PERSPECTIVESOPINION

The musician’s brain as a modelof neuroplasticity
Thomas F. Münte, Eckart Altenmüller and Lutz Jäncke
Studies of experience-driven neuroplasticity
at the behavioural, ensemble, cellular andmolecular levels have shown that thestructure and significance of the eliciting
stimulus can determine the neural changes
that result. Studying such effects in humans
is difficult, but professional musicians
represent an ideal model in which toinvestigate plastic changes in the humanbrain. There are two advantages tostudying plasticity in musicians: thecomplexity of the eliciting stimulus — music— and the extent of their exposure to thisstimulus. Here, we focus on the functional
and anatomical differences that have been
detected in musicians by modern
neuroimaging methods.

The size and temporal organization of corticalrepresentations of stimuli are continuallyshaped by experience1,2. Animal studies overthe past 20 years have gone a long way towards
explaining some of the rules of cortical plasticity. For example, it has been shown that training to make fine-grained temporal judgements yields an expansion of the bandwidthor receptive field in both the auditory andsomatosensory modalities, whereas tasks that
require fine-grained frequency or spatial tactile discrimination lead to a decrease in thereceptive-field size of cortical neurons1,3. Thiseffect has been explained by Hebbian learning
rules, whereby synapses are driven to change
by temporally coherent inputs in a competitive neural network. Attention to the sensory
input is very important in driving experience-

related plasticity, as is its behavioural significance3,4. Animal research has also revealed
neuroplasticity at the molecular, synaptic andmacroscopic structural levels5,6. Although animal models are useful for studying the cellularand molecular mechanisms of plasticity, thetypical laboratory animal is deprived of normal stimulation and might, therefore, be a
special case. Moreover, animal models are limited in the range of stimuli that are used, in the
behavioural manipulations that are associatedwith these stimuli and in the duration oftraining. In addition, it is far from clear how
the mechanisms that govern synaptic plasticity at the cellular level are related to theflexibility of operations seen for large-scale
neuronal networks on the one hand, andcognitive processes on the other.
It is therefore important to extend theseinvestigations to the human brain. Significant
headway has been made by studying intermodal plasticity in congenitally blind7 or deaf
subjects8,9, or by monitoring the effects oflimb amputations10. In this article, however,
we are concerned with findings in professional musicians that have been describedover the past decade or so. Performing musicat a professional level is arguably among themost complex of human accomplishments. A
pianist, for example, has to bimanually coordinate the production of up to 1,800 notes
per minute (FIG. 1). Music, as a sensory stimulus, is highly complex and structured in several dimensions11, so it extends beyond any of
the stimuli that have been used in animal
research. Moreover, making music requires

3 seconds

Figure 1 | Example of a hypercomplex musical score. Two three-second segments of the 11th
variation from the 6th Paganini-Etude by Franz Liszt. The depicted segments require the production of
1,800 notes per minute. Reproduced by kind permission of Peters Edition Ltd.

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the integration of sensory and motor information, and precise monitoring of performance. Finally, the study of musicians mightallow us to tease apart the effects of musicaltraining or experience from those of genetic
predisposition.
So, the musician’s brain might constitute a
perfect model in which to study neuroplasticity
in the auditory and motor domains. It can also
be used to examine the effects of dysfunctional
plasticity, as illustrated by musician’s cramp, aparticular kind of occupational dystonia12,13.
Functional measures of plasticity

In a seminal study in 1995, Elbert and colleagues14 investigated somatosensory evokedmagnetic fields in string players. Source
analysis revealed that the cortical representation of the digits of the left hand (the fingering hand) was larger in these musicians thanin controls. In the case of the right hand, inwhich no independent movements of the fingers are required in string players, there were
no differences between musicians and controls. The cortical reorganization of the representation of the fingers was more pronounced
in musicians who had begun their musicaltraining at an early age. A first indication that
extensive musical training can plastically alter
receptive functions was provided by Pantev
and colleagues15. Equivalent current dipoles,
computed from evoked magnetic fields, wereobtained in response to piano tones and topure tones of equal fundamental frequency
and loudness. In musicians, the responses topiano tones (but not to pure tones) were~25% larger than in non-musicians. In a
study of violinists and trumpeters, this effectwas most pronounced for tones from eachmusician’s own type of instrument16.
Although musical sounds were used inthese early studies, they lacked a main characteristic of music: tones become music only
when they are structured. The temporal
structure of music comprises local rhythm,
defined by the duration and temporal distance of tones, and global metrum, which
determines whether a piece is, for example, amarch or a waltz. As far as the pitch dimension is concerned, the interval between two
successive notes, the global contour of a musical piece and the harmonic structure can be
distinguished. In processing (and enjoying)music, the extraction of structural regularities
is of paramount importance. At what cognitive and neural levels are such regularities ofmusic processed? The mismatch negativity(MMN), a frontal negative wave in the eventrelated potential (ERP), is a marker of the
pre-attentive detection of changes in regularsequences of auditory stimuli17. Consider, for

Figure 2 | Structural changes in the brains ofmusicians. Some of the brain areas that havebeen found to be enlarged in musicians inmorphometric studies based on structural
magnetic resonance imaging. Red, primary motor
cortex; yellow, planum temporale; orange, anterior
part of the corpus callosum.

example, a series of 1,200-Hz tones that isinterrupted occasionally by a deviant 1,500-Hz
tone. In this situation, the deviant tones giverise to an MMN. Importantly, the MMN
occurs in the absence of attention to the stimuli, and newer research has indicated that, in
addition to simple physical deviants (such as
duration, pitch or intensity), the MMN is alsoelicited by more complex irregularities, suchas changes in sequences of several tones17.
Professional musicians, unlike non-musicians, show an MMN for tones that are
mistimed by as little as 20 ms in a series ofregularly spaced tones18. For stimuli that are
mistimed by 50 ms, which do produce an
MMN in non-musicians, the MMN in musicians was considerably larger than that of controls18. Musicians also showed an MMN forslightly impure chords that were presented
among perfect major chords, whereas, again,
non-musicians did not19. Recently, an MMN
was found for small changes in the contour oftransposed melodies in musicians who perform primarily without a score20. Source localization studies have shown that the MMN
arises mainly from neurons on the supratemporal plain of the temporal lobe, withfurther contributions from the frontal cortex17,21,22. These findings indicate that, after
years of musical training, neuronal populations in the auditory cortex might be shaped
such that they automatically detect subtle
changes in auditory stimulus sequences withsimple or higher-order regularities. The parameters that are needed for the acquisition ofthese skills are unknown, but probably involve
initial attentive processing of the stimuli20.
In an experiment that required theattentive analysis of chord sequences, the N1component of the ERP, which arises from theprimary auditory cortex, was enhanced in

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response to consonant chords, relative to dissonant (augmented fifth) chords, in musicians only23. This indicates that the auditory
cortex, possibly including the primary areas,in musicians might be tuned to complex
harmonic features of sounds24. Superior
attention-dependent processing in musicians
was also shown in a study that required thesubjects to select stimuli from one of two
information channels that were defined bytheir pitch. In such situations, stimuli that are
attended elicit a negativity in the event-related
potential, known as the Nd, relative to stimuli
that are not the focus of attention25. In professional musicians, the Nd was more pronounced, indicating superior attentional
selectivity in the pitch dimension26.
A recurring theme in the animal literatureis the plasticity of sound localization. Interaural differences in the level and phase ofsounds are important for sound localization,
but the filter functions of the outer ear,
known as head-related transfer functions27,
also provide crucial information. A conductor, more than any other musician, is likely todepend on spatial localization for successful
performance — for example, he might needto home in on a certain player. Consistent
with this, professional conductors were foundto be better than pianists and non-musiciansat separating adjacent sound sources, oneof which was task relevant, in the periphery of
the auditory field. This behavioural selectivity
was paralleled by modulation of the Nd component, which was selective for the attended
source in conductors, but not in pianists or
non-musicians28.
Anatomical differences

Since the age of phrenology, neuroscientists
have tried to relate extraordinary skills tochanges in brain anatomy. For example, atthe beginning of the twentieth century,
Auerbach reported that the middle and posterior thirds of the superior temporal gyrus
were larger than normal in several postmortem studies of the brains of famousmusicians29. Modern brain-imaging techniques, such as high-resolution magnetic resonance imaging (MRI), allow us to study
anatomical details in the brains of living
humans. Studies in which these techniques
have been used have shown that several brain
areas, including the planum temporale, theanterior corpus callosum, the primary hand
motor area and the cerebellum, differ in theirstructure and size between musicians andcontrol subjects (FIG. 2). These findings havereopened the debate about whether thesestructural differences are related directly tomusical ability.

www.nature.com/reviews/neuro© 2002 Nature Publishing GroupPERSPECTIVES
Asymmetry of the planum temporale has
been suggested as a marker of cerebral dominance, because its direction and size correlatewith handedness30. In two independent samples, musicians with absolute pitch (AP) had amore pronounced leftward planum temporale asymmetry than did musicians with relative pitch (RP) or non-musician controls31,32;
another study found no significant differencein planum temporale volume between musicians with AP and those with RP33. However,when compared with a large sample of righthanded non-musician controls, musicianswith AP again showed a larger left planum
temporale (BOX 1).
Amunts and colleagues34 have estimatedthe size of the primary hand motor area bydetermining the intrasulcal length of the posterior bank of the precentral gyrus in linearlynormalized MRI images. Musicians had agreater intrasulcal length on both sides, butmore so on the right, non-dominant hemisphere, resulting in reduced asymmetry scoresfor this area in musicians. There was a strong
negative correlation between the time atwhich musical training had begun and thesize of the right- and left-hand motor areas.
This was reflected behaviourally by reduced
asymmetry in measurements of hand skill
and a negative correlation between hand-skill
asymmetry and the age at which musicaltraining started35.
Many musical instruments require precise
coordination of bimanual movements.
Interestingly, musicians who began theirmusical training before the age of seven havea larger anterior midsagittal corpus callosum
than controls or musicians who started training later36. Because the size of the midsagittal
corpus callosum is a good indicator of thenumber of axons that cross the midline37, thisfinding indicates that this subgroup of musicians has an enhanced interaction between
the two hemispheres. This hypothesis has
been corroborated by a bilateral transcranial
magnetic stimulation (TMS) study in pianists
and guitarists38, which revealed decreased
interhemispheric inhibition. This, in turn,might facilitate bimanual coordination inmusicians by increasing signal transfer
between the hemispheres.
The precise timing of movements also
requires the participation of the cerebellum.
Recently, male musicians have been shownto have a greater mean relative cerebellar
volume than male non-musicians. The difference could not be ascribed to a difference
in total brain volume, as this was similar inboth groups39. Together, the findings indicatethat musicians have anatomical differences
in several brain areas that are involved in

motor and auditory processing. Changes insuch large-scale neural networks can bedetected by voxel-based morphometry40, a
statistical method of revealing differences inbrain anatomy, viewed by MRI, between
groups, without the need to focus on target
structures. Such an analysis revealed
increased grey matter volume in musiciansin a motor network that included the left
and right primary sensorimotor regions, theleft basal ganglia, and the bilateral cerebellum, as well as the left posterior perisylvian
region41.
Sensorimotor learning

It has been proposed, on the basis of neuroimaging studies, that motor learning occurs
in several phases: a fast initial phase of performance gains is followed by a period of
consolidation that lasts for several hours.
This is succeeded by a slow learning phasethat occurs during continued practice andleads to gradual increases in performance42.
With regard to slow learning, practising acomplex tapping task over several months led
to an increase in functional MRI (fMRI) activation in the primary motor area by ~25% in
non-musicians43. When highly skilled
pianists and non-musicians were exposed toa novel tapping task during a single scanningsession, musicians showed a rapid increasein M1 (primary motor cortex) activation,
whereas non-musicians did not44. This effectof recruitment in the primary motor area ofmusicians resembled the slow learning
described previously in non-musicians42,
even though it occurred within minutes
rather than months. It was therefore interpreted as an effect of pre-practice experience44. With regard to secondary motor areas,musicians showed a much smaller area ofactivation in the supplementary motor area
(SMA), pre-SMA and cingulate motor area
than non-musicians in several studies44–46. So,
pianists recruit smaller neural networks than
do non-musicians, indicating that they are
more efficient in the control of movements.
In musically naive subjects, training on the
piano for two hours per day over five days led
to increased excitability of the cortical motor
areas that control the contralateral finger
flexor and extensor muscles of the hand47, as
measured by mapping of the responses to
TMS. A similar but less pronounced effectwas observed in subjects who mentallyrehearsed the motor tasks over five days.
Mental practice therefore seems to be sufficient to promote the modulation of neuralcircuits that are involved in the early stages ofmotor-skill learning47.
Exceptional musical performance requires
the high-speed control of complex movement
patterns under continuous auditory feedback.
As a prerequisite, audio-motor integration at
cortical and probably subcortical levels has tobe established. This audio-motor coupling —Box 1 | Absolute pitch
Perfect or absolute pitch (AP) is defined as the ability to identify accurately the pitch of a single
tone heard in isolation, usually in terms of musical-scale categories or keys on a piano. Estimatesof the prevalence of AP vary widely from 1 in 1,500 in amateur musicians62 to up to 15% in
students at music schools63. Although AP was initially considered to be an innate ability64, there
is now a consensus that it relies on both a genetic predisposition65,66, possibly even a major geneeffect, and early musical training32,63. Anatomically, an in vivo magnetic resonance imaging(MRI) morphometric study of musicians has shown increased left-sided asymmetry of thesuperior bank of the temporal lobe, the planum temporale (PT)31, in individuals with AP.
Interestingly, this asymmetry seemed to be the result of a smaller-than-normal right PT inmusicians with AP, rather than an expansion of the left side32. Attempts to correlate anatomicalmeasures with performance in pitch-identification tasks have yielded conflicting results, withthe size of either the right32 or the left33 PT being the better predictor of performance inmusicians with AP. However, a recent functional MRI study found that the intensity of
haemodynamic responses to music pieces in the left, rather than right, PT correlates with bothAP ability and the age at which musical training started67. Early training alone cannot accountfor the PT asymmetry, as musicians with relative pitch (RP) who started training early do nothave such an asymmetry32. In functional terms, it has been suggested on the grounds of asmaller68 or even absent69 P300 event-related potential — generally interpreted as an index ofworking memory updating — that musicians with AP do not rely on auditory working memory
for pitch identification. This view is supported by a positron emission tomography study thatrevealed stronger activation of a right inferior frontal area, which is proposed to be involved intonal working memory, in musicians with RP carrying out a task that required an interval
judgement for two successive tones. On the other hand, musicians with AP, but not RP, showed
activation in the posterior left dorsolateral frontal region, which is thought to support
verbal–tonal associations, in a passive listening task33.

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VOLUME 3 | JUNE 2002 | 4 7 5© 2002 Nature Publishing GroupPERSPECTIVES
which is comparable to the oral–aural loop in
language processing — is established during
musical training48. At the behavioural level, it
is reflected by reports from professional
pianists that their fingers move more-or-lessautomatically when they are listening to pianomusic. Exposing musically naive subjects tocontrolled piano training led to audio-motor
coupling after as little as 20 minutes of training, as shown by topographic analysis of very
slow event-related potentials48. After the firsttraining session, there was further activity overmotor areas while subjects listened to simple
piano tunes. Likewise, finger movements on amute keyboard were associated with anincrease in activity over auditory areas. The
effect could be enhanced and stabilized during
five weeks of training (FIG. 3). Similar co-activation has also been found in professional
pianists, who showed magnetoencephalographic (MEG) activity in sensorimotor

Listening

Moving20 years

0.98

20 daysneg

0.95

20 minutes
pos
0.83

Start
0.78

Figure 3 | Sensorimotor integration inmusicians. Shown are spline-interpolated
isovoltage maps of averaged electroencephalographic recordings. Subjects either
listened to a short piece of piano music (leftcolumn) or performed on a mute piano keyboard(right column). Before training, musically naive
subjects (Start) produced grossly different
topographic patterns of slow event-related
potentials in the two conditions, as indicated by
the maps and a measure of map similarity (scalar
product). With training for 20 minutes or 20 days,
the topographic distributions became increasingly
similar. In professional pianists with approximately
20 years of training, maps in both conditions are
virtually identical. Modified, with permission, from
REF. 48 © 2001 New York Academy of Sciences.

cortical regions while listening to pianomusic49. These neural networks thus seem tobehave in a similar way to the ‘mirror neurons’in the monkey frontal cortex (area F5), which
are active during both the execution of complex movements and visual observation of thesame movements50.
Maladaptive plasticity

There is a dark side to the increasing specialization and prolonged training of modernmusicians — namely, loss of control anddegradation of skilled hand movements, adisorder referred to as ‘musicians’ cramp’ or
focal dystonia12. The first historical record ofthis condition, from 1830, appears in the
diaries of the famous pianist and composer
Robert Schumann51. As was probably the casefor Schumann, prolonged practice and pain
syndromes due to overuse can precipitate dystonia, which is developed by ~1% of professional musicians and usually ends theircareers12. Neuroimaging studies point to dysfunctional (or maladaptive) neuroplasticity as
its cause13,52. For example, an MEG study ofmusicians with focal dystonia showed fusionof the digital representations in the somatosensory cortex, reflected in a decreased distance between the representations of theindex and little fingers relative to healthy control musicians13 (FIG. 4). These findings arecorroborated by psychophysical measurements and fMRI investigations in a relateddisorder, writer’s cramp, which showeddecreased temporal and spatial discrimination at the finger tips53,54. Observations inmonkeys indicate that rapid, stereotypicalmovements in a learning context can actively
degrade the cortical representations of sensory information that guide fine hand movements55. This dedifferentiation of sensory
feedback information has been proposed toform the basis of focal dystonia47,55. Indeed,
the repeated temporal association of movement patterns is a characteristic of music —for example, when playing arpeggios or musical scales. In a further study, symptoms were
provoked in five dystonic guitarists when they
played a modified guitar inside an fMRI scanner56. Relative to non-dystonic guitarists, they
showed more activation of the contralateral
sensorimotor cortex but less activation of premotor areas, indicating abnormal recruitment of cortical areas that are involved in thecontrol of complex movements.
Musicians as a model?

Neuroplasticity allows the brain to adapt toenvironmental factors that cannot be anticipated by genetic programming. The neuraland behavioural changes that are attributed

4 7 6 | JUNE 2002 | VOLUME 3

to plasticity have been observed on different
timescales, ranging from several minutes tothe whole lifetime of the individual.
Different processes are likely to support
plastic changes at the extremes of this timeline. Accordingly, experience-driven neuroplasticity has been explained by both thede novo growth and improvement of new
dendrites, synapses and neurons5,57, and thedisinhibition or inhibition of pre-existinglateral connections between neurons by
sensory input58. The former mechanism
entails structural changes at the microscopic
and macroscopic levels, whereas the lattercan be achieved by strengthening or inhibiting pre-existing synaptic connections in thespirit of Hebbian learning. Sometimes, evenmore rapid changes of brain responses,
occurring in the order of milliseconds, havebeen discussed under the heading of neuroplasticity. However, these are likely to result
from the attentional modulation of neuralcircuits, and should be distinguished fromtrue plastic changes59.
Research into plasticity in musicians is still in
its infancy, but, already, many of the findings
from animal studies have found parallels instudies of musicians. At one extreme, years ofmusical experience, especially in those musicians who begin training early on, might lead toan increase in grey and white matter volume inseveral brain regions31,32,36,39,41. These anatomicalalterations seem to be confined to a critical
period. The fact that, in several studies, a correlation was found between the extent of the
anatomical differences and the age at whichmusical training started strongly argues against
the possibility that these differences are preexisting and the cause, rather than the result, ofpractising music. The view that these differences
represent genuine plastic changes of the brain
receives further support from neuroimaging
studies in other populations. For example, acorrelation between the size of the posterior
hippocampus and years of driving experiencehas been reported in London taxi drivers60.
Further research using advanced imaging techniques, such as magnetic resonance spectroscopy and diffusion tensor imaging, and theextension of studies beyond the conventionalcross-sectional design, are needed to investigate
the underlying neurophysiological changes.
At the other extreme, several minutes oftraining can induce changes in the recruitment of areas of the motor cortex44 or establish auditory–sensorimotor coupling 48.
Some of the other findings discussed here
probably require training in the order ofmonths to several years and, at present, it isunclear what neural processes support thisbehavioural plasticity.

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1.

2.

3.

D5

Affected digits

D2–D4

D3–D5

4.

D2
D1

D1
5.

Non-dystonic hand
6.

7.
8.

9.
10.

11.

12.
13.

14.

Right

Left

Figure 4 | Fusion of the somatosensory representation of single digits of the hand in a musician
suffering from focal dystonia. The best-fitting dipoles to explain the evoked magnetic fields after
sensory stimulation of single digits (D1–D5) are shown projected on the individual’s magnetic resonanceimaging scan. Whereas for the non-affected hand, the typical homuncular organization (inset) reveals a
distance of ~2.5 cm between the sources for the thumb and the little finger (yellow circle and square on
the right of the brain), the somatosensory representations of the fingers on the dystonic side are blurred,
resulting from a fusion of the neural networks that process incoming sensory stimuli from different fingers(red circles). Modified, with permission, from REF. 13 © 1998 Lippincott, Williams and Wilkins.

15.
16.

17.

18.

The investigations presented in thisoverview convincingly show the value of themusician’s brain as a model of neuroplasticity and have set the stage for further
research. Some of the questions that need tobe tackled include the following. What are
the training parameters that lead to successful learning and plasticity? Can these parameters be exploited in musical education or
to enhance learning in other domains? Whatis the role of genes in determining auditory
neuroplasticity in musicians? What is therange of structural regularities that can beextracted from the auditory input in an
automatic, pre-attentive fashion? As making
music undoubtedly requires intense selfmonitoring, and error detection and correction, are there any plastic changes in theexecutive brain systems that are responsible
for performance monitoring?
Finally, one has to bear in mind that musiccan elicit powerful emotional reactions.
Strong emotional responses to music, leading

to shivers down the spine and changes in heartrate, are accompanied by the activation of abrain network that includes the ventral striatum, midbrain, amygdala, orbitofrontal cortex
and ventral medial prefrontal cortex — areasthat are thought to be involved in reward,
emotion and motivation61. Further research
will show whether activity in these areas is also
directly involved in mediating neuroplasticity.
Thomas F. Münte is at the Department of
Neuropsychology, Otto-von-Guericke University,
Universitätsplatz 2, Gebäude 24,39106 Magdeburg, Germany.
Eckart Altenmüller is at the Institute of Music
Physiology and Musicians’ Medicine, Hannover
School of Music and Drama, Emmich Platz 1,
30175 Hannover, Germany.
Lutz Jäncke is at the Department of
Neuropsychology, University of Zürich, Zürichberg
Strasse 43, CH-8044 Zürich, Switzerland.
Correspondence to T.F.M.
e-mail: thomas.muente@med.uni-magdeburg.de
doi:10.1038/nrn843

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19.

20.

21.

22.

23.

24.

25.

26.

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Online links
FURTHER INFORMATION
Encyclopedia of Life Sciences: http://www.els.net/
auditory processing | brain imaging: observing ongoing neuralactivity | cortical plasticity: use-dependent remodelling |magnetic resonance imaging | topographic maps in the brain
MIT Encyclopedia of Cognitive Sciences:
http://cognet.mit.edu/MITECS/
auditory plasticity | electrophysiology, electric and magnetic
evoked fields | magnetic resonance imaging | neural plasticity |positron emission tomography
Access to this interactive links box is free online.

OPINION

Complex-trait genetics: emergence ofmultivariate strategies
Tamara J. Phillips and John K. Belknap
Complex traits, including many diseaserelated traits, are influenced by multiple
genes. Bivariate approaches that associate
one gene with one trait are yielding tomultivariate methods to synthesize theeffects of multiple genes, integrate results
across independent studies, and aid in theidentification of coordinated pathways andinteractions between loci.

The extraordinary success of the molecularrevolution in transforming modern biology
has generated one important problem — how
do we synthesize the wealth of molecular data
to gain insight into ‘higher-order’ processes
that exist at the levels of pathways, organ systems and whole organisms? This question has
led to the rise of newer, synthetic researchstrategies to complement those based on dissection and analysis. This trend is evident in
the genetic analysis of complex central nervous
system traits — the subject of this article.
Complex (or quantitative) traits are those
influenced by multiple loci (genes), each ofwhich is known as a QUANTITATIVE TRAIT LOCUS(QTL). Approaches to the study of complex
traits are of two general kinds, each with

4 7 8 | JUNE 2002 | VOLUME 3

strengths and weaknesses1. Gene-driven methods focus on a particular gene and seek todetermine the phenotypes that are influenced
by that gene. The study of knockout animals isa prime example. By contrast, trait-driven
methods focus on a specific trait (phenotype)and seek to discover the underlying genes.
QTL mapping, genome-wide mutagenesis
screens and MICROARRAY expression analysis areexamples of this approach. Because of theirability to focus simultaneously on many genes,
trait-driven methods lend themselves more
readily to multivariate genetic approaches
than do gene-driven methods. However, someexamples of the latter are also amenable tomultivariate approaches (such as the use of
double knockouts), as we discuss below.
Arguably, the first important advances inthe study of complex-trait genetics were madeby gene-driven knockout and transgenic
strategies, which have been used to identify
hundreds of genes that probably influencespecific complex traits. Much of the progressthat has been made so far has been based on
bivariate approaches — one locus is shown to
influence one trait, or a series of bivariate
experiments are used to link one locus towww.nature.com/reviews/neuro© 2002 Nature Publishing Group