Brain Plasticity and Functional Losses in the Aged: Scientific Bases for a Novel Intervention

Article · February 2006with1,516 Reads
DOI: 10.1016/S0079-6123(06)57006-2 · Source: PubMed
Abstract
Aging is associated with progressive losses in function across multiple systems, including sensation, 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 substantial 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 re-engage 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 conducted 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 significant improvement in memory within the training group (effect size 0.41, p<0.0005), with no significant 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 potentially offer benefit to a broad population of older adults.
2 Figures
A.R. Møller (Ed.)
Progress in Brain Research, Vol. 157
ISSN 0079-6123
Copyright r 2006 Elsevier B.V. All rights reserved
CHAPTER 6
Brain plasticity and functional losses in the aged:
scientific bases for a novel intervention
Henry W. Mahncke
2
, Amy Bronstone
2
and Michael M. Merzenich
1,
!
1
Keck Center for Integrative Neurosciences, University of California, San Francisco, CA 94143-0732, USA
2
Posit 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 dom inate 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, reliabil ity,
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: merz@phy.ucsf.edu
DOI: 10.1016/S0079-6123(06)57006-2 81
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
Introduction
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
deterioration, behavioral and environmental
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
aging therapeutics.
Cognitive decline in aging is progressive and can
become pathological
Cognitive decline is a univers al 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 3 0 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-cons istent 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
82
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 varia bil-
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 decreas e 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 wi th 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 cau se 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 supp ort 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 co mmon 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).
83
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 meta bolic 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-
served anatomical changes (e.g., cell death,
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 negati ve consequences is a crucial
contributor to age-related cognitive decline. As
individuals age, their schedules and strengths of
brain engagement substan tially 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). Evi dence 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 capab le 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 an d 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
84
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 learni ng 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 monk eys 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
85
use of the hand under conditions that require the
accurate processing of sensory inputs and motor
outputs with high degrees of spatiotemporal com-
plexity.
Although this example shows how negative
plastic ch anges 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 dimens ions 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
more difficult.
86
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.
Noisy processing
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-
sponses to relevant stimuli. These adaptive
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.
Negative learning
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 be haviors in ways that can reinforce negative
aspects of the sensory input and motor output.
For example, as it be comes 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 ha ve 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
87
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
systems
Although sensory and cognitive systems are often
discussed and studied as separate entities, a large
body of anatomical, physiological, and behavioral
evidence suggest s 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, 20 00). Although this chapter
focuses on sensory and cognitive deficits, the prin-
ciples and science underlying these issue s 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
88
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 tempora l
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 adu lts 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 young er 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
aging deficit.
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 (Sc hneider 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
tasks under signal-to-noise conditions that
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 impr ove 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.
89
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 visua l 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 sensi tivity. 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 rep resen-
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 defic it 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 meani ngful 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
90
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-
modulatory control; and it disrupts the
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 that an enriched environment
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: de ndrites, 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 prob ably be restored
(Stevens et al., 2002; Saleh et al., 2003;
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;