni-hemispheric a-tDCS, task-related
activity resulted modified after bilateral stimulation; in particular,
dual tDCS on motor cortex enhanced activity in bilateral M1,
demonstrating its efficiency in the modulation of interhemispheric
activity. This evidence is particularly important because highlights
that the mechanism underlying tDCS effects encompasses not only
local polarity-related modifications of cortex excitability but also
complex interhemispheric connections. The fascinating possibility
of modulating brain networks indeed needs more in-depth analysis
and will be discussed as a future direction in Section 4.3.
3.3.3. Language
One of the most frequently and disabling complains of elders is
the difficulty in the retrieval of proper names and a certain slowing
of verbal fluency. Therefore, neuromodulation of language especially
focuses on the enhancement of naming ability. Despite the
presence of substantial literature on healthy adults and aphasic
patients (for a review on the topic see Monti et al., 2013), currently,
only four studies attempted to improve naming and verbal fluency
in healthy aging.
In 2011, Ross et al. tested the possibility of replicating tDCS
based facilitation on name recall for famous faces (Ross et al., 2010)
in the elderly. 15 min of a-tDCS with an intensity of 1.5 mA was
delivered over the right or left anterior temporal lobe (ATL) in a
group of 65 healthy elderlies; the cathode electrode was positioned
on the contralateral cheek. Stimulation was applied online, during
a naming task encompassing the presentation of photographs
of famous people or landmarks. Results reported different effects
on the base of the availability of the names, with tDCS facilitation
being more evident for names that were not immediately
accessible. Moreover, the target of the stimulation produced different
effects according to the type of the presented stimuli: left
a-tDCS tended to increase naming performance for famous people,
while right a-tDCS improved the ability to recall names of places.
Selectivity of the effects across stimulation site and stimulus type
demonstrated that tDCS-induced neuroenhancement was probably
not dependent by a general increase in cortical excitability,
in line with the notion that tDCS effects take place after the cessation
of stimulation rather than during it, at least for the motor
cortex (Santarnecchi et al., 2014). Meinzer et al. (2013) employed
a sham-tDCS controlled, crossover design to investigate wordretrieval
performance during a-tDCS and related brain activity. In
line with previous studies showing improved word retrieval after
inferior frontal gyrus (IFG) stimulation in younger adults and old
78 E. Tatti et al. / Ageing Research Reviews 29 (2016) 66–89
patients with stroke (Cattaneo et al., 2011; Holland and Crinion,
2012; Iyer et al., 2005; Meinzer et al., 2012), 1 mA direct current
was delivered for 20 min on the left ventral IFG, using right supraorbital
region as reference region. Moreover, fMRI was performed
both in resting and task related conditions to assess changes at
network level. Analysis revealed that 20 min of 1 mA direct current
delivered on the left ventral inferior frontal gyrus (IFG) was
capable of improving performance almost up to those of young
control group. Moreover, the combination between resting and
functional MRI and tDCS provided evidence of a negative correlation
between enhanced resting bilateral prefrontal connectivity
and behavioral performance; this connectivity pattern, evident also
during task-fMRI and supporting the dedifferentiation hypothesis
of the aging brain, was significantly reduced after a-tDCS. In
light of these results, a successive study by the group of Meinzer
et al. (2014) investigated the same ability comparing unilateral atDCS
and dual hemisphere tDCS while subjects were performing
semantic word-retrieval and motor speech tasks. In the attempt of
elucidating the elusive relationship between motor and language
systems (namely, whether motor cortex stimulation could result
in an improvement of speech production) anodal electrode was
placed over the left M1, using right M1 as cathodal reference in dual
stimulation condition. Results indicated that both a-tDCS and dual
tDCS alike reduced the number of errors made during the semantic
word-retrieval task, thus corroborating the opportunity of targeting
primary motor cortices to enhance semantic verbal production
well beyond their known impact on action words (Liuzzi et al., 2010,
2008). However, given that the size of the used electrodes limited
the spatial focality of the stimulation, the possibility that the highlighted
effects were elicited by excitability changes in neighbors’
areas (e.g. parietal) should be taken into account. For what concerns
task-related activity following a-tDCS, this study found no
modification of brain activity in world-retrieval trials but showed
a significant decrease in bilateral prefrontal connectivity in motor
speech trials. Together, these evidences (further discussed in Section
4.3) stress out the need of a proper assessment of the nature
of the increased bilateral connectivity in the aging brain to guide
stimulation designs.
Currently reviewed studies on language assessed cognitive performance
during the application of tDCS without follow up, an
approach that does not allow capturing possible long-term maintenance
of behavioral improvements. To uncover this possibility,
Fertonani et al. (2014) designed two stimulating conditions; in
the online paradigm, 2 mA t-DCS was applied on DLPFC during
the task, resulting in 4–5 min of stimulation, while in the offline
condition tDCS was applied for 10 min to assure an after-effect.
As hypothesized by the authors, in the aged group, only online atDCS
induced a significant improvement on picture-naming task in
terms of reduced reaction times, an effect that resulted inconsistent
in the healthy young control group. This effect is in line with
previous findings (Kuo et al., 2008; Stagg and Nitsche, 2011) and
could suggest that LTP-like neuronal plasticity mechanisms probably
induced by offline tDCS may be disrupted in the aging brain.
In conclusion, this study strengthens the importance of timing in
the neuroenhancement of the aging brain and the need of further
studies comparing online and offline stimulation in the same pro-
tocol.
3.3.4. Jogging elderly’s memory
Forgetfulness could be considered a frequent but not inevitable
part of aging processes. Variably defined as “Benign senescent forgetfulness”
(Kral, 1962) or “Age Associated Memory Impairment”
(Crook et al., 1986) by definition, physiological memory lapses have
little impact on daily living. Despite its “benign” nature, there may
exist a possible continuum between “senior moments” and objective
memory impairment (Reisberg and Gauthier, 2008). On this
basis, the possibility of preventing harming memory loss in healthy
aging population through early brain stimulation interventions
have been explored in 6 studies.
Lapse of memory for object-locations, such as forgetting where
an object was previously placed, is undoubtedly one of the mostly
reported and crippling issues in everyday life of elderly. Given the
relevance of this type of memory in daily living, Flöel et al. (2012)
attempted to neuroenhance object-location memory through the
delivery of a-tDCS over right temporo-parietal cortex. Anodal direct
current was administered with an intensity of 1 mA during objectlocation
learning paradigm, for an overall stimulation time of
20 min in 20 healthy aged people. After five learning blocks, where
subjects were asked to memorize buildings positions on a street
map, learning success was tested immediately and one week later
with a free recall task. Results indicated that the learning curve over
the blocks of the task did not differ between active and sham stimulation
conditions and that immediate free recall was not affected
by electrical stimulation. Despite this, compared to sham stimulation,
a-tDCS condition resulted in an impressive improvement in
accuracy one week later. These results may indicate that a-tDCS
could induce strong delayed effects through long-term consolidation
mechanisms (Reis et al., 2009). Anyway, the lack of control sites
in the experimental design could not disentangle whether only the
temporo-parietal site stimulation is critical in determining such
effects.
Moving toward broad episodic memory functioning, Manenti
et al. (2013) tested the possibility of improving retrieval of abstract
and concrete words in a group of 32 elderly individuals. Given
evidences of bilateral recruitment of PFC and the activation of parietal
cortices (PARC) during episodic memory tasks (Binder et al.,
2005; Klostermann et al., 2008), three types of stimulation (right
anodal, left anodal and sham) over two target sites (DLPFC and
PARC) were applied during retrieval phase (an old/new judgment
task). Results indicated that only a-tDCS applied over left targets
(both DLPFC and PARC) improved retrieval for abstract and concrete
words in the aged group. Intriguingly suggestions come from
comparing performances of the elderly with those of a control
group of young adults in which facilitation effects were seen with
both right and left a-tDCS. While right target stimulation improvement
observed in the control group fits with HERA model, sole
asymmetric left facilitation observed in the elderly could not be
interpreted straightforwardly. Although the authors commented it
from the perspective of a dedifferentiation process, probably meaning
a reduction in right hemisphere functioning, this result runs
counter to the idea that loss of specificity occurring with aging
often encompasses the decline of regional specialization and the
recruitment of unrelated areas.
In another study by Sandrini et al. (2014), a-tDCS was applied
over left and right DLPFC to induce an improvement in the retrieval
of previously learned concrete words. The experiment was conducted
over four separate sessions, namely a first learning and
immediate recall phase, a 24 h delayed reconsolidation session
with 15 min of tDCS, as well as a 48 h and a 30 days delayed
retrieval. Control condition encompassed the application of a-tDCS
during reconsolidation session without a direct reminder of what
was done the day before. No sham stimulation without reminder
condition was tested. Results indicated that, independently from
giving or not a reminder, a-tDCS over left DLPFC was capable
of strengthening accuracy in memory retrieval, reducing forgetting
up to 30 days. This finding is in line with long lasting effects
observed in Flöel et al. (2012), supporting the hypothesis that tDCS
mainly acts through long-term consolidation mechanisms of the
memory trace. Moreover, this result points out the possibility of
strengthen existing memories by means of tDCS. This chance was
further addressed by Eggert et al. (2013) exploring whether slow
oscillating tDCS (a particular type of stimulation that combines
E. Tatti et al. / Ageing Research Reviews 29 (2016) 66–89 79
alternating current with DC offset) (Bergmann et al., 2009) could
impact sleep-dependent memory consolidation in a group of 26
elderly subjects. Based on evidences proving the role of slow oscillations
in memory consolidation in healthy young people during
sleep (Marshall et al., 2006), bi-frontal anodal stimulation was
applied during early non-rapid eye movement (NREM) sleep with
a frequency of 0.75 Hz. Word-pair association and sequential finger
tapping (SFTT) tasks were administered in the evening before
sleeping to assess both declarative and procedural memory. Morning
recall performances were then compared with those of the
evening. Results showed that tDCS not only failed to show sleepdependent
performance enhancement both in the declarative and
the procedural memory tasks but rather overall non significant performance
impairment was observed. This evidence complies with
studies reporting reduced sleep-dependent consolidation in aging
(Pace-Schott and Spencer, 2011; Spencer et al., 2007), an effect
probably depending on changes in sleep structure (Hornung et al.,
2005). Given that effectiveness of oscillating tDCS is thoroughly
related to the ongoing brain activity, it may be possible that the
patterns of brain activity recorded with EEG after a-tDCS, that is an
increase of the awake phase as well as reduced NREM stage 3 sleep,
prevented tDCS resonance effects. Further studies should therefore
assess whether and how slow wave sleep could be a feasible phase
to enhance consolidation of memories in the elderly.
Last but not least, we conclude this section discussing available
evidences on one of the most affected abilities in the aging brain:
working memory (WM). Despite WM loss occurring with aging
accounts significantly for impairment in several cognitive tasks
(Glisky, 2007), currently only three studies attempted to improve
WM abilities in the elderly (Berryhill and Jones, 2012; Park et al.,
2013; Jones et al., 2015). The first attempt was designed by Berryhill
and Jones (2012), which tested the possibility of improving visual
and verbal WM in 25 healthy adults divided on the base of their
level of education. Built on the evidence that older adults tend
to rely more heavily on bilateral frontal regions to perform WM
tasks (Eyler et al., 2011), tDCS was applied across three experimental
sessions: a-tDCS to the right (F4) and left (F3) DLPFC and
sham stimulation over F3 or F4. Four pseudorandomized blocks
each of the visual and verbal 2-back WM tasks were performed
after 10 min of 1.5 mV current stimulation. Despite the easiness of
the 2-back task, results reported a surprisingly clear dissociation
in performance between subjects with higher and lower education.
Regardless of stimulation site and WM task, only older adults
with more education showed an improvement on WM. Instead, low
education group was unexpectedly hurt by a-tDCS (an effect especially
evident in visual WM task after stimulation over the right
hemisphere). These results raise important questions and mechanistic
hypotheses: selective tDCS benefit in subjects with higher
education may be related to the fact that they employed a different,
more efficient, WM strategy than the low education group and
that, in turn, this allows recruiting PFC structures more promptly
whether coupled with tDCS. The interpretation of the impressive
impairment in visual WM is almost a wild-goose chase, instead.
In line with the right hemi-aging model which sustains that right
hemisphere cognitive functions are prominently more affected by
aging (Dolcos et al., 2002) it may be that bilateral stimulation is
needed to recruit additional resources in a worn-out system. A further
analysis of differential pattern of activity according to this key
variable could indeed be fundamental to address this issue and to
design proper neuroenhancement studies.
Finally, a further step towards the creation of proper cognitive
interventions encompasses the application of tDCS during cognitive
trainings. Despite the large number of positive evidences in
healthy subjects (Elmasry et al., 2015), only two studies combined
tDCS and cognitive training in healthy aging (Park et al., 2013; Jones
et al., 2015). Park et al. (2013) coupled computer-assisted cognitive
training (CACT) to multisession a-tDCS on bilateral PFC to evaluate
their long-term effects. In this study, a-tDCS and CACT were
concomitantly administered for 10 sessions in 40 healthy elderly
aged over 65 years. The accuracy of the verbal WM task and digit
span forward test was significantly improved with a-tDCS as compared
with sole CACT program. Moreover, in the verbal WM task
the effect lasted 4 weeks with a significant boosting also in WM
reaction times. This result provides evidence that bilateral simultaneous
stimulation with external reference electrodes is able to
enhance memory accuracy and reaction times in healthy elderly.
Most importantly, it suggests that the combination of computerized
cognitive training and tDCS can lead to a long-lasting improvement
of memory functioning. Jones et al. (2015) further explored the possibility
of inducing long-lasting enhancement in WM abilities, as
well as to transfer these ameliorations to other cognitive domains.
Seventy-two old participants carried out 10 training sessions coupled
with a-tDCS or sham stimulation. For what concern active
stimulation, three stimulation designs were created: right frontal
a-tDCS (F4), right parietal posterior cortex (P4) a-tDCS and P3-P4
alternating a-tDCS. To assess single effects of multi-session tDCS
stimulation, a control group without cognitive training was added.
Results showed an enhancement of WM abilities in all the groups
who carried out the cognitive training. Therefore, after the 10th
session, no apparent boosting effect induced by active tDCS was evident.
Surprisingly, after 1 month, subjects in the active-tDCS group
had a better performance both to trained and un-trained tasks,
thereby demonstrating that active stimulation enhanced longerlasting
improvements and solid near transfer effects. This elegant
studies provides several sparks: first, the lack of a significant behavioral
difference across the different stimulation sites suggests that
tDCS could enhance WM by targeting the fronto-parietal network;
given the role of both prefrontal and parietal areas to WM ability,
future studies should replicate this design adding an unrelated area
or/and combing functional imaging. Secondly, it is notably that cognitive
enhancements and transfer effects were stronger in the more
challenging and adaptive tasks, where a rapid update of information
is needed (e.g. 2 n-back task). This effect is in line with some
previous studies (Jaeggi et al., 2008; Schmiedek et al., 2010) and is
indeed a good starting point in the design of future training studies
aimed at enhancing transfer effects.
3.3.5. Other cognitive domains
Moving towards other aspects of human cognition, Boggio et al.
(2010) attempted to neuromodulate decision making in the elderly.
A previous tDCS study on young adults (Fecteau et al., 2007)
observed reduced risk-taking after anodal stimulation over the
right PFC. In the reviewed study, the same electrode montage
(F3/F4) was utilized to assess the effect of a single session of online
a-tDCS on decision-making performance of a group of 28 healthy
elderlies. Results indicated a different tendency compared to young
adults: left anodal/right cathodal PFC stimulation increased risky
choices as compared with sham and right anodal/left cathodal
PFC stimulation. These results are probably not as paradoxical as
authors believed, for the following reasons; a first consideration
arises from the fact that the study on young subjects was based on
the investigation of the subtle interhemispheric balance between
left and right PFC. In particular, the selected montage was chosen
to explore the effects of the upregulation of the right DLPFC
(commonly linked with conservative decisions) coupled with the
downregulation of the left DLPFC activity. The lack of improvement
after right a-tDCS observed in the replication by Boggio could be
therefore interpreted as a reduction of prefrontal right hemisphere
functionality in healthy aging. On the base of recent research on
decision making (Fecteau et al., 2007) both right and left DLPFC are
needed to obtain a significant effect on decision making. It could
therefore be possible that increased risky behavior retrieved after
80 E. Tatti et al. / Ageing Research Reviews 29 (2016) 66–89
left a-tDCS stimulation is the outcome of an imbalance across PFCs
activities. Another possibility is that differences between young and
old adults are due to different levels of education, a variable that,
as we described in the study of Berryhill and Jones (2012), could
mediate the effects of tDCS-based neuroenhancement in aging pop-
ulation.
In order to stick to the issue at hand, namely right DLPFC stimulation
to enhance high order cognition in the elderly, we review a
recent paper by Harty et al. (2014). The cognitive function under
scrutiny in this case is the ability to detect errors: 106 healthy
old adults were recruited for four separate experiments with different
electrode montages, aiming at studying the role of right
DLPFC in error detection and the possibility of recovering awareness
deficits in aging population. Under real stimulation condition,
1 mA tDCS was applied for the duration of each of the five blocks
of a Go/No-go response inhibition task (Error Awareness Task-EAT)
alternating anodal and cathodal active stimulation over right and
left DLPFC. The reference electrode was placed over the vertex (Cz)
in all the conditions. Results indicated that a-tDCS over right DLPFC
increased the number of errors pointed out by the elderly in the
Repeat no-go part of the task (“do not press the button whether
the same word is repeated”). No significant effect was observed in
Stroop no-go subtest. This effect may be due to the fact that repeat
no-go error awareness may be facilitated as a result of improved
memory capacity and that Stroop No-go performance was close to
ceiling (average of 91% across all experiments) and could not be
improved further.
Finally, moving toward recent investigations, a brand-new
research by Manor et al. (2016) moved beyond the single cognitive
domain and approached a “more ecologic” issue. Starting from
evidences showing a “dual-tasking” cost in healthy aging (namely a
performance decline in one or both task when performed together)
(Verhaeghen et al., 2003), these authors tried to improve a basic
but fundamental ability: standing and walking while performing
cognitive tasks. In this study, 37 older adults underwent 2 sessions
encompassing five single and dual tasking blocks; they were
asked to stand, walking (with and without counting backwards)
or to simply counting backwards while staying seated. Previous
neuroimaging evidences showed that performing more than one
task together requires a stronger recruitment of prefrontal areas
and, as we discussed in the introductive section, the performance
of elderly people drops with high cognitive load. On this basis, left
frontal a-tDCS (F3) was applied offline for 20 min at an intensity
of 2 mA. Results showed no effects on single tasks performances
but a significant amelioration of the cost of standing and walking
while performing serial subtraction. However, as the authors
themselves highlight, the study does not provide any insight on
the mechanisms underlying this improvement. This shortage is
partially related to a lack of consensus on the neurophysiological
mechanisms of dual tasking; when hypothesizing that the two tasks
are processed serially (thereby penalizing the one who is processed
later), a-tDCS effects could be explained in terms of an improvement
in processing speed in healthy aging. Conversely, if we posit
that both tasks are processed concurrently, a-tDCS may lead to
an enhancement of the ability to allocate cognitive resources or
to recruit additional ones. Given the relevance of dual-tasking in
everyday life, further research aimed to uncovering the neurophysiological
underpinnings of dual-tasking costs and its amelioration
after PFC a-tDCS is certainly needed.
3.4. Further nibs-techniques
During the last decades a clear relationship between brain oscillatory
activity and the dynamics of cognitive processes has been
uncovered. The functional meaning of brain oscillatory activity
has been first spotted analyzing changes in oscillatory activity
phase or time-locked to specific cognitive and sensorial events.
Indeed, synchronization of neural assemblies in specific frequency
bands could be considered the language of the brain, allowing
the segregation and the integration of a multitude of distributed
information-processing activities following external or internal
stimulus. Notwithstanding, electrophysiological evidences of the
correlation between brain dynamics and cognitive performances
could not disentangle whether brain rhythms are just an epiphenomenon
or a fundamental mechanism for cognitive functioning.
The recent introduction of novel NiBS methods, e.g. tACS and tRNS,
could help addressing this issue. Differently from polarity-specific
effects of tDCS, tACS and tRNS represent a different approach to cognitive
neuroenhancement (Santarnecchi et al., 2015a, 2016). tACS
holds the unique capability to directly influence cortical rhythms,
namely to induce an “entrainment”, that is an alignment between
endogeneous and exogeneous (tACS induced) oscillatory potentials
(Ali et al., 2013). tACS is effectively suitable to interact with ongoing
brain oscillatory activity and modulate them in a frequency-specific
fashion (Thut and Miniussi, 2009), a capacity that could contribute
to a better understanding of the functional meaning of cortical
rhythms. Moreover, tACS is able to influence cortical excitability
(Antal et al., 2008; Chaieb et al., 2011; Feurra et al., 2013, 2011;
Kanai et al., 2010; Wach et al., 2013) and a series of studies in
healthy subjects have recently showed the potential for using tACS
to improve basic higher-cognitive functions like working memory
(Polania et al., 2012) and fluid intelligence (Santarnecchi et al., 2013,
2016). Typically, tACS is delivered without DC offset at a specific frequency
within the canonical spectrum captured by EEG recordings
(1–80 Hz) and in the so called “ripple” range (140 Hz) (Moliadze
et al., 2010). As long as it lacks superimposed polarity, frequency,
phase and intensity are the major parameters defining the effects
of stimulation. Despites accumulating evidences in support of the
feasibility of this tool, at present no articles have been retrieved
using tACS without DC offset in the aging population.
Therefore, this section will only focus on a specific type of oscillating
stimulation: tRNS. First experimentally employed in 2008
(Terney et al., 2008) tRNS is the newcomer in the tES family. Based
on normally distributed random currents with a frequency spectrum
between 0.1 and 640 Hz (Fig. 2), tRNS leads to a long-lasting
increase of cortical excitability by a mechanism possibly entailing
“stochastic resonance” phenomena (Terney et al., 2008). As
research demonstrated, repeated subthreshold stimulations with
tRNS could act preventing the homeostasis of the system, enhancing
the reaching of firing thresholds and, ultimately, eliciting
plasticity (Fertonani et al., 2014). Given that the output from both
electrodes is (from a practical standpoint) polarity unspecific, this
type of stimulation is appropriate for stimulating two regions at
the same time and may also provide longer lasting effects than
tDCS, at least as suggested for tinnitus suppression (Vanneste et al.,
2013). Such a long-lasting effect could be particularly influential
in the design of neuromodulatory interventions on healthy aging
population. Currently, only one study assessed the effects of tRNS
on healthy elderly’s cognition. Cappelletti et al. (2015) designed
an elegant investigation aimed at disentangling whether learning
effects elicited by cognitive training are due to an enhanced
ability to integrate information or to improved inhibitory abilities.
To address this complex issue, they trained for five consecutive
days 30 young and 30 aged subjects on a numerosity discrimination
task with concomitant tRNS. According to literature supporting
the role of parietal cortex in numerosity discrimination, electrodes
were positioned on parietal regions (P3 and P4) or motor (C3 and
C4) regions (control condition). Random noise current was delivered
for 20 min with an intensity of 1 mA during one-hour training
session. Results revealed that, regardless to age, numerosity discrimination
accuracy was improved after training (∼19%), an effect
shown to be significantly larger (32%) and long lasting (up to 16
E. Tatti et al. / Ageing Research Reviews 29 (2016) 66–89 81
weeks) when training was coupled with parietal tRNS. Despite similar
improvement in young and aged groups, data revealed crucial
age-dependent differences in the content of learning: aging participants
improved more in inhibitory abilities, whereas younger
subjects improved in cue-integration abilities. Moreover, elderly
showed cognitive transfer to other untrained tasks, especially
those probing inhibitory abilities and a negative transfer to space
processing, a related but untrained ability. Despite these results
deserve further in-depth consideration, they unequivocally provide
an amazing evidence of the fact that NiBS long-lasting effects are
grounded on different mechanisms of learning across lifespan and
highlights the high-potential of coupling cognitive training with
NiBS in aging population to induce cognitive improvement with
remarkably transfer effects to untrained cognitive abilities.
3.5. Summary of the use of NiBS neuro-enhancement protocols in
the elderly
NiBS techniques could be considered a promising effective tool
in the attempt of counteracting cognitive outcomes of aging, showing
preliminary positive effects on several cognitive abilities, and
initial evidence that these changes may last in time, which is crucial
for improving everyday activities. Specifically, these improvements
were observed for motor functioning (75% of 4 available studies),
language (100% of 5 available studies), memory (85.7% of 7 available
studies), inhibitory control (1 available study), number acuity
(1 available study), dual tasking (1 available study) and metacognition
capacities (error awareness: 1 available study). Stimulation
with tES mainly resulted in improved accuracy (77.8%) rather than
reaction times (16.67%) (see Fig. 3, for a more detailed overview
on tDCS studies), whereas a major effect on reaction times was
observed in all the reviewed rTMS studies (3 studies). Despite growing
validation to this method, some issues regarding the use of NIBS
to modulate functions should be addressed in upcoming studies in
order to customize interventions in aging population. These issues
are discussed in the following paragraphs.
4. Future directions
4.1. Giving real-world impact to NiBS
In order to make NiBS techniques really useful even outside laboratory
settings, a pivotal goal in the near future is to address their
efficacy as long lasting brain enhancers. Although some studies
reported long-term enhancement (lasting up to 1 month) after single
session (Sandrini et al., 2014), dearth of follow up assessments
in the available literature makes difficult to establish unequivocally
long-term impact of single session on aging population. Besides
this, multi-session NiBS could be a feasible avenue to counteract
plasticity reduction in aging population and to elicit late-phase
LTP/LTD plasticity. In fact, reviewed studies demonstrated sharper
and lasting improvements when tES was applied during multiple
sessions, especially when it was coupled with training (Park et al.,
2013; Zimerman et al., 2013; Jones et al., 2015). A recent metaanalysis
by Hsu et al. (2015) confirmed this hypothesis, showing a
larger effect size for multiple sessions of stimulation (mean effect
size, 0.89) than for single session studies (mean effect size, 0.44).
Noticeably, as we will discuss shortly afterwards, multi-sessions
interventions have to be properly designed in order to achieve
lasting effects. Moreover, the increasing automatization in the computation
of current flow on the base of individual head models could
indeed have a positive return in the application of NiBS in clinical
settings; in the future, highly customizable interventions on the
base of the subjects’ specific anatomy could provide higher efficacy
and adaptability in multi-sessions protocols.
4.2. The right place at the right time. Best targets and parameters
of NiBS in the aging brain
Taking into account the structural and functional changes occurring
in the brain of aging population, the selection of areas to be
targeted should not be inferred from studies on healthy young
adults, but they probably have to be grounded on strong evidences
about the cognitive biomarkers of the aging brain. In the
matter of this, a first necessary step could be the proper characterization
of the functional or dysfunctional role of an area in
the targeted cognitive domain. Basic assumptions about NiBSbased
enhancement or inhibition interventions should therefore
be guided by proven neuroimaging and electrophysiological models
of the aging brain. Similarly, the choice of reference electrodes
in tDCS experiments should be taken into account carefully. Indeed,
different effects depending on electrode’s reference location have
been described (Vandermeeren et al., 2010); this mainly applies
for polarity-specific tDCS, rather than for tRNS or tACS. Moreover,
given evidences of task-related bilateral activation in elderly, selection
of contralateral areas as reference (a choice adopted by 17.6%
of the reviewed papers) should be thought wisely.
A notable criticism against tES methodic is that the outcome of
the intervention is largely confounded by electrodes size. Indeed,
as modeling of current flow and neuroimaging studies recently
claimed, standard pad electrodes size (usually 5 × 5 cm or 5 × 7 cm)
does not provide enough spatial focality to allow a clear explanation
of the mechanisms underlying tES nor to guarantee the maximum
efficacy of the methodic. Several technical solutions have been
proposed to shape the current flow (e.g. increasing return electrode
size, using an extracephalic reference) and, recently, the use
of ring and concentric electrodes have been proposed to overcome
the issue of current spread. On this base, a new growing
approach to tDCS has been developed to make tES a more targeted
and customized technique: high-density tDCS (HD-tDCS)
(Datta et al., 2008). Based on the use of multiple arrays of small
anode and cathode electrodes, high-density tDCS uses specific electrodes
positioning, such as the 4 × 1 montage, where the anode is
surrounded by 4 return electrodes positioned in a circular configuration
(Minhas et al., 2010). Noticeably, given that the aging brain
is often characterized by task-related activation of differentlyapparently
unrelated- brain areas, this advancement could be very
important to localize target areas without affecting the distal ones.
For what concerns “timing” of stimulation, the majority of the
tDCS studies designed an online stimulation (52.9%) while rTMS
literature mainly adopted an offline protocol (67%). Despite this
tendency, positive results were also obtained with offline tDCS,
according to motor cortex reactivity to tDCS which mainly changes
after stimulation cessation (Santarnecchi et al., 2014), with the
exception of Fertonani et al. (2014). Moreover, the positive results
obtained by Sandrini et al. (2014) by means of application of tDCS to
reinforce acquired memories (i.e. consolidation), raises the possibility
of further testing neuromodulation according to the intrinsic
temporal dynamics of cognitive processes under scrutiny. In their
recent meta-analysis, Hsu et al. (2015) showed a larger effect
size for offline (mean effect size 0.92) than for online stimulation
(mean effect size 0.23): they cautiously interpreted this difference
as an evidence that healthy aged subjects could benefit more
from induced LTP-like mechanisms by means of offline stimulation.
However, since their analysis pooled subjects who received either
rTMS or tDCS interventions, it is difficult to disentangle which of
the two techniques was more effective for this cohort and, in turn,
which design suits best for each approach. Indeed, the speed up
of naming reaction times in the healthy aging group appeared
only when a-tDCS was applied online, whereas both offline and
online interventions improved reaction times in healthy young
subjects (Fertonani et al., 2014). The fact that Hsu et al.’s meta-
82 E. Tatti et al. / Ageing Research Reviews 29 (2016) 66–89
Fig. 3. Brief schematic overview showing main results of tDCS-based protocols for cognitive neuroenhancement in aging population. Panel (A) brain regions stimulated
(number of studies are specified in brackets); Panel (B) % of studies employing Online/Offline protocols; behavioral effects of tDCS; hemispheric localization of stimulated
area.
analysis further reported a higher effect size for online stimulation
(mean effect size, 1.79) than for offline stimulation (mean effect
size, 1.04) in AD patients suggests that tES application should be
based on strong evidences on the neural changes occurring during
the lifespan (e.g. cortical excitability). Therefore, neuromodulatory
interventions (either online or offline, with rTMS or tDCS), particularly
in the elderly, should be guided by a proper characterization of
the different neural mechanisms mediating the expected benefits.
Another issue related to timing encompasses the establishment
of the best time-window between multi-sessions tES, in order to
stretch induced neuroplasticity. Far off from being a minor variable,
the induction of late-phase LTP/LTD plasticity could largely depend
on proper spacing interval between sessions. As a recent work
by Goldsworthy et al. (2014) remarked, studies on animal models
reported longer lasting plasticity effects with closer stimulation
sessions (3–30 min), whereas daily treatment schedule is currently
the typical gold-standard in human research. This result was confirmed
by Monte-Silva et al., 2013; showing that the application of a
second a-tDCS stimulation with an inter-stimulation interval of 3 or
20 min (i.e. during the after-effects of the first one) enhanced motor
cortex excitability for more than 24 h. Moreover, as a recent review
highlights (Prehn and Flöel, 2015), multi-session stimulation with
large time intervals (3–24 h) could even prevent neuroplasticity
(Monte-Silva et al., 2013). Without exceeding safety and practical
constrains, further research should therefore investigate whether
“high-density stimulation” could product longer-lasting and reliable
improvements than single and widely spaced sessions.
4.3. Connectome-based neuroenhancement
Despite promising evidences emerging from the application of
NiBS on aging population, most of the available studies are still
grounded on a modular approach, which intrinsically considers
complex cognitive functions as an emerging property of segregated
brain regions. Although the adoption of this simplistic framework
has been, and still is, a necessary step to make proper advancements
in this field, an interest is emerging in adapting NiBS protocols to
the complex universe of brain connectivity: this because of latest
evidence of the interaction between the complexity of individual
brain connectivity and cognitive profiles (Santarnecchi et al.,
2015b,c; van den Heuvel et al., 2009; Sporns, 2014). An ideal
result was obtained by Meinzer et al. (2013) coupling tDCS with
concurrent fMRI. They showed that performance improvement
was associated to a reduction of the task-related hyperactivity of
bilateral prefrontal areas, the anterior cingulate gyrus and the precuneus.
Moreover, resting state fMRI analysis revealed that a-tDCS
induced a connectivity pattern quite similar to those of younger
subjects. More complex pictures could result studying network
activity changes after NiBS. As we discussed previously, connectivity
analysis performed in the study of Vidal-Pi˜neiro et al. (2014)
revealed that iTBS applied over IFG was associated to broad “Level
of Processing-dependent” changes in task-related functional activity.
Evidence of IFG-posterior (occipital and cerebellum) coupling
under deep encoding condition and iTBS modulation sustains the
role of left IFG in semantic processing, and further suggests a
possible top-down modulation over posterior areas. Although considerations
on temporal dynamics of such coupling could not be
made through offline fMRI-TMS, this result could be of special interest
in respect to posterior–anterior shift in aging (PASA) model.
Neuroimaging-based tES modulation could also help disentangling
differential effects of unilateral and bilateral stimulation in aging
population. Lindenberg et al. (2013) intriguingly found that dual
but not anodal tDCS enhanced connectivity of the left dorsal posterior
cingulate cortex. Furthermore, dual tDCS yielded stronger
activations in bilateral M1 compared with anodal tDCS when participants
used either their left or right hand during the task, thus
suggesting that dual stimulation is able to elicit more complex bihemispheric
modulation than the unilateral one. In closing, food
for thought results from the fact that the last two studies failed to
find an overt effect in task performances. Given that NiBS demon-
E. Tatti et al. / Ageing Research Reviews 29 (2016) 66–89 83
strated strong covert neurophysiological changes, efforts should be
made to study pattern of neural activity induced by NiBS in order
to explain and predict lack of behavioral improvements.
For what concern this possibility, apolipoprotein E (APOE) status
has recently been shown to mediate changes in large-scale networks
as identified by the analysis of low-frequency fMRI-BOLD
fluctuations. In a recent report by (Pe˜na-Gomez et al., 2012), the
assessment of the APOE ␧4 allele profile, known as one risk factor for
the development of AD, was combined with the data of a rTMS study
on elders with memory impairment (Solé-Padullés et al., 2006). On
the base of the carried genetic profile, two subgroups (APOE-␧4
allele carriers/non-carriers) were created to investigate whether
this genetic profile could mediate the response to rTMS. Results
indicated that, even if there was not any difference in behavioral
performance of the two subgroups, there was a clear dissimilarity
between brain connectivity of the APOE-␧4 allele carriers and
those of the non-carriers; most noticeably, the statistical analysis
revealed that rTMS was able to reorganize brain networks configuration
of the APOE-␧4 bearers as those observed in the non-carrier
elders prior to stimulation.
Also, different allelic profiles in the Catechol-OMethyltransferase
(COMT) gene, which regulates PFC dopamine
(DA) levels, could help the interpretation and design of cognitive
interventions in the elderly. Specifically, recent evidences
reported that allelic variations in COMT gene accounted for great
differences in working memory abilities in the elderly. This is
not surprisingly considering the key role of the dopaminergic
system in modulating neuronal signal-to-noise ratio, synaptic
plasticity and learning processes; prefrontal DA levels show an
“inverted-U” relationship with cognitive functioning and accounts
for age-related impairment of processing speed, working memory,
fluid intelligence and memory (Backman et al., 2010). Moreover,
DA seems to modulate brain response to cognitively demanding
tasks, as shown by its increase with increasing task difficulty.
Moreover, emerging studies reported that DA reduction is related
to the under-recruitment of task-specific brain areas and to variable
non-selective recruitment of apparently task-unrelated brain
regions. Hence, it seems reasonable that age-related decline in DA
could largely explain neuronal and cognitive changes occurring
with aging. Consequently, a fundamental question that research
on NiBS-based cognitive neuroenhancement should address is
whether neuromodulation could affect dopaminergic system
and how COMT genetic profile could prevent or even reverse
these effects. Emerging studies on the interaction between DA
and tDCS demonstrate the existence of a delicate balance across
genetic profiles, neurotransmitters levels, brain stimulation and
response differences (see Medeiros et al., 2012 for a review on
neurobiological mechanisms of tDCS). Recent pharmacological
studies (Fresnoza et al., 2014a, 2014b; Monte-Silva et al., 2010)
highlighted U-shaped dosage-dependent effects of L-DOPA, that is
low and high L-DOPA intake abolished the excitatory and inhibitory
effects of tDCS on motor cortex plasticity. Therefore, dopamine
levels could skew tDCS effects thereby indicating that individual
allelic profile could lead to similar different outcomes. Plewnia
et al. (2013) showed that the application of a-tDCS on left DLPFC
during a Go-NoGo task impaired executive performance only in
COMT Met–Met homozyguous young subjects (the ones with the
lowest DA levels), whilst c-tDCS showed inhibitory effects only in
Val58Val carriers (the ones with lowest DA levels) (Nieratschker
et al., 2015). Thus, tDCS is able to modulate prefrontal DA but its
behavioral effects are largely dependent on the genetic background
of the individual. Given that the effects of COMT genetic profile
on cognition are somewhat enhanced in the elderly and new
evidences reporting further modulatory effects between BDNF
and COMT genotypes (Nagel et al., 2008), future NiBS studies
on healthy aging should account on individual genetic profile to
design reliable interventions.
In conclusion, the combination of NiBS with other neuroimaging
techniques could provide insights in whether and how brain
networks are influenced by transcranial stimulation (Santarnecchi
et al., 2015a). Thus, in order to target specific connections
rather than specific areas, it is necessary to understand in depth
both aging-related changes occurring in large scale-networks and
whether, how, and when these networks respond during or after
tES. Genetic background analysis could indeed become a valuable
ally to a better understanding of these issues and to provide useful
indications to the future use of NiBS as a tool to help preventing
pathological aging; therefore, more studies combining NiBS and
genetics are thus yearned in the next years.
5. Conclusions
We have reviewed experimental data supporting the feasibility
of NiBS as an effective interventional approach to hinder cognitive
decline in healthy aging individuals. Indeed, NiBS can be
easily coupled to cognitive trainings to bolster positive effects and
could be a feasible and safe tool to partly counteract physiological
age-related decline. However, despite some encouraging evidence,
most −but not all- of the published studies showed variability and
volatility of the induced neuro-enhancement effects. Neuroimaging
and genetic measures could indeed be fundamental allies to
the refinement of some theoretical and practical issues that limits
NiBS feasibility out of the lab’s walls. Therefore, a multidisciplinary
approach to the study of the best NiBS protocols could represent
a key point to offer to the aging population an effective tool to
counteract their otherwise unavoidable cognitive decline.
Conflict of interests
Dr. Emiliano Santarnecchi and Prof. Simone Rossi are consultant
for EBNeuro Ltd., developing new hardware for non-invasive
brain stimulation and electroencephalography. None of the NIBS
approaches and/or methods and/or tools cited in the present
manuscript are property of EBNeuro, therefore they do not constitute
any conflict of interest.
Statements of funding
No authors received funding for this study.
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