A.R. Møller (Ed.)
Progress in Brain Research, Vol. 157
Copyright r 2006 Elsevier B.V. All rights reserved
Brain plasticity and functional losses in the aged:
scientific bases for a novel intervention
Henry W. Mahncke2, Amy Bronstone2and Michael M. Merzenich1,?
1Keck Center for Integrative Neurosciences, University of California, San Francisco, CA 94143-0732, USA
2Posit Science Corporation, San Francisco, CA 94104, USA
Abstract: Aging is associated with progressive losses in function across multiple systems, including sen-
sation, cognition, memory, motor control, and affect. The traditional view has been that functional decline
in aging is unavoidable because it is a direct consequence of brain machinery wearing down over time. In
recent years, an alternative perspective has emerged, which elaborates on this traditional view of age-related
functional decline. This new viewpoint — based upon decades of research in neuroscience, experimental
psychology, and other related fields — argues that as people age, brain plasticity processes with negative
consequences begin to dominate brain functioning. Four core factors — reduced schedules of brain activity,
noisy processing, weakened neuromodulatory control, and negative learning — interact to create a self-
reinforcing downward spiral of degraded brain function in older adults. This downward spiral might begin
from reduced brain activity due to behavioral change, from a loss in brain function driven by aging brain
machinery, or more likely from both. In aggregate, these interrelated factors promote plastic changes in the
brain that result in age-related functional decline. This new viewpoint on the root causes of functional
decline immediately suggests a remedial approach. Studies of adult brain plasticity have shown that sub-
stantial improvement in function and/or recovery from losses in sensation, cognition, memory, motor
control, and affect should be possible, using appropriately designed behavioral training paradigms. Driving
brain plasticity with positive outcomes requires engaging older adults in demanding sensory, cognitive, and
motor activities on an intensive basis, in a behavioral context designed to reengage and strengthen the
neuromodulatory systems that control learning in adults, with the goal of increasing the fidelity, reliability,
and power of cortical representations. Such a training program would serve a substantial unmet need in
aging adults. Current treatments directed at age-related functional losses are limited in important ways.
Pharmacological therapies can target only a limited number of the many changes believed to underlie
functional decline. Behavioral approaches focus on teaching specific strategies to aid higher order cognitive
functions, and do not usually aspire to fundamentally change brain function. A brain-plasticity-based
training program would potentially be applicable to all aging adults with the promise of improving their
operational capabilities. We have constructed such a brain-plasticity-based training program and con-
ducted an initial randomized controlled pilot study to evaluate the feasibility of its use by older adults. A
main objective of this initial study was to estimate the effect size on standardized neuropsychological
measures of memory. We found that older adults could learn the training program quickly, and could use
it entirely unsupervised for the majority of the time required. Pre- and posttesting documented a signifi-
cant improvement in memory within the training group (effect size 0.41, po0.0005), with no significant
?Corresponding author. Tel.: +1 415-476-0490;
Fax: +1 415-476-1941; E-mail: email@example.com
within-group changes in a time-matched computer using active control group, or in a no-contact control
group. Thus, a brain-plasticity-based intervention targeting normal age-related cognitive decline may po-
tentially offer benefit to a broad population of older adults.
Keywords: brain plasticity; cognitive rehabilitation; computer-based training
This chapter reviews the scientific bases of a novel
approach intended to improve the functional per-
formance of older adults by slowing, halting, or
reversing large-scale and progressive losses in
brain functioning commonly experienced in later
life. This hypothesis-driven approach is envisioned
to be much like an exercise program for the brain
that, ideally, should be initiated early in the aging
process to enhance brain health and cognitive fit-
ness before significant losses develop, but also
could be effective later in the aging process when
significant losses have already emerged.
The core of this chapter introduces a new per-
spective about the root causes of functional decline
in aging that is based on decades of research on
brain plasticity, experimental psychology, and
other related fields. Brain plasticity refers to the
lifelong capacity for physical and functional brain
change enjoyed by humans and other animals and
is inherently bidirectional: through the same mech-
anisms and plasticity processes, brain function can
either be strengthened or degraded, depending on
the circumstances. During normal aging, individ-
uals typically undergo physical, behavioral, and
environmental changes that, in the aggregate, pro-
mote negative plastic changes that degrade brain
function. Four interrelated factors are proposed as
the core causes of deterioration of functioning in
older adults. These root causes of functional de-
cline involve a complex interplay of physical brain
changes, and brain plasticity processes.
Just as brain plasticity processes with negative
consequences can contribute to age-related func-
tional decline, plasticity processes that strengthen
brain function can provide a foundation for a
therapy to restore sensory, cognitive, memory, mo-
tor, and affect systems in aging. This chapter fo-
cuses particularly on age-related cognitive decline,
though the concepts and principles discussed here
should apply to other areas of functioning (e.g.,
motor control) known to deteriorate with age.
The principles governing such brain plasticity
processes are now sufficiently well understood to
develop a new approach to maximize the quality
and extend the duration of healthy aging. A brain-
plasticity-based approach should be significantly
more effective than current interventions for
healthy aging, and could conceivably work in con-
junction with a variety of other behavioral and
pharmaceutical advances. When clinically vali-
dated, this science-based approach, which explic-
itly targets the underlying causes of long, slow
functional decline, could signify a revolution in
Cognitive decline in aging is progressive and can
Cognitive decline is a universal aspect of the aging
process. Memory decline during aging is pervasive
(Park and Gutchess, 2003; Reuter-Lorenz and
Sylvester, 2003; West, 2004). It may begin as early
as age of 30 and, on the average, worsens slowly
but steadily thereafter (Fig. 1) (Park et al., 1996).
In addition, virtually all older adults will eventu-
ally develop a reduction in speed of processing
(Salthouse, 1996). Various other cognitive abilities
(e.g., visuospatial skill, executive functions, speech
comprehension) have been found to commonly
deteriorate with age (Harvey and Mohs, 2000;
Zacks and Hasher, 2000; Schneider et al., 2002;
Buckner, 2004). A number of labels have been
used to describe normal age-related cognitive de-
cline, including age-associated memory impair-
ment, age-consistent memory impairment, benign
senescent forgetfulness, late-life forgetfulness, and
aging-associated cognitive decline (Ritchie et al.,
2001; Bischkopf et al., 2002; Fillit et al., 2002). In
normal aging, the extent of cognitive decline grad-
ually increases with age, although there is consid-
erable variability across individuals in the nature,
degree, and timing of cognitive loss (Ylikoski et
al., 1999; Park et al., 2003). Despite this variabil-
ity, normal cognitive decline is an inevitable con-
sequence of age; should individuals live long
enough, virtually all will eventually lose a degree
of cognitive efficacy.
Normal cognitive decline is distinct from path-
ological cognitive loss, which affects a sizeable
proportion of older individuals and culminates in
dementia. Pathological cognitive decline may look
much like healthy aging in the beginning stages,
but at some point a precipitous decrease in func-
tioning, particularly in memory, typically occurs.
About one in four older adults will experience a
decline now generally diagnosed as mild cognitive
impairment (MCI), in which function in a specific
cognitive domain (e.g., memory) is impaired while
activities of daily living generally remain intact
(Unverzagt et al., 2001). Individuals with MCI
show extreme losses in neuromodulatory activity
crucial for sustaining learning operations and viv-
ifying memory (AsDEaRC, 2001–2002, 2002).
MCI may be a transitional or high-risk state
between normal cognition and dementia, as 80%
of individuals diagnosed with MCI are diagnosed
with dementia within 5–8 years (Craft et al., 2003).
In contrast, only 1?2% of adults with normal age-
related cognitive decline develop dementia each
year (Craft et al., 2003).
Some argue that if adults were to live long
enough, the progressive physical deterioration of
the brain would eventually cause dementia in all
cases (Terry and Katzman, 2001). Others argue
that cases of dementia (and perhaps MCI) are
specific pathological conditions that are not the
expected end states of healthy aging (Fillit et al.,
2002). Data exist to support both theories regard-
ing the inevitability of dementia. On one hand,
older adults become more vulnerable to develop-
ing dementia with increasing age, such that almost
half of the adults of age 85 and above have Al-
zheimer’s disease (AD) (National Institute on Ag-
ing, 1998). On the other hand, many individuals
remain cognitively vital even into extreme old age,
evidencing only minor changes in speed of process-
ing and the most attention-demanding tasks (Fillit
et al., 2002). In either case, it is clear that normal
cognitive decline is a universal phenomenon, and
that a clinically significant amount of cognitive
decline is increasingly common with age.
Root causes of age-related cognitive decline
Changes in the brain occur with aging
Thousands of studies have documented the phys-
ical, anatomical, physiological, and chemical
changes that occur in the brain with aging (Bus-
siere and Hof, 2000; Magistretti et al., 2000; Raz,
2000; Mattson, 2003; Backman and Farde, 2005).
Fig. 1. Memory changes with age. Items recalled from a 16-word list in the immediate period (A, California verbal learning test CVLT-
II trial 5) and in the delay period (B, CVLT-II delayed free recall), and number of digits recalled in a digit-span backwards task (C,
Weschler memory scale III) (CVLT-II data courtesy of the University of California, San Francisco Memory and Aging Center).
In aggregate, this large body of research has es-
tablished five fundamental principles: (1) neurons
and the strengths and richness of their intercon-
nections progressively atrophy as individuals age;
(2) the deteriorating brain machinery includes cor-
tical areas and subcortical nuclei that are specifi-
cally related to sensation, cognition, memory,
motor control, and affect; (3) the metabolic de-
cline and down-regulation of key neuronal popu-
lations commonly precede cell death; (4) many
aspects of physical and chemical deterioration and
emergent neuropathology are correlated with gen-
eral and specific behavioral losses; and (5) al-
though there is substantial variability in the time
of onset, course, and magnitude of functional and
physical deterioration, these changes are a virtu-
ally universal outcome of the later years of an ex-
tended human life.
The above observations have led to a viewpoint
that is often termed the ‘‘wear and tear’’ hypothesis
of cognitive decline in aging (Aldwin and Gilmer,
2004). This hypothesis suggested that brain ma-
chinery simply wears down over time. The ob-
metabolic status, connectivity) and consequent
functional changes (e.g., memory deficits, reduced
processing speed, impaired spatial abilities) are the
consequences of any biological or mechanical ma-
chine that has been in operation for multiple dec-
ades. The natural conclusion of the wear and tear
hypothesis is that cognitive decline is normal, in-
evitable, and irreversible (Baron and Cerella, 1993).
The physical aging of the brain obviously plays
an important role in age-related cognitive decline.
However, it is increasingly clear that the inevitable
physical deterioration of the aging brain cannot
completely account for many of the changes in
functioning observed in older adults. The extensive
literatures on brain plasticity and the perceptual
psychophysics of aging strongly suggest that brain
plasticity with negative consequences is a crucial
contributor to age-related cognitive decline. As
individuals age, their schedules and strengths of
brain engagement substantially change, and are
paralleled by active degradation of brain function.
It is believed that such changes in brain use and
engagement are direct and critical contributors to
age-related cognitive decline.
Learning changes the brain through brain plasticity
Brain plasticity refers to the brain’s lifelong ca-
pacity for physical and functional change; it is this
capacity that explains how experience induces
learning throughout life. The concept of brain
plasticity is more than a century old (Woodruff-
Pak, 1993), and its study has been ongoing for
several decades. Historically, brain plasticity has
been more often discussed in the contexts of early
child development, stroke recovery, and percep-
tual learning than in regard to aging.
Before the concept of lifelong brain plasticity
was introduced, many researchers believed that the
human brain was hard-wired in early life (Wood-
ruff-Pak, 1993). Evidence supported this view by
demonstrating that the brain developed the phys-
ical structures and long-range interconnections
that determine neurological functioning during
an early ‘‘critical period’’ of child development. It
was established that, during this critical period, the
brain was capable of substantial remodeling in re-
sponse to alterations in input; but after the critical
period closed, it was generally observed that the
brain was not capable of further significant re-
modeling, elaboration, or growth. This notion that
the brain developed its immutable long-range in-
terconnections in early life contributed to the belief
that age-related cognitive decline was inevitable
and irreversible (Baron and Cerella, 1993).
Today, after decades of accumulated cross-dis-
ciplinary research, a new and very different view
has emerged about the origin and maintenance of
human abilities. This view holds that the brain is
plastic; that is, the brain is capable of reorganiza-
tion, including developing new short-range inter-
connections, at any age throughout adult life.
Brain plasticity experiments have documented a
number of important ways in which progressive
learning changes brain machinery. In aggregate, this
research demonstrates that the adult brain contin-
uously adapts to disproportionately represent rele-
vant sensory stimuli and behavioral outputs with
well-coordinated populations of neurons. This is
achieved by engaging competitive processes in brain
networks that refine the selective representations of
sensory inputs or motor actions, typically resulting
in increased strengths of cortical resources devoted
to, and enhanced representational fidelity (or
‘‘precision’’) of, the learned stimulus or behavior.
Brain plasticity with positive consequences
Competitive processes underlie all brain plasticity.
In perceptual, cognitive, and motor skill learning
tasks, competitive processes result in the narrow-
ing of time and space constants that define the se-
lectivity of processing in cortical networks. In this
way, the selective responses of cortical neurons
specialize to meet the specific demands of the task.
The representation of input timing is dramatically
elaborated, and the cortex’s ability to respond ac-
curately in fast, precisely measured time as a re-
ceiver and a controller of action are dramatically
advanced. For example, in monkeys trained to de-
tect a specific pattern of stimulation to the fingers,
the somatosensory cortex reorganizes to represent
that specific input pattern with large, well-organ-
ized, and spatiotemporally coherent responses (Re-
canzone et al., 1992a, b; Wang et al., 1995).
Similarly, in monkeys trained to perform demand-
ing motor tasks (e.g., retrieving food pellets, turn-
ing bolts), the primary motor cortex reorganizes to
represent the specific required motions of the digits
and hand with larger cortical areas (Nudo et al.,
1996). These types of changes have now been well
documented in humans as well; for example, violin
players have been shown to have stronger and
more distinct representations of the fingers in the
right hemisphere, corresponding to the individu-
ated finger movements required by their left hands
(Elbert et al., 1995). This example illustrates that
beautifully elaborated and highly differentiated
cortical representations develop in the learning of
any highly skilled behavior.
In learning skilled behaviors, such as playing a
musical instrument, as brain machinery is progres-
sively refined in its specificity, selectivity, and fidel-
ity through competitive processes, it increases the
representational power of behaviorally important
sensory stimuli and motor outputs, as manifested
by increased response magnitude and distributed
response coherence. This is achieved, in large part,
by plasticity processes that increase cortico?corti-
cal connectional strengths between neurons in
nearly simultaneously excited cortical networks.
A key effect of this learning-induced change is to
strengthen the signal-to-noise ratio of relevant
cortical activity. Cortical systems operate against a
constant background of internal noise from high
spontaneous network activity levels; detecting the
signal in this noise is a key challenge to such sys-
tems. Enhancement of the signal-to-noise ratio is
likely to be a key mechanism by which learning
improves brain function.
The outcome of these competitive processes is
positive because, through the locally adaptive
processes by which the brain specializes to repre-
sent salient input, the brain’s processing machinery
becomes more locally and globally adapted to
perform important behavioral tasks. As a general
principle, brain plasticity with positive conse-
quences is likely to underlie virtually all forms of
perceptual and skill learning in the brain.
Brain plasticity with negative consequences
We have just described how brain plasticity under-
lies all learning (e.g., perceptual, cognitive, motor).
Because plasticity processes are inherently compet-
itive, there always will be a competitive ‘‘winner’’
and ‘‘loser’’ (i.e., excitatory and inhibitory synaptic
changes); thus, plastic changes with negative con-
sequences are just as common as those with positive
outcomes. Plasticity can be manipulated by ad-
justing the learning context; it is possible to
actively degrade and weaken the brain processing
machinery just as easily as it is possible to refine,
elaborate, and strengthen the processing machinery.
One example of plasticity with negative conse-
quences is seen in monkeys trained under condi-
tions in which heavy synchronous input is
delivered across fingers (Jenkins et al., 1990; Al-
lard et al., 1991; Wang et al., 1995) or the entire
hand (Byl et al., 1996). In response to this type of
sensory stimulation, the somatosensory cortex re-
organizes to adaptively represent the undifferenti-
ated spatiotemporal characteristics of the trained
input. This results in an undifferentiated map —
with abnormally large, overlapping receptive fields
and degraded spatial and temporal response char-
acteristics. While this map is adaptive in that it
represents the use conditions of the hand, it is
maladaptive in that the map does not support the
use of the hand under conditions that require the
accurate processing of sensory inputs and motor
outputs with high degrees of spatiotemporal com-
Although this example shows how negative
plastic changes can be actively induced, these
changes more often occur naturally (i.e., without
conscious effort) in later life. For example, as peo-
ple age they commonly begin to stereotype and
simplify behaviors that previously were quite com-
plex and elaborated. The brain is likely to auto-
matically adjust to these less complex behaviors by
simplifying its representations that support them.
We refer to these changes as brain plasticity with
negative consequences because, through the locally
adaptive processes by which the brain specializes
to represent salient input, the brain’s processing
machinery becomes less locally and globally
adapted to perform important behavioral tasks.
Brain plasticity with negative consequences is very
likely to underlie specific pathological conditions
(e.g., focal dystonia of the hand) (Byl et al., 1996)
as well as general sensory or cognitive dysfunc-
tions (e.g., learning impairments in children) (Tall-
al et al., 1996a). Based on a growing literature in
the fields of psychophysics, neurology, neuropsy-
chology, and brain plasticity, it is almost certain
that the problems of age-related cognitive decline
are substantially caused by negative dimensions of
brain plasticity as well.
Age-related cognitive decline is a problem of brain
plasticity with negative consequences
Our forebrain processing machinery is sustained in
a refined, powerful, and efficient operational state
by its intensive use under challenging conditions.
In adulthood, continuous active interaction with
environments that are demanding to sensory, cog-
nitive, and motor systems is necessary to maintain
brain health and cognitive fitness. As people age, a
self-reinforcing, downwards spiral of reduced in-
teraction with challenging environments and re-
duced brain health significantly contributes to
cognitive decline (see Table 1). This downward
spiral might begin either from a reduction in the
schedule and engagement of brain activity or from
an initial small loss in brain function driven by
degraded sensory inputs (or, more likely, from
both). In either case, once such a spiral begins, it
continues through a sequence of interrelated
events that reinforces a cascade of negative inter-
actions, resulting in worsened cognitive fitness and
brain health. We identify four interrelated factors
as central and mutually reinforcing:
1. reduced schedules of activity
2. noisy processing
3. weakened neuromodulatory control
4. negative learning
Reduced schedules of activity
As people age, they typically change their activity
patterns, such that the level of engagement in cog-
nitively demanding activities is lessened (Hultsch
et al., 1999). Even people with historically high
levels of cognitive activity typically reduce their
level of stimulation, either by conscious choice
(e.g., retirement) or by unconsciously ‘‘resting on
their laurels’’ and pursuing only activities at which
they already excel. This results in less overall
Table 1. Root causes of functional decline in aging
Reduced Schedules of Activity – Reduction in the schedules of inputs and actions that engage the brain that are required to
continuously refine existing skills and drive new learning. Often referred to as ‘‘brain disuse.’’
Noisy Processing – Brain processing that produces low-fidelity, unreliable, and weakly-salient cortical representations of sensory inputs
and actions. This occurs because the deteriorated brain produces poor signal quality, and must adjust its time and space constants to
process these degraded signals, thus creating a noisy processing machine.
Weakened Neuromodulatory Control – Down-regulation of metabolism and connectivity of neuromodulatory control systems caused
by age-related physical deterioration and reduced schedules of activity.
Negative Learning – Changes in behavior that accelerate cognitive decline, typically chosen because ordinary behaviors have become
stimulation for sensory, cognitive, and motor
systems, and importantly reduces stimulation for
attention, reward, and novelty-detecting neuro-
modulatory systems. Through animal models, sci-
entists have shown that the physical and functional
consequences of brain disuse (engendered by ex-
posing animals to impoverished environments)
parallel the signature changes in the aged human
brain. These studies have documented that a lack
of brain engagement causes negative changes in
neuronal metabolism (e.g., the production and
function of neurotransmitters, receptors, and other
key functional biochemical constituents of neu-
rons), and in neuronal architecture (e.g., the elab-
oration of dendrites, axonal arbors, spines and
synapses, cortical and subcortical neuropil, and
gray matter) (Diamond et al., 1975; Katz and Da-
vies, 1984; Sirevaag and Greenough, 1985; Beau-
lieu and Colonnier, 1989; Park et al., 1992;
Melendez et al., 2004). These negative physical
changes are accompanied by impaired learning
and memory capacities that are thought to be the
result of long-term alterations in neuronal plastic-
ity driven by exposure to impoverished, nonstim-
ulating, and noncomplex environments (Lewis,
2004). The parallels between the physical and func-
tional changes seen in these models of brain disuse
and those seen in the aged human brain are unmis-
takable, and the fact that such changes are revers-
ible through environmental enrichment (Winocur,
1998) suggests that age-related cognitive decline
may be slowed, arrested, or even reversed.
Another consequence of aging is that sensory in-
put from all systems (e.g., auditory, visual, tactile,
proprioceptive) is degraded as a result of basic
deterioration of peripheral sensory organs (e.g.,
loss of hair cells in the cochlea, loss of photo-
receptors in the retina, changes in skin properties).
The brain must adjust to these degraded sensory
inputs by lengthening space and time integration
constants in an effort to detect relevant signals.
These adaptive changes are made at a cost — brain
systems with long space and time integration con-
stants cannot accurately represent the details of
spatiotemporally complex signals. This inaccuracy
manifests as temporally and spatially noisy re-
changes necessarily slow the speed of information
processing as well.
Weakened neuromodulatory control
A further consequence of aging is that the metab-
olism, connectivity, and eventually, structure of
neuromodulatory control systems, which regulate
learning and plasticity in adults, become degraded.
The key neuromodulators controlling plasticity are
ACh (Bartus et al., 1982), which modulates synap-
tic plasticity in the hippocampus, cerebral cortex,
and striatum (Doya, 2002), and controls memory
and the rate of learning (Gu, 2002); dopamine,
which mediates many aspects of cognitive, emo-
tive, and motor functions (Gu, 2002), and is im-
plicated in the prediction of reward and in action
learning (Doya, 2002); serotonin, which regulates
the time scale of reward prediction (Doya, 2002);
and norepinephrine, which controls mental alert-
ness and attentional focus (Usher et al., 1999). In
aggregate, degraded neuromodulatory control sys-
tems weaken the brain’s control over its own plas-
ticity, lowering learning rates and trapping the
brain in potentially inappropriate or unhelpful
patterns of activation.
As reduced schedules of activity, noisy processing,
and weakened neuromodulatory control interact
to make novel or demanding activities more chal-
lenging to perform, individuals naturally adapt
their behaviors in ways that can reinforce negative
aspects of the sensory input and motor output.
For example, as it becomes harder to follow the
rapid speech of a child on the telephone, an older
adult might turn up the volume on the phone (in-
creasing signal distortion along with loudness),
find it more frustrating to have such conversations
(decreasing neuromodulatory responses required
to maintain high brain function), or simply choose
to have fewer of such conversations (further re-
ducing the schedule of brain activity).
Substantial reorganizations in the responses of
older brains to sensory and cognitive tasks relative
to younger brains have been measured using func-
tional magnetic resonance imaging (fMRI). In a
variety of such tasks, the changes seen in older
brains can be interpreted as a dedifferentiation of
response properties, including the recruitment of
contralateral or new brain regions or the substitu-
tion of different brain regions to support task per-
formance (Park et al., 2001). This neurological
dedifferentiation is most likely a manifestation of
the physiological brain plasticity with negative
consequences we describe here.
In aggregate, these four factors create a brain
that is substantially less capable of representing the
spatiotemporal detail of incoming stimuli, less able
to represent such stimuli with strong, coherent, and
salient neural activity, less able to actively modulate
its own activity and capacity for change, and less
able to support rapid interactions across relevant
brain systems. Such a brain will manifest longer
time and space constants, a slower processing
speed, and integrated sensory, cognitive, and mo-
tor dysfunction. Below, we review the data from a
wealth of psychophysical, neuropsychological, and
cognitive studies that demonstrate that aged brains
manifest reduced accuracy and speed of informa-
tion processing as well as integrated dysfunction.
Cognitive decline is driven by changes across brain
Although sensory and cognitive systems are often
discussed and studied as separate entities, a large
body of anatomical, physiological, and behavioral
evidence suggests that, in fact, these systems are
very tightly interrelated (Schneider and Pichora-
Fuller, 2000). Information continuously flows
both forward and backward through the brain’s
sensory, cognitive, and motor systems. Sensory
systems detect and analyze fundamental stimulus
properties and feed this information forward to
cognitive systems that store, manipulate, and act
on it. Cognitive systems feedback to influence sen-
sory processing through attention, expectation,
memory, and context, while directly driving motor
systems to execute planned activities. Motor sys-
tems are tightly integrated with cognitive systems
through premotor areas involved in movement
planning, and indirectly provide feedback to sen-
sory systems through proprioceptive and vestibu-
lar systems. Because sensory, cognitive, and motor
systems are parts of a highly integrated informa-
tion processing system, disruption in any one sys-
tem would be expected to cause disruption in the
others, and degrade the overall accuracy and speed
of information processing (Schneider and Pichora-
Fuller, 2000). Indeed, deficits in sensory, cognitive,
and motor functioning are common in older adults
(Harvey and Mohs, 2000). Although this chapter
focuses on sensory and cognitive deficits, the prin-
ciples and science underlying these issues are also
directly relevant to motor function in older adults.
Researchers have begun to explicate the com-
plex ways in which these systems interact. It is now
clear that sensory systems with degraded function
negatively affect cognitive function. The source of
such degraded sensory input has typically been
assumed to be in the periphery (e.g., loss of hair
cells in the cochlea, loss of photoreceptors in the
retina) given the well-documented changes that
occur there (Scialfa, 2002; Madden et al., 2003).
However, a growing literature has shown that cen-
tral sensory processing deficits play a significant
role in the reduced cognitive performance of older
adults as well (Schneider and Pichora-Fuller, 2000;
Faubert, 2002). In the auditory system of older
adults, the negative effect of sensory losses on
cognitive performance has been extensively docu-
mented. Similar findings are emerging in the study
of the visual system. Other systems (e.g., somato-
sensory, vestibular) also decline with age, although
their relationship to associated cognitive systems is
not yet well understood. Below, we summarize the
deficits in the auditory and visual systems, and the
research that explores how these deficits contribute
to cognitive decline in older adults.
Changes in the central auditory system contribute to
cognitive deficits in aging
Many adults experience a decline in auditory sen-
sitivity with age, called presbycusia, which is com-
monly experienced as a sensory loss in the high-
frequency range of hearing, and is caused by the
deterioration of inner hair cells in the cochlea.
However, many other age-related auditory sensory
deficits have been shown to exist independent of, or
in combination with, high-frequency hearing loss,
suggesting that these deficits cannot be solely at-
tributed to deterioration of the peripheral sensory
system and must be rooted in the central auditory
system (Schneider and Pichora-Fuller, 2000).
The ability to temporally resolve an auditory
signal is critical for accurate speech perception
(Drullman, 1995a, b) and decreases with age (Abel
et al., 1990; Moore and Peters, 1992; Fitzgibbons
and Gordon-Salant, 1994; Schneider et al., 1994).
By comparing the temporal resolution abilities of
young adults with good hearing, hearing-impaired
older listeners, and older listeners with good hear-
ing, researchers have determined the extent to
which hearing loss and age may mediate temporal
resolution. These studies consistently have shown
that older adults with good hearing have reduced
temporal resolution compared to younger adults
with good hearing (i.e., temporal resolution de-
clines with age), and that there is no difference in
temporal resolution abilities of older listeners with
good hearing and those with hearing loss (i.e., re-
duced temporal resolution in older adults is unre-
lated to hearing loss) (Abel et al., 1990; Moore and
Peters, 1992; Fitzgibbons and Gordon-Salant,
1994; Schneider et al., 1994).
Older adults also experience challenges with
speech perception. A common complaint among
older individuals is that everyday conversations
are hard to understand — speakers seem to mum-
ble or speak too fast, and cannot be understood in
noisy situations (Schneider et al., 2002). Even
when adults with good hearing sensitivity are in
quiet conditions, they may not fully understand all
words or speech sounds (Schneider et al., 2002).
Consistent with this subjective loss of cognitive
efficacy, experimental studies have shown that
older adults make more errors than younger adults
in recognizing and remembering fast speech (Pi-
chora-Fuller et al., 1995; Wingfield et al., 1999;
Schneider et al., 2002), speech under noisy condi-
tions (Humes and Roberts, 1990; Humes and
Christopherson, 1991; Murphy et al., 2000), and
speech lacking contextual cues (Gordon-Salant
and Fitzgibbons, 2001). Moreover, these deficits
are apparent even when controlling for hearing
loss (Dubno et al., 1984; Cheesman et al., 1995;
Gordon-Salant and Fitzgibbons, 2001; Gordon-
Salant and Fitzgibbons, 1993), suggesting that pe-
ripheral sensory loss is not the only factor in this
Although the neurological origins of these defi-
cits in speech perception are not yet well under-
stood, significant insights have been gained from
studies comparing younger and older adults. In
studies of speech rate and background noise on
speech recognition in older and younger adults,
the performance of younger adults when listening
to rapid (Wingfield and Lindfield, 1995; Wingfield,
1996) or noisy (Schneider et al., 2002) speech was
similar to that of older adults listening to slower
speech or speech under quiet conditions. These
results suggest that the neurological dysfunctions
in older adults act to lower the temporal fidelity of
and add noise to auditory input.
These and other studies have gone on to dem-
onstrate that these perceptual deficits have delete-
rious consequences for memory and cognitive
performance in older adults. Several studies of
the role of noise in speech processing have shown
that when young adults performed verbal memory
matched their sensory performance to the rela-
tively poor performance of older adults, their
memory abilities were equivalent to those of older
adults (Murphy et al., 2000; Schneider et al., 2002).
These results demonstrate that the poor sensory
function of older adults can significantly impair
their memory for speech.
In aggregate, there is a large and increasingly
deep body of knowledge from studies of auditory
psychophysics, perception, and cognition in older
individuals, which argues that degraded represen-
tational fidelity and noise in the central auditory
system is responsible for crucial auditory process-
ing deficits seen in older adults, and that these
sensory processing deficits can in turn cause mean-
ingful deficits in memory and cognitive functions
(Schneider and Pichora-Fuller, 2000). The logical
implication of this literature and the literature of
brain plasticity is that a training program designed
to improve the fidelity of the representation of
auditory stimuli in older adults should lead to
substantially improved cognitive and memory per-
formance in tasks involving the auditory system.
This conclusion is not in conflict with studies that
have shown that peripheral sensory loss also con-
tributes to cognitive deficits in older adults. How-
ever, we contend that central processing deficits
could be remediated with a plasticity-based train-
ing program while there is no known remedy for
peripheral sensory loss.
Changes in the central visual system contribute to
cognitive deficits in aging
Age-related changes in the eye, whether caused by
specific pathologies (e.g., cataracts, glaucoma,
macular degeneration) or generalized issues in ag-
ing (e.g., decline in the number of rods), can re-
duce visual acuity, contrast sensitivity, color
vision, and light sensitivity. This decline in the
visual peripheral sensory system without question
contributes to a less accurate and more noisy rep-
resentation of the visual world in older adults.
However, as in the auditory system, there is a
growing body of evidence that shows that the de-
cline in various basic visual abilities occurring with
age is independent of optical factors (Morrison
and McGrath, 1985; Owsley et al., 1985; Nameda
et al., 1989), suggesting that a deteriorated central
visual sensory system produces a noisy represen-
tation of the visual world that substantially con-
tributes to a decline in visual cognitive processing
(Schneider and Pichora-Fuller, 2000).
Impairments in central visual perception include
a difficulty in detecting and discriminating between
static peripheral targets; problems with motion per-
ception; an impaired ability to track and visually
process moving objects (Sharpe and Sylvester, 1978;
Scialfa and Kline, 1988; Kline, 1994; Olincy et al.,
1997), difficulty in identifying and discriminating
letters (Akutsu et al., 1991), trouble inferring three-
dimensional structure from two-dimensional im-
ages (Plude et al., 1986; Robins-Wahlin et al.,
1993), and difficulty in mental rotation (Dollinger,
1995); and poorer face discrimination abilities
(Owsley et al., 1981; Eslinger and Benton, 1983;
Koss et al., 1991; Cronin-Golomb et al., 2000).
Older adults show deficits in backward masking
tasks involving visual stimuli (Kline and Birren,
1975; Kline and Szafran, 1975; Walsh, 1976) and in
flicker fusion tasks (Kim and Mayer, 1994), both of
which suggest abnormalities in temporal integra-
tion. They are also less sensitive to object move-
ment (Elliot et al., 1989; Kline et al., 1994), and are
less able to detect coherent motion (Trick and Sil-
verman, 1991; Wojciechowski et al., 1995). In com-
bination, these spatial and temporal psychophysical
deficits suggest that problems in the central visual
system substantially impair even the earliest stages
of visual processing in older observers.
As in the auditory system, a growing number of
studies suggest that age-related problems in visual
cognition, visual memory, and visuospatial skills
can be traced to degraded central visual system
processing. Lowering the contrast of stimuli in
visual neuropsychological tasks to mimic the con-
trast deficit of older adults decreases the cognitive
performance of younger adults to that of a typical
50–55-year-old (Spinks et al., 1996). Improving the
contrast of stimuli significantly increases the per-
formance of older adults in reading comprehen-
sion (Echt and Pollack, 1998). When noise is
added to visual stimuli to mimic the poor discrim-
ination abilities of older participants, the ability of
younger participants to identify visually presented
words declines, such that it can no longer be dis-
tinguished from the performance of older adults
(Speranza et al., 2000).
Although this literature is less well developed
than research in the auditory system, these findings
argue that degraded representational fidelity and
noise in the central visual system are responsible
for crucial visual processing deficits seen in older
adults, and that these sensory processing deficits
can in turn cause meaningful deficits in memory
and cognitive functions. Together, these literatures
suggest that degraded representational fidelity and
consequent cognitive and memory deficits are gen-
eral operating principles of the aging brain, and
that training programs targeting each sensory sys-
tem in turn should lead to substantially improved
cognitive and memory performance.
Physical and functional deterioration in the brain
can be slowed, arrested, and reversed
As already mentioned, there now exists ample sci-
entific support for the idea that the brain has a
lifelong capacity for plasticity. We have just
described how reduced schedules of activity, noisy
processing, weakened neuromodulatory control,
and negative learning work in concert to signifi-
cantly impair cognition in older adults. If these
conditions were reversed, would it be possible to
restore cognitive functioning in older adults that
had experienced significant decline? Evidence from
human and animal research strongly indicates that
substantial physical, sensory, cognitive, and motor
recovery is possible.
Human studies have documented that cognitive
activity wards off future decline with aging, per-
haps by building what others refer to as a ‘‘cog-
nitive reserve,’’ which may be a euphemism for
strengthened brain processing machinery (Whalley
et al., 2004). In the past few years, various well-
designed, prospective studies have shown that par-
ticipation in cognitively stimulating activities (Wil-
son et al., 2002, 2003), intellectually complex work
(Schooler et al., 1999), and leisure activities (Scar-
meas et al., 2001; Verghese et al., 2003) during
adulthood reduces the risk of loss of cognitive
abilities in later life. Involvement in cognitive ac-
tivity would clearly counter each of the four con-
ditions believed to contribute to functional decline
in the aged: cognitive activity is the opposite of
brain disuse; it would strengthen the brain process-
ing machinery to ensure less noisy processing; it is
likely to be done in behavioral contexts (e.g., at-
tention, reward, novelty) that strengthen neuro-
downwards spiral of negative learning.
Human behavioral studies have shown that
losses in sensory, cognitive, and motor processing
can be reversed. Specific training can refine de-
graded representations in the sensory and motor
cortices (Bao et al., 2003; Byl et al., 2003), improve
signal-to-noise conditions for neuronal represen-
tations and distributed neuronal response coher-
ence (Deutsch et al., 2000; Nagarajan et al., 2000;
Nagarajan and Merzenich, unpublished manu-
script), and restore the effectiveness of long-range
feed-forward connections (Temple et al., 2000,
2003; Olesen et al., 2004). Neglected cognitive
skills can be strengthened and refined by use (Wolf
et al., 2001; Dick et al., 2003).
A large and growing body of animal studies
have shown thatan
designed to be cognitively stimulating promotes
positive plastic changes in the brain and can re-
verse the negative physical, sensory, and cognitive
aspects of aging. (For reviews, see Diamond, 2001;
Mohammed et al., 2002; Lewis, 2004; Li and Tang,
2005). These studies have shown that new neuron
production can be increased in areas where cell
division and proliferation are possible (e.g., the
hippocampus) (Kempermann et al., 1997, 2002;
Lemaire et al., 1999) and apoptotic cell death can
be reduced (Young et al., 1999). Gray matter can
be thickened: dendrites, spines, and synapses in the
cortical neuropil can be elaborated (Diamond et
al., 1975; Greenough et al., 1978, 1985; Floeter and
Greenough, 1979; Green et al., 1983; Diamond et
al., 1985; Mohammed et al., 1993; Rosenzweig and
Bennett, 1996; Mattson et al., 2001; Kleim et al.,
2002; Mohammed et al., 2002; Frick and Fern-
andez, 2003; Frick et al., 2003). Even myelination,
which had previously been thought to be irrecov-
erable in the adult brain, can probably be restored
Piraino et al., 2005).
Neuromodulatory control systems, weakened
during aging, can be strengthened by behavioral
training. Such training can up-regulate the meta-
bolic states and the production and release of key
neurotransmitters of limbic system and basal gan-
glion neurons (Nakamura, 1991; Bezard et al.,
2003; Cohen et al., 2003; Tillerson et al., 2003).
Through their more effective and intense reactiva-
tion, cortical and subcortical terminals of modu-
latory control nuclei can be elaborated (Wolfman
et al., 1994; Spengler et al., 1995). Under optimal
environmental conditions, almost every physical
aspect of the brain can recover from age-related
losses. A degradation of vascular dynamics attrib-
utable to ACh control of nitric-oxide-related en-
zymes believed to presage AD pathology may be at
least partially overcome (Hilbig et al., 2002). A
recent study demonstrated that even certain path-
ological hallmarks of AD (e.g., amyloid bodies)
could be ameliorated by exposure to an enriched
environment (Lazarov et al., 2005).
Numerous studies have shown that behavioral
training or exposure to a novel environment can
refine representations in the sensory, somatosen-
sory, and motor cortices of animals (Wang et al.,
Salehet al., 2003;
1995; Nudo et al., 1996; Xerri et al., 1996, 1998;
Byl et al., 1997; Coq and Xerri, 2001; Nudo et al.,
2003). In addition, exposure to an enriched envi-
ronment can improve memory and learning in
older animals (Doty, 1972; Cummins et al., 1973;
Berman et al., 1988; Kobayashi et al., 2002; Frick
and Fernandez, 2003; Frick et al., 2003; Fernandez
et al., 2004), and those that are cognitively im-
paired (Rampon et al., 2000; Arendash et al., 2004)
perhaps by inducing synaptic structural changes
that enhance memory and learning (Kempermann
et al., 1997; Rampon et al., 2000). The negative
effects of earlier exposure to an impoverished en-
vironment can be at least partially reversed by ex-
posure to an enriched environment later in life
This very large group of studies shows that sub-
stantial recovery is possible in the aging brain:
physical losses can be reversed, and many aspects
of sensory, motor, neuromodulatory, and cogni-
tive systems can be restored to optimal levels of
functioning. Additional experimental and applied
studies conducted over the next several decades
will more clearly define the extent of positive plas-
tic changes that may be achieved, and the condi-
tions under which these changes can be maintained
For brevity’s sake, we have selected four exam-
ples of brain plasticity research for more in-depth
discussion. In each example, we describe how neg-
ative plasticity processes degrades sensory, cogni-
tive, or motor functioning and how positive
plasticity has restored behavioral and neurologi-
Age-related decline in rat
Negative plasticity in elderly rat
As a rat nears the end of its life, it loses control of
its forepaws. This loss is manifested, for example,
by increasing functional difficulty in food object
retrieval and manipulation. Across the same pe-
riod of time, the rat’s mobility becomes progres-
sively degraded: its gait becomes slow and clumsy.
If the rat lives long enough, it will lose the ability
to control its hind legs in locomotion; turn its feet
over so that their hairy dorsal surface is touching
the cage surface (apparently because contact of the
glabrous surface with the ground is painful); and
drag itself around its environment using its fore-
legs. The aged rat’s difficulty in feeding itself con-
tributes to the rat’s death shortly into its third year
The neuronal basis of the rat’s loss of control
of its paws and limbs is revealed through recon-
structing the cortical maps of the rat’s paw sur-
faces in the somatosensory cortex and movement
representations in the motor cortex (Godde et al.,
2002). The cortical maps of the paw surfaces and
movement representations are profoundly de-
differentiated and noisy in the old rat, with cor-
tical neurons responding weakly and unreliably
(Coq and Xerri, 2000; Godde et al., 2002). In such
a cortex, inputs have weak salience and poorly
engage nondeclarative memory processes crucial
for sustaining complex, normal forepaw grasp be-
In the 2-year-old rat, physical signs of deterio-
ration are broadly expressed in the somatosensory
and motor cortices, and in subcortical thalamic
and limbic system nuclei (Godde et al., 2002). The
rat’s gray matter is thin; the neuropil is shrunken;
neurons have less complex dendrites and fewer
spines; synapses are less elaborate; intracortical
axons are less complexly branched; and myelina-
tion is reduced. In sum, the cerebral cortex, and
the subcortical thalamic and modulatory control
nuclei that support it, are slowly dying. This de-
terioration in the cortex and the modulatory sub-
cortical nuclei is paralleled by slower learning rates
and lower learning ceilings in aged animals. A
degradation of dynamic control of vascular per-
fusion almost certainly is due to the inexorable
degradation of modulatory control system (nu-
cleus basalis, locus coeruleus) function.
Positive plasticity-based training reverses
Many of these destructive functional and physical
changes in the aged rat can be reversed through
appropriate, targeted, intensive retraining of the
rat’s forepaws, and postural and mobility control
(Churs et al., 1996; Reinke and Dinse, 1999). The
training that was applied in this model engages the
rat in behaviors that progressively reestablish re-
fined representations of the paw surfaces and of
paw and limb movements. Rats were trained to
cross-rotating bumpy rods to retrieve a food re-
ward. The difficulty of the task was progressively
increased by narrowing the bar and increasing the
rate of rotation. Successfully crossing the bar de-
livers significant spatiotemporally complex input
to the somatosensory system while demanding
substantial attentional resources and requiring
complex motor output.
Following training, the reversal of these physical
changes is revealed by the restoration of relatively
normal forepaw maps in the primary somatosen-
sory and motor cortices. The restoration of the
cortical maps in these rats translated into func-
tional recovery: trained rats recovered their ability
to manipulate food objects with their forepaws
and control their limbs in locomotion. With these
functions restored, the rats lived 4–5 months
longer than would otherwise be the case.
changes following such training is still incomplete,
the limited studies conducted to date and other
related experiments show that the cortical gray
matter can be thickened, largely through an in-
crease of neuropil; dendrites can be elaborated; the
resting metabolism of cortical and subcortical ar-
eas can be increased; the reversal of the loss of
dynamic brain perfusion and the up-regulation of
a nitric-oxide-related enzyme indicates that ACh
production and nucleus basalis function becomes
This model shows that appropriately structured
and intensive behavioral engagement can substan-
tially reverse both physical and functional deteri-
oration of complex forebrain-mediated behaviors
in older animals. While the changes induced in
these experiments do not completely physically or
functionally restore youthful behaviors, the posi-
tive, normalizing changes that do occur are ex-
pressed on a very large scale. Moreover, the scope
of the training employed in these experiments has
been very limited. More intensive and elaborate
training should produce considerably more pow-
erful and complete reversals of functional and
Remediation of acquired hand movement disorders
Negative plasticity in overtrained humans and
Acquired hand movement disorders (e.g., focal
hand dystonia) often arise from specific forms of
occupational hand use in humans (e.g., playing the
piano, keyboard data entry). We have induced ac-
quired movement disorders in monkeys through a
negative plasticity scenario, and shown that the
acquired loss of motor control is a consequence of
learning-driven dedifferentiation of sensory and
motor cortex representations in the forebrain. Hu-
mans with acquired hand movement disorders
show the same pattern of degraded hand cortical
maps as were seen in the primates. This is a pre-
dictable consequence for any behavior in which
there is a competitive ‘‘winner’’ achieved through
very stereotypic excitation of skin surfaces, or in
which nearly identical, larger sectors of skin are
consistently simultaneously activated (Wang et al.,
1995; Byl et al., 1996; Nudo et al., 1996; Xerri et
al., 1996; Elbert et al., 1998; Merzenich, 2001).
Inputs that are nearly simultaneously engaged by a
very stereotypic or broadly engaging stimulus will
be mutually costrengthened, and their integration
will result in larger receptive fields. By natural
plasticity processes, the separate, normally differ-
entiated representations of sensory inputs from
fingers and palmar surfaces can be largely sub-
sumed by an undifferentiated cortex in which al-
most any stimulus excites almost any hand-zone
neuron (Wang et al., 1995; Byl et al., 1996; Elbert
et al., 1998). The behaviors that generate dediffer-
entiation of cortical hand representations in mon-
keys are exactly the kinds of hand-use behaviors
that generate acquired hand movement disorders
in humans (Byl et al., 1996; Byl, 2004).
Positive plasticity-based training reverses acquired
hand movement disorders
Using strategies that have much in common with
the training conducted on rats described earlier, we
developed a behavioral training program to re-
verse chronic hand dystonias in humans. The
training program uses multiple tasks designed to
engage the broad types of sensory inputs that pro-
vide feedback for fine hand motor control (Byl et
al., 2000). Training begins with sensory discrimi-
nation tasks and progresses to graded movements,
sensorimotor activities, motor control activities of
daily living, fine motor practice, and finally target-
specific task practice. Training tasks are progres-
sively difficult and modified by the context, con-
tent, force, and mobility of the stimulus while
being targeted to a restricted skin surface. All
training activities require substantial attention,
and success is actively rewarded. With training
applied for about one hour per day for 1–2
months, most patients (the majority of whom
have been professional musicians) recover rela-
tively normal hand use (Byl and McKenzie, 2000;
Byl et al., 2003). Gains with training were inde-
pendent of the age of the individual. Initial brain-
imaging studies indicate that training results in
more normal representational topographies and
sensory-evoked responses in participants.
These studies strongly indicate that training
programs can reverse functional and behavioral
losses in fine motor control and sensory input from
the hand in adult humans. As in trained rats, such
training rerefines grossly dedifferentiated maps of
sensory inputs and movement governing sensory-
guided motor behaviors.
Up-regulating dopamine cell function in adult rats
Limb disuse exacerbates Parkinsonian symptoms in
Parkinson’s disease is characterized by progressive
motor impairment caused by degeneration of do-
paminergic (DA) neurons in the nigrostriatal sys-
tem (Zigmond and Burke, 2002). Researchers can
induce hemi-Parkinsonian symptoms (e.g., slow-
ness or loss of movement on the affected side,
preferential use of the nonaffected side) in the rat
by unilaterally destroying a percentage of DA
neurons in the basal ganglion. These Parkinsonian
symptoms are exacerbated by restraining use of
the rat’s impaired forelimb (Tillerson et al., 2002),
suggesting that a decrease in physical activity not
only is a symptom of the disease but also
through negative plasticity processes.
to the disease process, perhaps
Reversing behavioral and neurochemical losses
through exercise and exposure to a complex
A series of studies has demonstrated that behavi-
oral and environmental conditions can reverse the
behavioral and neurochemical losses in Parkin-
son’s-induced rats. One study forced the rat to use
one of its forelimbs for a period of time before
inducing behavioral and neurochemical deficits in
the forced-use limb (Cohen et al., 2003). This
forced exercise attenuated the loss of striatal DA
neurons and its metabolites in response to induc-
ing degeneration of DA neurons in the rat. A sec-
ond study forced the rat to exercise its impaired
limb after it had been injured, and showed that this
exercise reversed the induced behavioral deficits
(Tillerson et al., 2003). This recovery from lesion-
induced behavioral deficits is paralleled by an at-
tenuation of the depletion of striatal DA neurons
in rats (Tillerson et al., 2003). The gains made
during the exercise program are lost once the ex-
ercise is discontinued, indicating that continuous
‘‘therapy’’ is needed to maintain improvements
(Woodlee and Schallert, 2004). Other studies have
shown that a motor learning environment (a
‘‘rich’’ environment in which objects in the rat’s
cage are changed daily) can prevent the progres-
sion into Parkinson’s-like symptoms in rats whose
DA neurons have been destroyed (Bezard et al.,
2003; Faherty et al., 2005). This prophylaxis
is almost certainly attributable to the heavy,
learning-activity-based engagement of surviving
Together, these studies of behavior and neuro-
degeneration in rat models of Parkinson’s disease
strongly indicate that the health and vitality of the
DA neuromodulatory system is regulated by its
own functional activity. A training program of
forced use blocks the progression of cell loss and
the exacerbation of the down-regulation of DA
neuron production and release attributable to mo-
tor dysfunction, while symptom progression is re-
versed. This result opens the door for the
investigation of active training programs that go
beyond forced use to revivify DA as well as other
neuromodulatory control systems in the brain.
Reversing language learning and reading
impairments in children and young adults
Negative plasticity in children with learning and
Over the past two decades, brain plasticity studies
in monkeys and rats have led to the hypothesis
that impaired language development commonly
leading to reading problems is often a consequence
of an early plasticity outcome by which the cortex
organizes its aural speech processing machinery to
specialize for the representation of a degraded
(noisy) speech model (Merzenich et al., 1993,
1998a, b; Merzenich and Jenkins, 1995). Many in-
herited neurological faults, as well as consistently
degraded inputs from the inner ear, would be ex-
pected to result in a muffled or noisy speech
model. The ‘‘fuzzy’’ phonemic representations be-
haviorally and physiologically documented in lan-
guage-impaired children are consistent with this
scenario. Moreover, theoretical and animal models
of this developmental scenario predict problems in
speech representation and language function that
are consistent with the behavioral deficit picture
presented in most language-impaired and reading-
Positive plasticity-based training reverses and young
adults with learning and reading impairments
More than a decade ago, researchers posited that
these processing deficiencies might be corrected in
individuals of any age by appropriate, targeted,
and intensive behavioral training. A seven-exercise
training program was developed, based on the
principles of brain plasticity cited above, to adap-
tively renormalize speech feature representation
and generalize improved processing abilities to all
of the syntactic relationships (contexts) that are
needed for facile speech reception. About five
thousand individuals spanning from about 4 to 18
years of age were given standard assessments test-
ing all aspects of speech reception and language
usage before and after training. The results dem-
onstrated large-scale improvements on virtually
every language-related cognitive or memory task
(Merzenich et al., 1996a, 1998a, b; Tallal et al.,
1996a, b ). Benefits generalized to aural speech
assessments of memory, cognition, and ‘‘process-
ing efficiency,’’ and to language usage (Tallal et al.,
1996b; Merzenich et al., 1998a). Benefits from
training were independent of the age of the indi-
viduals (Tallal et al., 1996b; Merzenich et al.,
More than 600,000 children and young adults
have now been trained with this program. The
conclusion that the training increases representa-
tional salience was confirmed by neurological
studies that longitudinally reconstructed dynamic
cortical responses. fMRI studies revealed that the
originally very abnormal response patterns re-
corded while these children performed key reading
and language behaviors could be consistently re-
stored to a more normal form after training. Brain
imaging and human recording studies have dem-
onstrated that the neuronal representations of au-
ral speech inputs are substantially more salient,
powerful, and reliable after training (Deutsch et
al., 2000; Nagarajan et al., 2000; Temple et al.,
2001, 2003; Hayes et al., 2003 Nagarajan and Me-
rzenich, unpublished manuscript). Trained chil-
dren had higher amplitude and more coordinated
magnetically recorded responses to the sound
parts of words represented within the primary au-
ditory cortex. Event-related potential studies have
shown that the discriminable differences that dis-
tinguish confusable speech phonemes were renor-
malized in the average trained child. The level of
coherent gamma activity evoked in memory-re-
lated tasks, which was grossly abnormal before
training, became normal after training in children
for both quiet and noisy background conditions.
The four studies described above provide com-
brain-plasticity-based training programs reverse
noisy processing, renormalize temporal and spatial
integration constants, enhance functioning of ne-
uromodulatory systems relevant to learning and
memory, and improve cognitive performance in a
variety of animal and human models of neurolog-
ical dysfunction. Each of these dysfunctions is
directly relevant to the cognitive challenges faced by
older individuals, and each suggests specific aspects
of training programs that would be relevant to
slow, arrest, or reverse age-related cognitive decline.
Current approaches to treating cognitive decline
have limited applicability and efficacy
Two general strategies have been pursued to amel-
iorate the cognitive changes seen in aging: phar-
approaches have focused on blocking and possi-
bly reversing the pathological processes that con-
tribute to the physical and functional deterioration
of the brain in clinically defined conditions, typ-
ically AD (and now, MCI). By targeting one of
these hypothesized pathological processes, such as
vascular changes, amyloid deposits, or the levels of
important neuromodulatory transmitters, it is
hoped that adults diagnosed with such diseases
can retain memory abilities, cognition, executive
functions, and movement control. Behavioral ap-
proaches have generally focused on teaching spe-
cific strategies in memory and attention to healthy
older individuals as well as to those diagnosed with
AD or MCI.
The most frequently applied drugs in patients with
MCI and AD are acetylcholine esterase (AChE)
inhibitors, which are designed to enhance levels of
ACh in the brain by blocking the normal ACh
breakdown. Positive benefits provided by AChE
inhibitors have been modest. A recent systematic
review of clinical trials of AChE inhibitors found
that only 10–20% of patients with AD benefited
from these drugs, and that high rates of noncom-
pliance among treated patients were commonplace
(Kaduszkiewicz et al., 2005). Additionally, the
benefit from treatment is only temporary; on an
average, patients improve over 3–6 months and
return to pretreatment status 9–12 months after
treatment initiation (Johannsen, 2004). In patients
with MCI, donezepil slows the progression to AD
for 12 months, after which progressive functional
decline continues at the same rate as control pa-
tients (Peterson et al., 2005). Even with the poten-
tially dramatic costs savings that come from
delaying AD onset, the cost effectiveness derived
from AChE inhibitors is uncertain because of the
high cost of these drugs and the fact that all pa-
tients still advance, with perhaps a brief delay, to
the more advanced stages of AD (Foster and Plo-
Research scientists have been working intensely
for more than a decade to improve the therapeutic
landscape for MCI and AD treatment. There are
more than 100 drugs now in the pipeline targeting
many different pathological processes believed to
contribute to cognitive decline. Other research ap-
proaches have investigated strategies for promot-
ing neuron regeneration or replacement using
genetic modification or stem cell-based approaches
designed to reinvigorate, protect, or grow more
new neurons, or to provide new sources of neurons
for deteriorating brains. While these approaches
are hopeful, no practical strategy is in hand, and
Food and Drug Administration (FDA) approval
for their use likely will not happen for a number of
These investigational paths are promising and
will almost certainly lead to improved treatments
for AD and perhaps also for MCI. At the same
time, none of these future therapies addresses the
tremendous problem of normal age-related cogni-
tive decline. In addition, even novel drugs may be
only marginally helpful for patients with AD or
MCI because they usually address only a single
dimension of the complex multidimensional proc-
esses of brain deterioration in aging.
A number of studies have suggested that cognitive
activity or stimulation could be a protective factor
against the functional losses of aging. Because
many of these studies were cross sectional, the
causal relationship between stimulation and cog-
nitive performance was difficult to establish. In the
past few years, however, a number of well-con-
trolled longitudinal studies have shown that par-
ticipation in cognitively stimulating activities
(Wilson et al., 2002, 2003), intellectually complex
work (Schooler et al., 1999), and leisure activities
(Scarmeas et al., 2001; Verghese et al., 2003) dur-
ing adulthood reduces the risk of loss of cognitive
abilities in later life. For example, one study meas-
ured older individuals’ self-reported frequency of
participation in a variety of activities, with each
assigned an objective level of cognitive stimula-
tion, over a period of 5 years. Older individuals at
the highest level of cognitive activity (90th per-
centile) experienced a 35% less decline in their
cognitive abilities than individuals with low levels
of cognitive activity (10th percentile) (Wilson et
al., 2003). The results of these studies are entirely
consonant with the negative brain plasticity view-
point on age-related cognitive decline, as the cog-
nitive stimulation quantified in these studies would
directly affect issues of disuse, noisy processing,
weakened neuromodulatory control, and negative
To date, various behavioral training strategies
have been proposed to remediate age-related cog-
nitive or memory impairment. The studies evalu-
ating these approaches typically have applied
strategy learning to enhance memory function in
healthy older adults, with some evidence for suc-
cess (Yesavage, 1983, 1989; Verhaeghen et al.,
1992; Caprio-Prevette and Fry, 1996; Verhaeghen
and Marcoen, 1996; Mohs et al., 1998; Glisky and
Glisky, 1999; McDougall, 1999). However, these
approaches are limited by their approach, and
have generally shown small effect sizes, poor
maintenance over time, and no generalization be-
yond the trained skill (Gatz, 2005). More recently,
several small trials have been completed that as-
sessed the impact of behavioral training in patients
with AD (Davis et al., 2001; Clare et al., 2002;
Loewenstein et al., 2004). These studies demon-
strated that significant short-term improvements
in certain cognitive functions were achievable even
in this severely impaired population, although the
extent to which these changes generalize more
broadly to cognitive and everyday functioning has
not yet been established.
It is difficult to fully evaluate the promise of any
single behavioral approach as few large, rigorous
studies have been conducted to evaluate proposed
training programs. A notable exception is the AC-
TIVE (advanced cognitive training in vital elderly)
study, a randomized, controlled trial that evalu-
ated three behavioral training programs (in speed
of processing, memory, and reasoning) in older
adults (Ball et al., 2002; Edwards et al., 2002).
Training in any one of these areas improved per-
formance in that area, but this did not translate to
improved everyday functioning possible due to a
These studies clearly demonstrate the promise of
training-based approaches; however, we believe
that a more intensive and comprehensive training
program explicitly based on the principles of brain
plasticity would most likely achieve more robust
benefits than have been seen to date. In general, we
do not expect that compensatory strategies, or
training that targets only higher order cognitive
functions, will achieve powerful, sustained effects
because such strategies do not address the fact that
age-related decline in sensation, memory, cogni-
tion, and guided motor control has more funda-
mental roots in degraded brain processing. Until
optimal programs are developed, we believe it is
unlikely that a standard of care for a behavioral
approach to cognitive decline in aging will emerge.
A novel training program to enhance memory and
cognition in the aged
The negative plasticity perspective on the origins
of cognitive decline in older individuals immedi-
ately suggests a novel approach to treating such
cognitive losses. We have built an initial version of
a brain-plasticity-based training program explicitly
designed to intervene in the downward cycle of
negative plasticity by enhancing signal-to-noise
ratios and improving neuromodulatory function,
while also increasing overall brain stimulation and
correcting negative learning. This brain-plasticity-
based training program operates on four basic
Strongly engage the brain
To reverse underlying disuse and drive brain plas-
ticity, the program strongly engages the brain with
demanding exercises and a daily training schedule.
Thousands of trials are required to ensure that the
representations of behaviorally important inputs
are coselected or integrated to create robust and
complex stimulus-specific and action-event-specific
neuronal responses in the cortex. In addition, pro-
gram exercises employ an adaptive training ap-
proach that begins with simplest tasks where there
is a high likelihood of success, and proceeds adap-
tively and incrementally with a series of exercises
in which the task demands are made gradually
more difficult. Performance within each compo-
nent is overlearned through repetitive, successful
practice with rewards.
Renormalize noisy processing
The training program aims to improve the ability
of the brain’s auditory and speech systems to en-
gage memory and cognitive systems by enhancing
their representational fidelity. Training tasks and
stimuli are designed to sharply increase the fidelity
and power of representations of complex, dynamic
inputs; decrease spatial and temporal integration
constants; and directly assure the effective gener-
alization of highly spatially and temporally refined
processing to all of the contexts for facile and effi-
cient ‘‘complex’’ (i.e., real) signal reception and
Enhance neuromodulatory function
Program exercises are also designed to strengthen
the basic function of each neuromodulatory sys-
tem component essential for the regulation of
learning and memory. Dimensions of behavioral
context (arousal, attention, reward, novelty) affect
the release of specific neurotransmitters (ACh, do-
pamine, serotonin, norepinephrine, endogenous
opioids) that in turn enable, amplify, and shape
plasticity in the adult brain (Merzenich and Jenk-
ins, 1993; Merzenich et al., 1996b; Cahill and
McGaugh, 1998; Kilgard and Merzenich, 1998a,
b; Bao et al., 2001; Merzenich, 2001; Gibbs and
Summers, 2002; Kilgard and Merzenich, 2002;
Weinberger, 2003; Schweighofer et al., 2004). For
training to be maximally efficient, attentional, re-
ward, and novelty detection system engagement
must be closely controlled to achieve near-opti-
mum learning rates. Exercises are specifically de-
signed to engage cholinergic attention systems by
requiring temporally focused periods of attended
behavior with the goal of stimulating the nucleus
basalis in every training exercise cycle (Kilgard and
Merzenich, 1998a). Rewards are delivered several
thousand times in each daily training session to
exercise DA systems in the ventral tegmental area
and the substantia nigra (Backman and Farde,
2005). Serotonergic and noradrenergic novelty de-
tection systems are targeted with similar frequency
to stimulate the locus coeruleus and the dorsal
Strengthen critical life skills
Besides targeting fundamental aspects of brain
plasticity, the program aims to guide users out of
learned behaviors with negative consequences for
brain health and into new behaviors that positively
reinforce their enhanced brain function.
Structure of the training program
The overall program is composed of six interre-
lated training exercises that in aggregate span the
acoustic organization of speech. The exercises in-
clude the following:
? ‘‘High or Low’’: frequency-modulated sweeps
(time-order judgment task)
? ‘‘Tell Us Apart’’: syllables (discrimination
? ‘‘Match It’’: short words with confusable
stop-consonants (spatial-match task)
? ‘‘Sound Replay’’: short words with confusa-
ble stop-consonants (forward-span task)
? ‘‘Listen and Do’’: complete spoken sentences
? ‘‘Story Teller’’: complete spoken narratives
Each exercise employs a combination of acous-
tically emphasized stimuli, adaptive training pro-
cedures, and intensive engagement of attentional,
reward, and novelty-detection systems. In aggre-
gate, these exercises are designed to improve the
accuracy and the speed with which the brain proc-
esses speech information, and reengage the neuro-
modulatorysystemsthatgate learning and
memory. By doing so, we hypothesize that the
representational salience of speech input is im-
proved in older brains, that the functional connec-
tivity (feed forward and feedback) between sensory
and memory systems would be improved, and that
as a result, speech reception accuracy, speed of
processing, and memory for speech would improve.
Pilot results from randomized, controlled study
As an initial test of this training program, we con-
ducted a randomized, controlled, pilot study de-
signed to assess the usability of this kind of
demanding, intensive program in a classroom en-
vironment by older individuals, and to estimate
the effect size of intervention on standardized ne-
uropsychological measures of memory.
We recruited 94 individuals from a local ac-
tive living community (Rossmoor, CA; aged
63–94, mean age 79.9, mean 16.3 years of
education) and, under the authority of an
Institutional Review Board, enrolled them
into a randomized three-arm study. Partici-
pants in the first study (intervention) arm
used the program in a classroom setting at its
recommended dosing (60min/day, 5 days/
week, 8 weeks). Trainers supervised the
classroom to provide technical assistance
and general encouragement. Participants in
the second study arm (active control) used
the same computers and classrooms to watch
and listen to educational material presented
on DVD. This activity was comparable to
program as an active control, in that it en-
gaged participants in an engaging auditory
and visual learning activity, was time- and
intensity-matched to the intervention, and
kept participants blind as to their active con-
trol status. The active control attempt ex-
plicitly to control neither for the adaptively
and progressively challenging nature of pro-
gram exercises nor for their intense reward
and attentional engagement, as we consider
those key ‘‘active ingredients’’ in the inter-
vention. Participants in the third study arm
(no-contact control) engaged in no-study ac-
tivities during the training period.
All participants completed identical neuro-
psychological assessment batteries before
and after the training period. The primary
instrument in this battery was the RBANS
(repeatable battery for the assessment of ne-
uropsychological status), a standardized in-
strument composed of 12 individual subtests
covering areas of immediate and delayed
memory, attention, visuospatial function,
and spoken language. The RBANS has al-
ternate forms designed and tested to be
equivalent; we used these alternate forms in
the pretraining and posttraining visits to
minimize test?retest effects. The assessments
in the neuropsychological battery were very
different from the training exercises in the
remedial program, ensuring that any changes
seen in the assessments would represent true
generalization of improvement rather than
training to the assessment.
Study participants were required to be 60
years of age or older, have a mini-mental
state examination (MMSE) of 24 or higher,
and have RBANS overall scores to be gen-
erally representative of normal aging (taken
to be 2 standard deviations within the nor-
mative population range, 70?130), and not
self-identify with dementia.
Fifty-one participants meeting these crite-
ria were randomized into the experimental
group, and 41 completed all training and
assessment activities. Four participants with-
drew during training citing schedule conflicts;
however, no withdrawals cited dissatisfac-
tion with the training programs as a reason
for withdrawal (six participants had pro-
tocol violations in the posttest condition and
were excluded from analysis). All participants
were able to learn the usage of the exer-
cises with the built-in training and minor as-
sistance from the classroom trainers; by
two weeks into training all participants were
using this experimental program without as-
Twenty-five participants were randomized
into the active control group, and 16 com-
pleted all training and assessment activities.
There were nine withdrawals over the few
days of training citing dissatisfaction with the
active control; however, participants complet-
ing the active control program were generally
satisfied with the material. Eighteen partici-
pants were randomized into the no-contact
control, and 15 completed all assessment ac-
tivities with withdrawals due to scheduling
conflicts or moves. The pretraining groups
were equivalent in age (one-way ANOVA,
p40.4) and educational level (p40.4).
Given that the training program focused
the primary outcome measure was a global
auditory memory score based on the six au-
ditory tests of the RBANS (list learning,
story memory, digit span forwards, delayed
list recall, delayed list recognition, and de-
layed story recall). The global auditory mem-
ory score was calculated by using the
normative RBANS population data to con-
tables allowing the conversion of raw score
data on each test (which generally showed
a strong skew) to scaled score data (opti-
mally normally distributed with a population
mean of 10 and a standard deviation of
3). Delayed list recall and delayed list recog-
nition were summed before scaling to allow
the inclusion of the significantly skewed
and otherwise unscalable delayed list recog-
nition data. The five scaled scores can then
be summed to yield a global auditory mem-
ory score. These look-up tables were then
used to calculate scaled scores and global
auditory memory scores for each participant
in this study for pre- and posttraining as-
Evaluation of the neuropsychological data
showed a significant improvement in the global
auditory memory score within the trained group
(Fig. 2, po0.0005, two-tailed paired t-test) and
nonsignificant trend toward improvement in the
active control group (p40.1) and no significant
effect in the no-contact control group (p40.4).
The magnitude of the effect size in this assessment
was 0.41, or slightly higher than 1/3 of a standard
deviation of enhancement relative to the distribu-
tion in the normal population (the standard devi-
ation of the global auditory memory score in the
normative RBANS population is 9.0).
The improvement in the global auditory mem-
ory score was driven by changes in each of the five
scaled score assessments of auditory memory func-
tion (Fig. 3). This suggests that the effects of
training are broadly distributed across cognitive
systems that relay on speech input, as we would
predict from the design of the training function.
Another approach for quantifying the effect size
is to examine the percentile change in the group
relative to the normal population. Using the distri-
bution of global auditory memory scores from the
normative RBANS population, prior to training,
the trained group scored at the 35th percentile. Fol-
lowing training, the group scored at the 59th per-
Fig. 2. Brain-plasticity-based training enhances global auditory memory scores in older adults. Pre- and post-training global auditory
memory scores for the intervention group (3.7 point change, significant), the active control group (2.4 point change, not significant),
and the no-contact control group (1.8 point change, not significant).
To roughly translate this effect size into a meas-
ure more relevant for populations undergoing the
natural course of normal aging, we might estimate
the rate of cognitive decline per year in the nor-
mative population and compare the effect size in
this study to this rate of decline. We estimated the
rate of decline of memory over time from the
RBANS normative data by developing scaled
scores and a global auditory memory score as de-
scribed above but based on the entire normative
data set including individuals from age 20 to 89
(i.e., not age-stratified by decade as described
above). We then averaged this global auditory
memory score data set with a moving window with
a width of 10 years to plot the rate of decline of
RBANS memory function with age (Fig. 4). This
function shows an initial decline from 25 to ?40
years of age, followed by a broad plateau, which is
followed by a subsequent decline from the age of
62 onwards. The shape of this function suggests
Fig. 3. Memory enhancement is distributed broadly across neuropsychological measures. Pre- and posttraining auditory memory-
scaled scores for the intervention group.
Fig. 4. Age-associated course of decline of memory as assessed with the RBANS. Decline of memory over time was estimated from
RBANS normative data by developing scaled score look-up tables for list learning, story memory, digit span, the sum of delayed list
recall and delayed list recognition, and delayed story recall based on the entire normative data set including individuals from age 20 to
89. Global auditory memory score was calculated as the sum of the scaled scores. Global auditory memory scores were averaged across
a moving window with a width of 10 years to plot the rate of decline of RBANS memory function with age.
that the RBANS, which was designed for mildly
impaired populations, is not likely to be sensitive to
the known cognitive changes that occur in middle
age and early old age. The slope of the decline in
the 62+ period is 0.35 points per year (through age
84, the last year for which the data for full 10 year
window is available in the normative data set). The
training-induced change in this global auditory
memory score is 3.5 points, suggesting that average
improvement in the trained group was roughly
equivalent to ?10 years of memory performance as
assessed with the RBANS. We note that this rough
approach is only appropriate at the group level and
cannot be used to assess changes at the individual
level due to the meaningful test?retest variance for
any given individual in the RBANS (and virtually
all neuropsychological tests).
In summary, these pilot data demonstrate the
promise of this training-based intervention and
provide a proof-of-principle to guide larger studies
with a wider array of assessments that are fully
The losses in sensory, cognitive, memory, and mo-
tor abilities during aging can profoundly affect
everyday functioning and quality of life. Because
the brain experiences physical deterioration coin-
ciding with the onset of cognitive deficits, it has
long been assumed that this atrophy is the sole
cause of the loss of cognitive and memory abilities
in the aged. The science of brain plasticity suggests
a different model of origin of age-related cognitive
decline in which the role of physical atrophy is
complemented by the interactions of brain disuse,
noisy processing, weakened neuromodulatory con-
trol, and negative learning.
We have developed an initial version of a brain-
plasticity-based training program designed to ad-
dress the four potential causes of age-related cog-
nitive decline, and in doing so, to enhance auditory
perception, memory, and cognition in normally
aging individuals. An initial pilot randomized con-
trolled trial demonstrated the feasibility of the ap-
proach, in that the intervention was usable,
learnable, andwell acceptedby thetarget
population, and showed substantial promise in
the effect size of the memory enhancement.
Going forward, further studies with this training
program are required to establish more completely
the functional areas and magnitude of enhance-
ment, and in particular to quantitatively assess
what broader impact the training has on self-re-
port of everyday functioning measures. Structural
and functional brain imaging studies to document
the types and magnitudes of brain plasticity un-
derlying the behavioral changes following training
will be important as well. Finally, we believe that
this training program represents only the first step
in the development of a complete suite of training
programs that, in aggregate, should target the
broad array of sensory, cognitive, memory, and
motor problems that emerge with aging.
We thank William Boschin, Anne Bruce, Bradley
Brummett, Jill Damon, Danielle Doan, Lisa Fa-
ille, Amy Gentile, Jason Minow, and Amy Walt-
hall, for their work in collecting the data in the
study; Jed Appelman, Patrick Brannelly, Jane
Chang, David Cheng, Laurel Cox, Miriam Ha-
shimi, Tisha Hilario, Mark Johnson, Nicholas
Joyce, Jaclyn Kohlriter, Kim Schilling, Cynthia
Warren, Darrell Wayne, and Rick Wood for their
contributions to the execution of the study; and
Natasha Belfor, Bonnie Connor, Joseph Hardy
and Kimberly Tanner for their helpful comments
on the manuscript. We are grateful to Christopher
Randolph for the normative RBANS data used to
derive global auditory memory scores and for dis-
cussions regarding data analysis. We also thank
Marghi Merzenich and Kathy Gowell for help
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