Received: 16 November 2009, Revised: 11 February 2010, Accepted: 23 March 2010, Published online in Wiley InterScience: 2010

Love songs, bird brains and diffusion tensorimagingGeert De Groofa and Annemie Van der Lindena*

The song control system of songbirds displays a remarkable seasonal neuroplasticity in species in which song outputalso changes seasonally. Thus far, this song control system has been extensively analyzed by histological andelectrophysiological methods. However, these approaches do not provide a global view of the brain and/or do notallow repeated measurements, which are necessary to establish causal correlations between alterations in neuralsubstrate and behavior. Research has primarily been focused on the song nuclei themselves, largely neglecting theirinterconnections and other brain regions involved in seasonally changing behavior. In this review, we introduce andexplore the song control system of songbirds as a natural model for brain plasticity. At the same time, we point out theadded value of the songbird brain model for in vivo diffusion tensor techniques and its derivatives. A compilation ofthe diffusion tensor imaging (DTI) data obtained thus far in this system demonstrates the usefulness of this in vivomethod for studying brain plasticity. In particular, it is shown to be a perfect tool for long-term studies ofmorphological and cellular changes of specific brain circuits in different endocrine/photoperiod conditions. Themethod has been successfully applied to obtain quantitative measurements of seasonal changes of fiber tracts andnuclei from the song control system. In addition, outside the song control system, changes have been discerned in theoptic chiasm and in an interhemispheric connection. DTI allows the detection of seasonal changes in a regionanalogous to the mammalian secondary auditory cortex and in regions of the ‘social behavior network’, aninterconnected group of structures that controls multiple social behaviors, including aggression and courtship.DTI allows the demonstration, for the first time, that the songbird brain in its entirety exhibits an extreme seasonalplasticity which is not merely limited to the song control system as was generally believed. Copyright � 2010 JohnWiley & Sons, Ltd.

Keywords: diffusion tensor imaging (DTI); songbird brain; seasonal plasticity; starling (Sturnus vulgaris)

INTRODUCTION

One of the most important developments that has taken place inneuroscience in the past 25 years is the realization that the brainis not the fixed structure it was thought to be, but rather displaysextensive dynamic changes. These changes constitute what iscommonly called ‘neuroplasticity’. Understanding the specificnature and control mechanisms represents a critical step towardsa full understanding of brain functioning. Some of the mostdramatic brain structural modifications are the seasonal changesaffecting a connected set of brain nuclei controlling singingbehavior, the song control system (SCS), in oscine songbirds.In most temperate zone species, reproduction is a seasonalphenomenon. In a specific group of birds belonging to the orderPasseriformes, behavior associated with reproduction, such assinging, is performed at higher rates during the breeding season(1). In parallel, a seasonal variation in the volume of song controlnuclei has been observed (2–10). As a result of the magnitude ofthese changes, sometimes as large as a 99% increase (2), seasonalvariation in the brain of songbirds has emerged as one of the bestmodel systems for the study of naturally occurring brain plasticity(11–13). Plasticity in the song system has been intensivelystudied, but histology, by definition performed at post-mortem,only enables a single determination of morphological features,and electrophysiology usually captures the activity of just afew neurons. It is therefore difficult to correlate the overallstatus of the song system with dynamic behavioral data. MRI, a

nondestructive microscopic tool, now enables the investigationof these issues in vivo.Diffusion tensor imaging (DTI) has gained popularity in

neuroimaging because it provides a methodology for thenoninvasive assessment of unique structures in the brain, whichwas previously not possible. DTI is an MRI method that uses theanisotropic diffusion of water as a highly sensitive marker ofthe microarchitecture of tissues. It involves the use of diffusion

(www.interscience.wiley.com) DOI:10.1002/nbm.1551

Special Issue Review Article

* Correspondence to: A. Van der Linden, Bio-Imaging Laboratory, Department ofBiomedical Sciences, University of Antwerp, Campus Groenenborger, Groe-nenborgerlaan 171, B-2020 Antwerp, Belgium.E-mail: Annemie.VanderLinden@ua.ac.be

a G. De Groof, A. Van der Linden

Bio-Imaging Laboratory, Department of Biomedical Sciences, University of

Antwerp, Antwerp, Belgium

Abbreviations used: lI, eigenvalue of the tensor; AD, axial diffusivity; AFP,

anterior forebrain pathway; CoP, commissura posterior; DM, dorsomedial

nucleus of the intercollicular complex of the mesencephalon; DTI, diffusion

tensor imaging; EPI, echo planar imaging; FA, fractional anisotropy; HARDI,

high angular resolution diffusion imaging; HVC, acronym now used as a

proper name; formerly high vocal center; LaM, lamina mesopallialis; LMAN,

lateral magnocellular nucleus of the anterior nidopallium; NCM, caudomedial

nidopallium; OM, tractus occipitomesencephalicus; PoA, preoptic area; RA,

nucleus robustus arcopallialis; RD, radial diffusivity; SBN, social behavior

network; SCS, song control system; SNR, signal-to-noise ratio; VMN, nucleus

ventromedialis hypothalami; XIIts, vocal motor nucleus.

NMR Biomed. (2010) Copyright � 2010 John Wiley & Sons, Ltd.

1

gradients sensitized to diffusion in multiple directions todetermine the preferential directionality of diffusing spins (14).With the understanding that spins diffuse more rapidly alongwhite matter tracts as opposed to perpendicularly to them, thismethod can create voxel-wise maps of fractional anisotropy (FA).The noninvasive in vivo nature of the technique allows forlongitudinal studies in individuals following changes with time.Although DTI is increasingly being used in both animal and

human applications, when applied to bird brains this techniquefaces one technical challenge. Because the bird’s skull harborsmany small air cavities, the use of ultrafast imaging sequences(e.g. echo planar imaging or EPI) results in severe susceptibilitydistortions. DTI in songbirds has, until now, only been used withspin echo diffusion-weighted sequences. The single-shot EPImethod applied inmost human clinical studies has not been usedin songbirds because of the associated increase in susceptibilitygradients in high-field conditions. Furthermore, an EPI protocolcannot guarantee the higher spatial resolution achieved by a spinecho sequence. In order to study seasonal changes in thesame subjects, it is imperative that the signal-to-noise ratio (SNR)remains the same when repeating the measurements during theyear. This is of particular importance when studying changes inDTI parameters known to be sensitive to changes in SNR (15,16).In the high-resolution studies of songbirds, low SNR (especially indiffusion-weighted images) may be a problem, if many averages(n¼ 14) are not used (17–19). Apart from this, the set-up forsongbird imaging does not differ that much from a ‘normal’ ratset-up (in the case of starlings) or a ‘normal’ mouse set-up (in thecase of, for example, canaries); only the stereotaxic constrainer ismodified to incorporate a beak holder.This article provides an overview of what has been established

so far in the songbird brain with the use of DTI. It shows that DTIof the songbird brain has opened up new avenues to pursueseasonal plasticity of the songbird brain. In addition, it also aimsto introduce the reader to the songbird brain as both aninteresting natural model to study neuroplasticity and afascinating model for validating diffusion-weighted techniques.

SONGBIRD BRAIN: THE IDEAL MODEL TOSTUDY NEURONAL PLASTICITY

The songbird brain and the SCS

Vocal learning is rare among vertebrates. Apart from humans, inmammalian species only the cetaceans (whales and dolphins),two bats and possibly elephants have been shown to imitatevocal communication signals (about 300 species). In contrast,three large groups of birds are known to learn their vocalizations:parrots, hummingbirds and oscine songbirds (in total about5000 species). Songbirds are probably the only model systemexhibiting learned vocalizations that can be easily studied in thelaboratory, because song can be readily recorded, quantified andanalyzed. Song and human speech display many similarities,such as the existence of crucial periods for learning, dependenceon auditory experience and feedback, and lateralization ofsound production (20). Despite these interesting parallels, vocalcommunication probably evolved independently in songbirdsand humans (21).During phylogenetic evolution, several forebrain regions of

songbirds became part of a spatially organized circuitry thatmediates this ability to learn and produce songs: the so-called

‘song control system’ (22) (Fig. 1). HVC (formerly known as thehigh vocal center; acronym now used as a proper name (21)) is akey part of the SCS involved in learning, production and theperception of song (23). Two pathways originate from HVC: acaudal motor control pathway, which plays a critical role in theproduction of song, and an ‘anterior forebrain pathway’ (AFP),which plays a role in song acquisition in juveniles and songstability in adults (24). HVC neurons either project to the nucleusrobustus arcopallialis (RA) or to Area X, located in the avianhomolog of the mammalian basal ganglia. RA-projecting neuronsare part of the motor pathway that controls the activity of thesyrinx, the sound-producing organ in birds, and of a medullarypathway that synchronizes respiration with song production (25)(Fig. 1). The X-projecting neurons belong to the AFP required forsong learning and maintaining song stability in adulthood (26).Although HVC, RA and Area X are considered as themain nuclei ofthe SCS, several other nuclei make up the entire SCS, as illustratedin Fig. 1 [for more information, see ref. (27)].

Naturally occurring neuroplasticity in songbirds

In most temperate zone species, reproduction is a seasonalphenomenon and behaviors associated with reproduction, suchas singing, are performed at higher rates during the breedingseason (1). In parallel, a seasonal variation in the volume ofneuronal cell groups (nuclei) and their connectivity has beenobserved (13). The discovery of these seasonal changes,sometimes as large as a 99% increase (2), led to one of themost important paradigm shifts in modern neuroscience: therealization of the extent to which dynamic changes in brainfunction and structure are the norm rather than the exception.

Figure 1. (A–C) In vivo sagittal fractional anisotropy (FA) maps (from

0.4mm midsagittal to 3.2mm midsagittal) of one individual starling in

spring. (D) Schematic overview of the song control system and itsanatomical connections (note the LMAN!RA projection and the

HVC!X projection both run along the LaM in red). Image resolution,

0.1mm� 0.1mm. Scale bar, 10mm. CO, optic chiasm; CoA, commissura

anterior; CoP, commissura posterior; DLM, nucleus dorsolateralis anteriorthalamis, pars medialis; DM, dorsomedial nucleus of the intercollicular

complex; LaM, lamina mesopallialis; LFS, lamina frontalis superior; LMAN,

lateral magnocellular nucleus of the anterior nidopallium; LPS, lamina

pallio-subpallialis; nXIIts, nucleus nervi hypoglossi pars tracheosyringealis;OM, tractus occipitomesencephalicus (medial telencephalic part); RA,

nucleus robustus arcopallialis; X, Area X. [Adapted and reprinted from

ref. (18).]

www.interscience.wiley.com/journal/nbm Copyright � 2010 John Wiley & Sons, Ltd. NMR Biomed. (2010)

G. DE GROOF AND A. VAN DER LINDEN

2

Nuclei such as HVC, RA and Area X are twice as large in the springas in the fall. The cellular bases of these volumetric differencesrelate to variation in cell size, branching and spacing, but also, inthe case of HVC, to an active process of neurogenesis and newneuron incorporation (13). Young neurons born in the ventricularzone migrate and are incorporated into functional circuitsthroughout most of the dorsal telencephalon, especially in theHVC of songbirds. However, only HVC neurons projecting to theRA are replaced at a high rate and there is no replacement of AreaX-projecting cells in the HVC. This is a unique phenomenon of thesongbird brain model, namely the neurogenesis of projectionneurons. This cellular plasticity is controlled by testosteroneacting directly and indirectly on the SCS, but also by changes inthe social environment or by activity-dependent changesassociated with vocal production (i.e. increasedmetabolic activityin HVC and RA, and activity-dependent production of brain-derived neurotrophic factor) (12).European starlings (Sturnus vulgaris) are a typical example of

songbirds in which song production and the factors thatmotivate it differ seasonally. Male starlings sing throughoutthe year but, during the breeding season (spring), when theconcentration of plasma testosterone is elevated, singingbehavior can be highly sexually motivated (28). In the non-breeding season, when the plasma concentration of testosteroneis basal (29,30), song rather plays a role in social interactions,such as the maintenance of cohesion and of dominancehierarchies within the flock (31–33). The starling is a non-domesticated bird, unlike the canary for which it is assumed thatplasticity could be affected by long-term breeding in captivity. Assuch, male European starlings are an ideal model system toexplore naturally occurring seasonal neural plasticity of singingbehavior.

SONGBIRD BRAIN: THE IDEAL MODEL FORIN VIVO DTI STUDIES

The interpretation of changes in the measured diffusion tensor iscomplex and should be performed with care. Many publishedstudies have focused primarily on diffusion anisotropy (usuallythe FA measure), which may not be sufficient to characterizetissue changes. A change in FA is not always straightforward ininterpretation because it depends on all three eigenvalues.Therefore, several studies have suggested that specific combi-nations of the eigenvalues [e.g. radial diffusivity or RD¼ (l2þ l3)/2] could be used to describe diffusion changes in pathology.Within white matter, RD appears to be modulated mostly bymyelin content (34), whereas the axial diffusivity (AD¼ l1) ismore specific to the number and coherence of axons in the voxel(35). For example, white matter pathology often causes decreasesin anisotropy, which may result from increased RD (diffusionperpendicular to the axons), reduced AD (diffusion parallel to theaxons), or both.Interpretation is further complicated by the sensitivity of the

diffusion tensor, and anisotropy in particular, to a broad spectrumof other factors, including image noise (36,37), partial volumeaveraging between tissues in large voxels (e.g. signal mixing ofgray matter, white matter and cerebrospinal fluid) (38) andregions of crossing white matter tracts (38,39). The latterconfounder is unfortunately unavoidable, because certain areasof the brain have areas of fiber crossing, which have acorresponding low FA. In coherent, densely packed, white matter

fiber bundles, the direction of fastest diffusion, given by theprincipal axis (or primary eigenvector) of the diffusion tensor,points along the main axis of the fiber bundle and is commonlyused to map the trajectory of white matter fiber tracts in thebrain (40,41). However, although the orientation of the tensor hasbeen validated in large fiber bundles with coherent fiberorientations in brain (42–44), the tensor model cannot be usedto resolve multiple fiber bundles within voxels containing morethan one principal direction (45). Such complex fiber architec-tures frequently occur in both gray and white matter regionscontaining crossing or branching fiber tracks. In both cases,the apparent diffusion distribution will have multiple diffusionpeaks and the diffusion tensor no longer provides an accuratemathematical description of the apparent diffusion patterns. Thislimitation of DTI has prompted numerous efforts to developtechniques capable of resolving complex fiber architectureswithin voxels. Diffusion spectrum imaging (46) is a popularmodel-free method that applies the classical formalism of‘q-space’ theory (47) to recover the three-dimensional diffusionpropagator, or displacement spectrum, in each voxel. This can beused as a surrogate measure of complex fiber orientations withinvoxels (46,48). A relatedmodel-freemethod, called q-ball imaging(49), provides an alternative approach for recovering the diffusionpropagator in each voxel using less time-intensive and reducedencoding (spherical) diffusion acquisition protocols.All DTI studies in songbirds so far have tried to minimize these

confounding effects by not only looking at FA (but at the separateeigenvalues as well) and using spin echo sequences (allowing ahigher resolution, i.e. smaller voxels and less partial volumeeffects) and many averages (higher SNR and less image noise)(17–19). An advantage of using the songbird brain for DTI is thatcrossing fibers are less of a problem in bird brains because whitematter exists mostly in homogeneous areas with single fiberpopulations (comparable with pyramidal tracts and the corpuscallosum of humans) (21). A notable exception is the optic chiasmwhich, as its name implies, is a large bundle of crossing fibers.However, unlike in mammals, in which not all optic fibers crossover at the chiasm, those of birds do decussate almostcompletely (50) (Fig. 2). This makes the optic chiasm of birdsan interesting structure for the validation of in vivo fiber trackingtechniques, such as those based on constrained sphericaldeconvolution for instance (51–53) (Fig. 2).The songbird brain model offers the added advantage that the

plasticity of the brain is reversible: for example, the SCS growsduring the breeding season, shrinks during the nonbreedingseason and enlarges again in the following breeding season. Thesame reversibility is also found in the fiber connections betweenthe different song control nuclei. New axonal projection neuronsare formed in the breeding season and retract during thenonbreeding season. The measurement of a couple of starlings(n¼ 2) during two subsequent years clearly showed thisreversibility also in the DTI parameters (Fig. 3). This plasticity ispartially inducible by the implantation of testosterone, and caneasily be reversed by removing the testosterone implant (55),which makes it a very interesting model for the validation ofin vivo techniques for studying neuroplasticty. The extremereversibility of the neuroplastic changes in the songbirdbrain, and the fact that some circuits have been extensivelyhistologically analyzed, can be seen as a huge advantage over theother commonly used small animal models of plasticity.Reversibility is not only restricted to the brain anatomy itself.

Avian and mammalian hair cells of the inner ear differ, in that

NMR Biomed. (2010) Copyright � 2010 John Wiley & Sons, Ltd. www.interscience.wiley.com/journal/nbm

DTI OF BRAIN PLASTICITY IN SONGBIRDS

3

birds can regenerate new hair cells once the original cells havedied (56,57). In songbirds, even when nearly all original hair cellshave been lost, a new population of cells will grow and cover theepithelial surface thoroughly by 8 weeks after the loss of originalhair cells (58). Most importantly, regenerated hair cells becomeinnervated by the brain and restore hearing (58–62). Thus, thehearing loss caused by hair cell loss can be reversed. Recentstudies in humans are using DTI to quantify the effect of hearingloss on the brain and its connections (63,64). The reversibility ofhearing loss in songbirds makes them an ideal model to studythis phenomenon up close.

DTI: THE IDEAL TOOL FOR SONGBIRDBRAIN STUDIES

In vivo DTI is an MRI method that focuses on the preferentialdirectionality of water movements in tissue (14). DTI is classicallyused to study connectivity changes in the human brain duringdevelopment (65–67) or neurodegeneration (68–70). In vivostudies on small animals have been performed, although moststudies thus far have been restricted to the mammalian brain, i.e.in rats (44,71,72), mice (35,73–77) and cats (78–80). There arestrong indications that most functional sensory, motor andcognitive regions found in the mammalian telencephalon arealso present in the avian telencephalon, although both animalgroups display remarkable differences in telencephalic anatomy(21,81). The telencephalon of birds consists of conglomerationsof gray matter separated by thin lamina of white matter. This isin contrast with the cerebral organization in mammals, where asuperficial thin layer of gray matter (the laminated cortex) isclearly separated from the underlying structure of gray matter(the basal ganglia) by a thick mass of myelinated axons, theinternal capsule. This mammalian brain anatomy has proved veryaccessible to conventional MRI methods (T2-, T1- and protondensity-weighted MRI), providing superior contrast differencesbetween white and gray matter. In contrast, the telencephalon ofbirds is quite different in its gross appearance from that ofmammals, and a clear morphometric distinction between graymatter and myelinated axons cannot be made using conven-tional intrinsic MRI contrast settings, even at high resolution(82,83) (Fig. 4A). This lack of intrinsic contrast in avian brain tissuenot only applies to the subtle laminae consisting of fibers dividingtelencephalic brain regions, but also to inherent differences incytoarchitecture and even to more obvious differences betweenspecific nuclei, such as those delineating the SCS (11,84,85).More recently, the use of an in vivo tract tracing technique

based on the stereotaxic injection of paramagnetic manganese inHVC has allowed the successful labeling of its two targets, RA andArea X (86–88), but still failed to show the fiber bundles thatconnect them. Moreover, this MRI technique can only target onecircuit at a time, which limits the investigation of entire braindynamics over time.The application of the DTI method to the starling brain as a

completely noninvasive tool resulted in FA maps which allowedmost of the gray matter and white matter tracts (Fig. 4B, C) to be

Figure 2. Fiber tracking of the optic chiasm of a starling. The top row

represents the difference in the optic chiasm betweenmammals (left) andbirds (right); in birds, the retinal fibers decussate completely. The second

row presents a coronal (horizontal) color-coded fractional anisotropy (FA)

map (right is caudal) of one male starling in the breeding season. Datawere obtained using a high angular resolution diffusion imaging (HARDI),

echo planar imaging (EPI), diffusion tensor imaging (DTI) sequence with

64 different diffusion directions. Depicted on the left side are the fiber

tracking results based on the second-order diffusion tensor. DTI fibertracking is unable to resolve the crossing fibers and instead reports

‘kissing’ or ‘bending’ fibers. The shape of the second-order diffusion

tensor is oblate at the optic chiasm, resulting in low FA values and

incorrect fiber tracking results. Depicted on the right are the fiber trackingresults based on constrained spherical deconvolution (CSD) (52,53). The

fiber orientation distribution functions describe the crossing fibers quite

well, resulting in correct fiber tracking in the optic chiasm of the starling.All data were visualized using ExploreDTI (54).

Figure 3. Fractional anisotropy (FA) values of the high vocal center–

nucleus robustus arcopallialis (HVC–RA) tract of two starlings measured

over 2 years (2004–2005). FA is a dimensionless value that varies betweenzero (isotropic) and one (anisotropic).

www.interscience.wiley.com/journal/nbm Copyright � 2010 John Wiley & Sons, Ltd. NMR Biomed. (2010)

G. DE GROOF AND A. VAN DER LINDEN

4

distinguished, including the laminae subdividing the aviantelencephalon and the tracts connecting the major song controlnuclei (e.g. HVC with RA and Area X) (Fig. 1). In addition, a numberof song control, auditory and visual nuclei, previously onlyrevealed through post-mortem histology, have been discernedon FA maps (17).

DTI for repeated visualization of the SCS

The first longitudinal DTI study of songbirds, comparing a groupof male starlings between spring (the reproductive season) andsummer (the sexually quiescent period), succeeded in discerningseasonal changes in the HVC to RA projection previously onlyidentified by histology (18). The results corroborated establishedfindings on the plasticity of the HVC–RA pathway in songbirds. Ithas long been known and well documented that the number ofHVC neurons projecting to RA increases during the breedingseason (89–91). However, changes in this fiber tract per se hadnot been quantified specifically. The fewer RA-projecting HVCneurons during the nonbreeding season was reflected in a l1(eigenvalue of the first eigenvector) decrease. The decrease inaxonal connections was also confirmed by histology (18). Inaddition, an increase in RD observed during the nonbreedingseason in this tract had been correlated previously with myelinloss in studies on animal models of experimental demyelinationand remyelination (34). In canaries, specifically, testosterone hasbeen shown to enhance the myelination of both HVC and RA (92).This showed that the in vivo DTI technique was sufficientlysensitive to observe changes in songbird brains previously onlydetectable using histology (Fig. 5C).DTI allows the clear delineation of the song control nuclei, RA

and Area X (17), as a result of their encapsulation of fibers(Fig. 5A, B). Therefore, using DTI, the seasonal effect on thevolumes of RA and Area X could be investigated in male starlings.A clear and significant volume increase (37% for both RA and AreaX) has been observed in starlings during spring (the breedingseason) compared with the same starlings in summer (thenonbreeding season). The seasonal volumetric changes in Area Xand RA observed using DTI at the individual level have the sameamplitude as those found in free-ranging male starlings at thegroup level (30), which confirms previous histological studies.Moreover, none of the volumes showed a correlation betweenthe two seasons, i.e. knowing the volume of a region in a birdin spring did not allow the prediction of the volume of this regionin summer. In addition, no correlation between the regions wasfound, meaning that the amplitude of volume changes within

Figure 4. Midsagittal slice through the starling brain with different contrasts. (A) Normal T2-weighted image. (B) Fractional anisotropy (FA) map.

(C) Color-coded FA map with the direction of the first eigenvector of the diffusion tensor in color. Notice the difference in the contrast of the different

laminae and the white matter of the optic chiasm and cerebellum.

Figure 5. Seasonal changes in the song control system. (A) Sagittal

fractional anisotropy (FA) map (right side is the caudal part of the brain)

of an individual starling; the insets show the seasonal volume changes inArea X. (B) Sagittal FA map of an individual starling; the insets show

the seasonal volume changes in the nucleus robustus arcopallialis (RA).

(C) Sagittal FA map of an individual starling in the breeding season(spring) and nonbreeding season (summer). Enlargement of region

shown in (B). Notice that the fiber tract between the high vocal center

(HVC) and RA (arrowheads) contains more fibers in spring than in summer.

OM, tractus occipitomesencephalicus [Adapted and reproduced fromref. (18).]

NMR Biomed. (2010) Copyright � 2010 John Wiley & Sons, Ltd. www.interscience.wiley.com/journal/nbm

DTI OF BRAIN PLASTICITY IN SONGBIRDS

5

one bird was not consistent between regions. These correlationresults could not have been obtained using histologicaltechniques.

DTI to discern unknown changes in the connectivity of thesong control brain circuit

Potential changes in the songbird’s brain fiber tracts have beenpoorly, if at all, investigated by histological techniques. Comparedwith histology, MRI/DTI excels in two different types of anatomicalcharacterization. One is the efficient (prehistology) screening ofthe entire brain, and the other is the quantification of anatomicalshapes and sizes. Light and electron microscopy can examinesamples at a limited number of locations where histology slicesare prepared. Therefore, histology-based methods are oftenhypothesis driven, so that one can extract slices at optimumlocations and angles. There is always the possibility that regionsthat show changes are overlooked with this approach. Inaddition, it is often important to study the time evolution ofplasticity to understand the role of a region in a certain changingbehavior.Longitudinal in vivo DTI data have demonstrated significant

seasonal changes in multiple nuclei and their connections in thesong control circuit, including the HVC–RA connection, RA andthe fibers surrounding this nucleus [RA-projecting HVC fibersforming a shell around RA (93)], the medial telencephalic partof the tractus occipitomesencephalicus [OM; which contains RAfibers projecting towards the dorsomedial nucleus of theintercollicular complex of the mesencephalon (DM) (94)] andDM itself (18). In the lower part of the vocal motor circuit(e.g. OM), DTI parameter changes were limited to an increase inRD (Fig. 6). From these changes in diffusion anisotropy, it cannot

immediately be concluded that the source of abnormalities lies incellular level structures, such as myelin and axons; it could be aresult of the reorganization of axons at macroscopic levels.However, the fact that the first eigenvalue or AD remainedunchanged indicated that the variation in anisotropy possiblyreflected a decrease in myelination between spring and summer.Histology indeed confirmed that OM in starlings hadmoremyelinduring than outside the breeding season (18). It was thereforesuggested that an upregulation of myelination during thebreeding season could support an improved conduction ofelectrical signals (95). Male European starlings indeed sing atincreased rates during the breeding season and this song isprimarily involved in mate attraction (33). The medial tele-ncephalic part of OM harbors projection fibers from RA to DMand the vocal motor nucleus (XIIts) (94,96). RA neurons show aseasonal plasticity in spontaneous firing rate (97) and thisplasticity is affected by steroid hormones (98). In this context, it isalso important to note that gonadal sex steroids modulatebrain myelination in mammals (99,100) and canaries (92).Recently, it has been shown that electrical activity in axons actson surrounding astrocytes which, in turn, affect myelinatingoligodendrocytes (101). This activity-dependent mechanismmight regulate the seasonal myelination detected by DTI inOM as a function of the discharges originating from RA.Within AFP, the lamina mesopallialis (LaM), which contains the

connections between HVC and Area X and between the lateralmagnocellular nucleus of the anterior nidopallium (LMAN) andRA (see Fig. 1D), also displayed seasonal changes, as demon-strated by a l1-induced change [both FA and AD (¼ l1) are higherin the spring]. Unlike RA-projecting HVC neurons, the numbers ofX-projecting HVC neurons and RA-projecting LMAN neuronsremain stable during life (102). The change in AD observed in thecorresponding structures (LaM, the lamina containing bothconnections, the capsule around Area X and LMAN) suggested,however, a seasonal change in either the number of axons or thecoherence of these axons. Axonal retraction of Area X-projectingHVC neurons has been reported (103). A retraction of these HVCaxons could alter the coherence of the fiber tract in which theseaxons, as well as the LMAN-to-RA-projecting axons (which may ormay not retract), reside.

DTI reveals unknown seasonal changes outside thesong control

DTI was originally validated in songbirds by showing that itidentifies changes in brain structure or volume consistentwith previous results obtained by histological or tract-tracingapproaches. Recently, it has started to reveal new braincharacteristics, such as the prominent plasticity affecting notonly the SCS, but also large areas of the songbird brain (18,19).A more unexpected result in a starling DTI study was the

detection of seasonal changes in the optic chiasm (18) (Fig. 7). Achange in anisotropy was observed seasonally as a result of anincreased RD in summer. The possible physiological reasonbehind this change could not be ascertained with 100% certainty,because no histology was performed (because the optic chiasm islost when preparing the brain from the skull). An RD changecould be caused by a seasonal change in myelination. A seasonalpeak of the enzyme 5a-reductase, whose metabolites play a rolein myelination, has been observed in starlings, and this enzyme isexpressed in high concentrations in myelin membranes of theoptic chiasm of mammals [see De Groof et al. (18) for a more

Figure 6. Enlargement of region shown in Fig. 5B. The images presentfiber tracking with the nucleus robustus arcopallialis (RA) as seed point.

The insets show the individual diffusion tensors of the tractus occipito-

mesencephalicus (OM). Notice that they are ‘broader’ in July compared

with April. This is a result of the increase in l2 and l3 (increase in radialdiffusivity RD) in July. HVC, acronym now used as a proper name, formerly

high vocal center. [Adapted and reproduced from ref. (18).]

www.interscience.wiley.com/journal/nbm Copyright � 2010 John Wiley & Sons, Ltd. NMR Biomed. (2010)

G. DE GROOF AND A. VAN DER LINDEN

6

detailed explanation]. Another explanation which is currentlyunder investigation could be that the amount of crossing fibersdiffers between seasons in starlings. The other main nuclei of thevisual system were also investigated (nucleus rotundus, optictectum, visual Wulst and nucleus geniculatus lateralis parsdorsalis principalis), but no seasonal changes were detected inthese structures. Male starlings become sensitive to females andstart to produce female-directed song during the breedingseason. Seasonal plasticity in the visual system might mediatechanges in visual acuity that could potentially play a role inthe control of reproductive behavior. Such functional changesin the visual system of songbirds have not been documentedpreviously, but should definitely be investigated based on themorphological changes detected here.The two hemispheres of the bird telencephalon are connected

to each other by two pathways: the commissura anterior andcommissura posterior (CoP). In the CoP, it was detected that themean diffusivity was lower in the breeding season than in thenonbreeding season. However, the shape of the diffusion tensorsin CoP remained the same (i.e. no change in FA between seasons),indicating a similar coherence of axons in this commissurebetween seasons. This could indicate that this fiber tract duringthe breeding season might be tighter (e.g. less extracellular spacebetween axons). CoP is a connection involved in the synchro-nization of the premotor activity in both hemispheres (104), andthe observed change in the interhemispheric connection mightserve to enhance the mentioned synchronization during thebreeding season.Despite the abundant use of DTI in the investigation of brain

white matter changes (in humans and small animals), its use inthe investigation of gray matter changes is relatively recent(105–107). Nevertheless, DTI can also provide quantitativedata on changes in diffusivity resulting from intracellularchanges (protein synthesis, vesicle formation or concentrationof organelles) and extracellular changes, such as dendritebranching (108–110).The ‘social behavior network’ (SBN), an interconnected group

of structures in the mammalian brain, controls multiple socialbehaviors, including aggression and courtship (111). In songbirds,homologs for these regions have been identified (112) and areimplicated in the regulation of singing behavior (113–115). Most

research on neural control of singing has focused exclusively onSCS, without reference to how this system might interactwith SBN, involved in the anticipation of sexual behavior and themotivation to sing (113–115). As these two functions varyseasonally, one might expect seasonal plasticity in this network.More and more studies have suggested that SBN differentiallyregulates song production within and outside the breedingseason (116). In seasonal breeding songbirds, gonadal steroidsincrease SBN activity induced by a socio-sexual signal (117).Seasonal plasticity in a number of SBN nuclei, such as the

preoptic area (PoA) and nucleus ventromedialis hypothalami(VMN), in terms of seasonal changes in FA (as a result of changesin the third eigenvalue l3), have been observed (19). Numerousstudies have shown the importance of aromatase for theregulation of the sexual and aggressive behavior of starlingsduring the reproductive season, particularly in brain areas such asPoA and VMN (28,118). Aromatase converts androgens intoestrogens (119). Aromatase-positive neurons have highly com-plex branching patterns and large nuclei (120), two factors thatinfluence the diffusion of water protons of tissues (121,122). Theexpression of aromatase in seasonal breeding songbirds varieswith seasons, being higher during the breeding season (123).The presence of statistically significant changes in l3 betweenseasons in brain regions that display seasonal changes inaromatase activity have led to the hypothesis that l3 changescould be a possible marker of aromatase activity changes.Indirect neuro-anatomical connections between SBN and SCShave been found in starlings (124). Furthermore, it has beensuggested that, in starlings, PoA stimulates vocal communicationin a sexually relevant context, but inhibits vocal communicationoutside such a context (125). By investigating the individualseasonal plasticity of PoA compared with the plasticity of songcontrol nuclei, it was demonstrated that both Area X and RAplasticity were positively linked with PoA plasticity (19). This resulthas contributed to the growing body of research that hashighlighted the major role of PoA in providing contextual inputto SCS.As in other songbirds, the avian forebrain analog of the

secondary auditory cortex, the caudomedial nidopallium (NCM),in starlings is responsible for auditory discrimination and songrecognition memories (126). Song production and, as a result, the

Figure 7. Seasonal changes in the optic chiasm (CO). The left panel presents a midsagittal color-coded fractional anisotropy (FA) map (right is caudal) of

one male starling in the breeding season. The middle panel illustrates, at higher magnification, the region of the CO of the same subject in the two

different seasons. The colors define the main diffusion direction in each voxel (red, rostral–caudal; green, dorsal–ventral; blue, medial–lateral; seeschematic representation by arrows in the top left part of the figure). Schematic representations of the diffusion tensors have also been overlaid on CO.

Note the broader tensors in July, although the main diffusion direction (color coding) of the tensors does not differ between seasons. Scale bar, 10mm.

Cb, cerebellum; CoA, commissura anterior; CP, commissura posterior. [Reproduced from ref. (18).]

NMR Biomed. (2010) Copyright � 2010 John Wiley & Sons, Ltd. www.interscience.wiley.com/journal/nbm

DTI OF BRAIN PLASTICITY IN SONGBIRDS

7

auditory environment of starlings change seasonally (33).Seasonal variation in peripheral and brainstem auditory activityin several songbirds has also been observed (127). The tuningwidth to simple tone stimuli of NCM neurons in canaries alsoshowed a seasonal variation (128). Furthermore, gonadal steroidsappear to increase the salience of socio-sexual signals actingthrough auditory, visual or olfactory systems (129). For instance,NCM is selective for song overtones only when plasma steroidsexceed nonbreeding levels in a female seasonal songbird (130). Inaddition, it is known that a large population of NCM neurons inzebra finches express aromatase (120). Using DTI, a seasonaleffect on NCM at the structural level was detected for thefirst time (19), involving a seasonal change in volume (about6% increase in spring) and in the diffusion parameter l3. Therepeated observation of seasonal l3 changes, exclusively in brainregions with known seasonal aromatase activity changes,provides support for the hypothesis that l3 changes could bea readout for aromatase activity changes.It has been shown in starlings that the responsiveness to song

changes as a function of day length (131). An increased volume ofan auditory region in spring may thus be an indication ofincreased auditory sensitivity at that time, possibly aiding in theperception of vocal signals. Sexual competition (33) may be thedriving force increasing starling acoustic sensitivity in the spring.

CONCLUSIONS AND FUTUREPERSPECTIVES

Studies on songbirds based on DTI have enabled us to visualizethe SCS repeatedly in the same subjects, quantifying long-itudinally the volume and cellular changes of the song nuclei andtheir connections. Since then, the aim has been to explore, in aquantitative manner, the seasonal plasticity of the songbird brainbeyond the SCS. It has been revealed, for the first time, thatseasonal microstructural and volumetric changes occur in asecondary auditory region of the telencephalon (NCM). Seasonalchanges in cellular attributes in regions of the SBN and seasonalchanges in white matter tracts of the visual system andhemispheric interconnections have been observed. DTI as a toolin songbirds is now available to explore a long list of unresolvedissues in songbird neuroscience, such as the respective rolesplayed by testosterone, social stimuli and singing activity itself inthe control of neural plasticity inside and outside the SCS.The songbird brain itself offers several advantages for the

validation and characterization of diffusion techniques. As a resultof the reversible nature of neuroplasticity, the songbird brainpresents itself as a natural model, i.e. no transgenic model orlesions are necessary and birds can be used as their own control.The extensive histology that has been performed to studyseasonal changes in some circuits of the songbird brain over thelast 25 years indicates that there is a wealth of knowledgethat can be utilized to interpret diffusion data. Moreover,the discovery of new regions showing seasonal plasticity in thesongbird brain further paves the way for the use of DTI as a toolfor the study of neuroplasticity in small rodents, monkeys andeven humans, especially now that extensive rewiring in theadult brain of monkeys after brain injury (132) or after learning achallenging new skill (133) is emerging. Training-inducedplasticity has also been shown in humans [for a review, see (134)].The next step is to move towards voxel-based morphometry in

order to unravel both volumetric and DTI parameter-related

seasonal changes in the songbird brain. Although there is debateabout the fidelity of voxel-based morphometry comparison datafor reasons such as registration errors caused by nonlineardistortions in MR images, within-group and longitudinal datawith high spatial resolution and constant SNRs, as acquired inseasonal studies on the same group of birds, might provide veryappropriate material for voxel-based morphometry. This wouldthen lead to probabilistic population-based seasonal brain atlasesand their statistical difference maps.A further characterization/specification of the diffusion

distribution in voxels would also be preferable in songbirds,specifically for the seasonal changes observed in the opticchiasm. High angular resolution diffusion imaging (HARDI)approaches, such as constrained spherical deconvolution (51),could help to improve the sensitivity and specificity of theobserved seasonal changes (Fig. 2) (52,53). However, this requiresfaster imaging techniques that must be corrected for suscepti-bility artifacts induced by the hollow skull (be it withpost-processing or fieldmap-based techniques).

Acknowledgements

This research was supported by Concerted Research Actions(GOA funding) from the University of Antwerp, Inter UniversityAttraction Poles (IUAP-NIMI-P6/38) and grants from ResearchFoundation – Flanders (FWO, project Nr G.0420.02) to AVdL.GDGwas supported by a PhD Fellowship from FWO. This researchwas partly sponsored by European Networks of Excellence(NoE) EMIL (LSHC-CT-2004-503569) and NoE DiMI (LSHB-CT-2005-512146). We wish to thank Ben Jeurissen and Prof. DrJan Sijbers (Vision Laboratory, University of Antwerp, Belgium)for their input and processing of the HARDI optic chiasm data. Wethank Dr Alexander Leemans (Image Sciences Institute, Utrecht,the Netherlands) for providing the ExploreDTI program.

REFERENCES

1. Phillmore LS, Hoshooley JS, Sherry DF, Dougall-Shackleton SA. Annualcycle of the black-capped chickadee: seasonality of singing rates andvocal-control brain regions. J. Neurobiol. 2006; 66: 1002–1010.

2. Nottebohm F. A brain for all seasons: cyclical anatomical changes insong control nuclei of the canary brain. Science 1981; 214: 1368–1370.

3. Kirn JR, Clower RP, Kroodsma DE, DeVoogd TJ. Song-related brainregions in the red-winged blackbird are affected by sex and seasonbut not repertoire size. J. Neurobiol. 1989; 20: 139–163.

4. Brenowitz EA, Nalls B, Wingfield JC, Kroodsma DE. Seasonal changesin avian song nuclei without seasonal changes in song repertoire.J. Neurosci. 1991; 11: 1367–1374.

5. Brenowitz EA, Baptista LF, Lent K, Wingfield JC. Seasonal plasticity ofthe song control system in wild Nuttall’s white-crowned sparrows.J. Neurobiol. 1998; 34: 69–82.

6. Bernard DJ, Ball GF. Two histological markers reveal a similar photo-periodic difference in the volume of the high vocal center in maleEuropean starlings. J. Comp. Neurol. 1995; 360: 726–734.

7. Bernard DJ, Ball GF. Photoperiodic condition modulates the effects oftestosterone on song control nuclei volumes in male Europeanstarlings. Gen. Comp. Endocrinol. 1997; 105: 276–283.

8. Smith GT, Brenowitz EA, Wingfield JC, Baptista LF. Seasonal changes insong nuclei and song behavior in Gambel’s white-crowned sparrows.J. Neurobiol. 1995; 28: 114–125.

9. Smith GT. Seasonal plasticity in the song nuclei of wild rufous-sidedtowhees. Brain Res. 1996; 734: 79–85.

10. Caro SP, Lambrechts MM, Balthazart J. Early seasonal development ofbrain song control nuclei in male blue tits. Neurosci. Lett. 2005; 386:139–144.

www.interscience.wiley.com/journal/nbm Copyright � 2010 John Wiley & Sons, Ltd. NMR Biomed. (2010)

G. DE GROOF AND A. VAN DER LINDEN

8

11. Tramontin AD, Brenowitz EA. Seasonal plasticity in the adult brain.Trends Neurosci. 2000; 23: 251–258.

12. Ball GF, Auger CJ, Bernard DJ, Charlier TD, Sartor JJ, Riters LV, BalthazartJ. Seasonal plasticity in the song control system: multiple brain sites ofsteroid hormone action and the importance of variation in songbehavior. Ann. NY Acad. Sci. 2004; 1016: 586–610.

13. Brenowitz EA. Plasticity of the adult avian song control system. Ann.NY Acad. Sci. 2004; 1016: 560–585.

14. Basser PJ, Jones DK. Diffusion-tensor MRI: theory, experimental designand data analysis – a technical review. NMR Biomed. 2002; 15:456–467.

15. Jones DK, Basser PJ. ‘Squashing peanuts and smashing pumpkins’:how noise distorts diffusion-weighted MR data. Magn. Reson. Med.2004; 52: 979–993.

16. Laun FB, Schad LR, Klein J, Stieltjes B. How background noise shiftseigenvectors and increases eigenvalues in DTI. MAGMA, 2009; 22:151–158.

17. De Groof G, Verhoye M, Van Meir V, Tindemans I, Leemans A, Van derLinden A. In vivo diffusion tensor imaging (DTI) of brain subdivisionsand vocal pathways in songbirds. NeuroImage, 2006; 29: 754–763.

18. De Groof G, Verhoye M, Van Meir V, Balthazart J, Van der Linden A.Seasonal rewiring of the songbird brain: an in vivo MRI study. Eur. J.Neurosci. 2008; 28: 2475–2485.

19. De Groof G, Verhoye M, Poirier C, Leemans A, Eens M, Darras VM, Vander Linden A. Structural changes between seasons in the songbirdauditory forebrain. J. Neurosci. 2009; 29: 13 557–13 565.

20. Doupe AJ, Kuhl PK. Birdsong and human speech: common themesand mechanisms. Annu. Rev. Neurosci. 1999; 22: 567–631.

21. Reiner A, Perkel DJ, Bruce LL, Butler AB, Csillag A, Kuenzel W, Medina L,Paxinos G, Shimizu T, Striedter G, Wild M, Ball GF, Durand S, GuturkunO, Lee DW, Mello CV, Powers A, White SA, Hough G, Kubikova L,Smulders TV, Wada K, Dugas F, Husband S, Yamamoto K, Yu J, Siang C,Jarvis ED. Revised nomenclature for avian telencephalon and somerelated brainstem nuclei. J. Comp. Neurol. 2004; 473: 377–414.

22. Nottebohm F, Kelley DB, Paton JA. Connections of vocal control nucleiin the canary telencephalon. J. Comp. Neurol. 1982; 207: 344–357.

23. Nottebohm F. Testosterone triggers growth of brain vocal controlnuclei in adult female canaries. Brain Res. 1980; 189: 429–436.

24. Doupe AJ, Solis MM, Kimpo R, Boettiger CA. Cellular, circuit, andsynaptic mechanisms in song learning. Ann. NYAcad. Sci. 2004; 1016:495–523.

25. Wild JM. Neural pathways for the control of birdsong production.J. Neurobiol. 1997; 33: 653–670.

26. Brainard MS, Doupe AJ. Auditory feedback in learning and mainten-ance of vocal behaviour. Nat. Rev. Neurosci. 2000; 1: 31–40.

27. Brenowitz EA, Margoliash D, Nordeen KW. An introduction to bird-song and the avian song system. J. Neurobiol. 1997; 33: 495–500.

28. Riters LV, Eens M, Pinxten R, Duffy DL, Balthazart J, Ball GF. Seasonalchanges in courtship song and the medial preoptic area in maleEuropean starlings (Sturnus vulgaris). Horm. Behav. 2000; 38: 250–261.

29. Ball GF, Wingfield JC. Changes in plasma-levels of luteinizin-g-hormone and sex steroid-hormone in relation to multiple-broodedness and nest-site density in male starlings. Phys. Zool.1987; 60: 191–199.

30. Riters LV, Eens M, Pinxten R, Ball GF. Seasonal changes in the densitiesof alpha(2) noradrenergic receptors are inversely related to changesin testosterone and the volumes of song control nuclei in maleEuropean starlings. J. Comp. Neurol. 2002; 444: 63–74.

31. Summers RW, Westlake GE, Feare CJ. Differences in the ages, sexesand physical condition of starlings Sturnus-vulgaris at the center andperiphery of roosts. Ibis, 1987; 129: 96–102.

32. Hausberger M, Richardyris MA, Henry L, Lepage L, Schmidt I. Songsharing reflects the social-organization in a captive group of Euro-pean starlings (Sturnus vulgaris). J. Comp. Psychol. 1995; 109: 222–241.

33. EensM. Understanding the complex song of the European starling: anintegrated ethological approach. Adv. Study Anim. Behav. 1997; 26:355–434.

34. Song SK, Yoshino J, Le TQ, Lin SJ, Sun SW, Cross AH, Armstrong RC.Demyelination increases radial diffusivity in corpus callosum ofmouse brain. NeuroImage, 2005; 26: 132–140.

35. Song SK, Sun SW, Ramsbottom MJ, Chang C, Russell J, Cross AH.Dysmyelination revealed through MRI as increased radial (butunchanged axial) diffusion of water. NeuroImage, 2002; 17: 1429–1436.

36. Pierpaoli C, Basser PJ. Toward a quantitative assessment of diffusionanisotropy. Magn. Reson. Med. 1996; 36: 893–906.

37. Basser PJ, Pajevic S. Statistical artifacts in diffusion tensorMRI (DT-MRI)caused by background noise. Magn. Reson. Med. 2000; 44: 41–50.

38. Alexander DC, Barker GJ, Arridge SR. Detection and modeling ofnon-Gaussian apparent diffusion coefficient profiles in human braindata. Magn Reson Med. 2002; 48: 331–340.

39. Frank LR. Anisotropy in high angular resolution diffusion-weightedMRI. Magn. Reson. Med. 2001; 45: 935–939.

40. Assaf Y, Pasternak O. Diffusion tensor imaging (DTI)-based whitematter mapping in brain research: a review. J. Mol. Neurosci. 2008;34: 51–61.

41. Mori S, van Zijl PC. Fiber tracking: principles and strategies – atechnical review. NMR Biomed. 2002; 15: 468–480.

42. Kaufman JA, Ahrens ET, Laidlaw DH, Zhang S, Allman JM. Anatomicalanalysis of an aye-aye brain (Daubentonia madagascariensis, primates:Prosimii) combining histology, structural magnetic resonance ima-ging, and diffusion-tensor imaging. Anat. Rec. A, Discov. Mol. Cell Evol.Biol. 2005; 287: 1026–1037.

43. Dauguet J, Peled S, Berezovskii V, Delzescaux T, Warfield SK, Born R,Westin CF. Comparison of fiber tracts derived from in-vivo DTItractography with 3D histological neural tract tracer reconstructionon a macaque brain. NeuroImage, 2007; 37: 530–538.

44. Lin CP, Tseng WY, Cheng HC, Chen JH. Validation of diffusion tensormagnetic resonance axonal fiber imaging with registered mangane-se-enhanced optic tracts. NeuroImage, 2001; 14: 1035–1047.

45. Le Bihan D, Poupon C, Amadon A, Lethimonnier F. Artifacts and pitfallsin diffusion MRI. J. Magn. Reson. Imaging, 2006; 24: 478–488.

46. Wedeen VJ, Hagmann P, Tseng WY, Reese TG, Weisskoff RM. Mappingcomplex tissue architecture with diffusion spectrum magnetic reson-ance imaging. Magn. Reson. Med. 2005; 54: 1377–1386.

47. Cory DG, Garroway AN. Measurement of translational displacementprobabilities by NMR: an indicator of compartmentation. Magn.Reson. Med. 1990; 14: 435–444.

48. Tuch DS, Reese TG, Wiegell MR, Makris N, Belliveau JW, Wedeen VJ.High angular resolution diffusion imaging reveals intravoxel whitematter fiber heterogeneity. Magn. Reson. Med. 2002; 48: 577–582.

49. Tuch DS. Q-ball imaging. Magn. Reson. Med. 2004; 52: 1358–1372.50. Cowan WM, Adamson L, Powell TPS. Experimental study of avian

visual system. J. Anat. 1961; 95: 545–563.51. Tournier JD, Calamante F, Connelly A. Robust determination of the

fibre orientation distribution in diffusion MRI: non-negativity con-strained super-resolved spherical deconvolution. NeuroImage, 2007;35: 1459–1472.

52. Jeurissen B, Leemans A, Tournier JD, Sijbers J. Fiber tracking on the‘fiber cup phantom’ using constrained spherical deconvolution.Medical Image Computing and Computer-Assisted Intervention (MICCAI)Workshop on DiffusionModelling and the Fiber Cup (DMFC’09), London,UK, 2009; 232–235.

53. Jeurissen B, Leemans A, Jones DK, Tournier JD, Sijbers J. Probabilisticfiber tracking using the residual bootstrap with constrained sphericaldeconvolution. Hum. Brain Mapping, 2010; DOI: 10.1002/hbm.21032.

54. Leemans A, Jeurissen B, Sijbers J, Jones DK. ExploreDTI: a graphicaltoolbox for processing, analyzing, and visualizing diffusion MR data.17th Annual Meeting of the International Society of Magnetic ResonanceMedicine, Hawaii, USA, 2009; 3537.

55. Brown SD, Bottjer SW. Testosterone-induced changes in adult canarybrain are reversible. J. Neurobiol. 1993; 24: 627–640.

56. Cotanche DA. Regeneration of hair cell stereociliary bundles in thechick cochlea following severe acoustic trauma. Hear Res. 1987; 30:181–195.

57. Cruz RM, Lambert PR, Rubel EW. Light microscopic evidence of haircell regeneration after gentamicin toxicity in chick cochlea. Arch.Otolaryngol. Head Neck Surg. 1987; 113: 1058–1062.

58. Woolley SMN, Rubel EW. Vocal memory and learning in adult Benga-lese Finches with regenerated hair cells. J. Neurosci. 2002; 22:7774–7787.

59. Tucci DL, Rubel EW. Physiologic status of regenerated hair cells in theavian inner ear following aminoglycoside ototoxicity. Otolaryngol.Head Neck Surg. 1990; 103: 443–450.

60. Marean GC, Burt JM, Beecher MD, Rubel EW. Hair cell regeneration inthe European starling (Sturnus vulgaris): recovery of pure-tone detec-tion thresholds. Hear Res. 1993; 71: 125–136.

NMR Biomed. (2010) Copyright � 2010 John Wiley & Sons, Ltd. www.interscience.wiley.com/journal/nbm

DTI OF BRAIN PLASTICITY IN SONGBIRDS

9

61. Marean GC, Cunningham D, Burt JM, Beecher MD, Rubel EW. Regen-erated hair cells in the European starling: are they more resistant tokanamycin ototoxicity than original hair cells? Hear Res. 1995; 82:267–276.

62. Woolley SM, Wissman AM, Rubel EW. Hair cell regeneration andrecovery of auditory thresholds following aminoglycoside ototoxicityin Bengalese finches. Hear Res. 2001; 153: 181–195.

63. Reiman M, Parkkola R, Johansson R, Jaaskelainen SK, Kujari H, Lehto-nen L, Haataja L, Lapinleimu H. Diffusion tensor imaging of the inferiorcolliculus and brainstem auditory-evoked potentials in preterminfants. Pediatr. Radiol. 2009; 39: 804–809.

64. Lin Y, Wang J, Wu C, Wai Y, Yu J, Ng S. Diffusion tensor imaging of theauditory pathway in sensorineural hearing loss: changes in radialdiffusivity and diffusion anisotropy. J. Magn. Reson. Imaging, 2008; 28:598–603.

65. Huppi PS, Dubois J. Diffusion tensor imaging of brain development.Semin. Fetal Neonatal Med. 2006; 11: 489–497.

66. Cascio CJ, Gerig G, Piven J. Diffusion tensor imaging: application tothe study of the developing brain. J. Am. Acad. Child Adolesc.Psychiatry, 2007; 46: 213–223.

67. Lebel C, Walker L, Leemans A, Phillips L, Beaulieu C. Microstructuralmaturation of the human brain from childhood to adulthood. Neuro-Image, 2008; 40: 1044–1055.

68. Ulug AM, Moore DF, Bojko AS, Zimmerman RD. Clinical use ofdiffusion-tensor imaging for diseases causing neuronal and axonaldamage. Am. J. Neuroradiol. 1999; 20: 1044–1048.

69. Rose SE, Chen F, Chalk JB, Zelaya FO, Strugnell WE, Benson M, SempleJ, Doddrell DM. Loss of connectivity in Alzheimer’s disease: anevaluation of white matter tract integrity with colour coded MRdiffusion tensor imaging. J. Neurol. Neurosurg. Psychiatry, 2000; 69:528–530.

70. Taber KH, Pierpaoli C, Rose SE, Rugg-Gunn FJ, Chalk JB, Jones DK,Hurley RA. The future for diffusion tensor imaging in neuropsychiatry.J. Neuropsychiatry Clin. Neurosci. 2002; 14: 1–5.

71. Xue R, van Zijl PC, Crain BJ, Solaiyappan M, Mori S. In vivo three-dimensional reconstruction of rat brain axonal projections bydiffusion tensor imaging. Magn. Reson. Med. 1999; 42: 1123–1127.

72. Cheung JS, Fan SJ, Chow AM, Hui ES, Wu EX. In vivo DTI assessment ofhepatic ischemia reperfusion injury in an experimental rat model. J.Magn. Reson. Imaging, 2009; 30: 890–895.

73. Zhang J, van Zijl PC, Mori S. Three-dimensional diffusion tensormagnetic resonance microimaging of adult mouse brain and hippo-campus. NeuroImage, 2002; 15: 892–901.

74. Song SK, Sun SW, Ju WK, Lin SJ, Cross AH, Neufeld AH. Diffusion tensorimaging detects and differentiates axon and myelin degeneration inmouse optic nerve after retinal ischemia. NeuroImage, 2003; 20:1714–1722.

75. Song SK, Kim JH, Lin SJ, Brendza RP, Holtzman DM. Diffusion tensorimaging detects age-dependent white matter changes in a trans-genic mousemodel with amyloid deposition. Neurobiol. Dis. 2004; 15:640–647.

76. Sun SW, Song SK, Hong CY, Chu WC, Chang C. Directional correlationcharacterization and classification of white matter tracts. Magn.Reson. Med. 2003; 49: 271–275.

77. Baloch S, Verma R, Huang H, Khurd P, Clark S, Yarowsky P, Abel T, MoriS, Davatzikos C. Quantification of brain maturation and growthpatterns in C57BL/6J mice via computational neuroanatomy of diffu-sion tensor images. Cereb. Cortex, 2009; 19: 675–687.

78. Ronen I, Kim KH, Garwood M, Ugurbil K, Kim DS. Conventional DTI vs.slow and fast diffusion tensors in cat visual cortex. Magn. Reson. Med.2003; 49: 785–790.

79. Kim DS, Kim M, Ronen I, Formisano E, Kim KH, Ugurbil K, Mori S,Goebel R. In vivo mapping of functional domains and axonal con-nectivity in cat visual cortex using magnetic resonance imaging.Magn. Reson. Imaging, 2003; 21: 1131–1140.

80. Takahashi E, Dai G, Wang R, Ohki K, Rosen GD, Galaburda AM, GrantPE, Wedeen VJ. Development of cerebral fiber pathways in catsrevealed by diffusion spectrum imaging. NeuroImage, 2010; 49:1231–1240.

81. Jarvis ED, Gunturkun O, Bruce L, Csillag A, Karten H, Kuenzel W,Medina L, Paxinos G, Perkel DJ, Shimizu T, Striedter G, Wild JM, Ball GF,Dugas-Ford J, Durand SE, Hough GE, Husband S, Kubikova L, Lee DW,Mello CV, Powers A, Siang C, Smulders TV, Wada K, White SA,Yamamoto K, Yu J, Reiner A, Butler AB. Avian brains and a new

understanding of vertebrate brain evolution. Nat. Rev. Neurosci.2005; 6: 151–159.

82. Van der Linden A, Verhoye M, Peeters R, Eens M, Newman SW,Smulders T, Balthazart J, DeVoogd TJ. Non invasive in vivo anatomicalstudies of the oscine brain by high resolution MRI microscopy. J.Neurosci. Methods, 1998; 81: 45–52.

83. Verhoye M, Van der Linden A, Van Audekerke J, Sijbers J, Eens M,Balthazart J. Imaging birds in a bird cage: in-vivo FSE 3D MRI of birdbrain. MAGMA, 1998; 6: 22–27.

84. Tramontin AD, Smith GT, Breuner CW, Brenowitz EA. Seasonalplasticity and sexual dimorphism in the avian song control system:stereological measurement of neuron density and number. J. Comp.Neurol. 1998; 396: 186–192.

85. Thompson CK, Brenowitz EA. Seasonal change in neuron size andspacing but not neuronal recruitment in a basal ganglia nucleus in theavian song control system. J. Comp. Neurol. 2005; 481: 276–283.

86. Van der Linden A, Verhoye M, Van Meir V, Tindemans I, Eens M, Absil P,Balthazart J. In vivo manganese-enhanced magnetic resonance ima-ging reveals connections and functional properties of the songbirdvocal control system. Neuroscience, 2002; 112: 467–474.

87. Van der Linden A, Van Meir V, Tindemans I, Verhoye M, Balthazar J.Applications of manganese-enhanced magnetic resonance imaging(MEMRI) to image brain plasticity in song birds. NMR Biomed. 2004;17: 602–612.

88. Tindemans I, Verhoye M, Balthazart J, Van der Linden A. In vivodynamic ME-MRI reveals differential functional responses of RA-and area X-projecting neurons in the HVC of canaries exposed toconspecific song. Eur. J. Neurosci. 2003; 18: 3352–3360.

89. Rasika S, Nottebohm F, Alvarez-Buylla A. Testosterone increases therecruitment and/or survival of new high vocal center neurons in adultfemale canaries. Proc. Natl. Acad. Sci. USA, 1994; 91: 7854–7858.

90. Hidalgo A, Barami K, Iversen K, Goldman SA. Estrogens and non-estrogenic ovarian influences combine to promote the recruitmentand decrease the turnover of new neurons in the adult female canarybrain. J. Neurobiol. 1995; 27: 470–487.

91. Tramontin AD, Brenowitz EA. A field study of seasonal neuronalincorporation into the song control system of a songbird that lacksadult song learning. J. Neurobiol. 1999; 40: 316–326.

92. Stocker S, Guttinger HR, Herth G. Exogenous testosterone differen-tially affects myelination and neurone soma sizes in the brain ofcanaries. Neuroreport, 1994; 5: 1449–1452.

93. Konishi M, Akutagawa E. Neuronal growth, atrophy and death in asexually dimorphic song nucleus in the zebra finch brain. Nature,1985; 315: 145–147.

94. Wild JM. Descending projections of the songbird nucleus robustusarchistriatalis. J. Comp. Neurol. 1993; 338: 225–241.

95. Waxman SG. Conduction in myelinated, unmyelinated, and demye-linated fibers. Arch. Neurol. 1977; 34: 585–589.

96. Wild JM. Functional neuroanatomy of the sensorimotor control ofsinging. Ann. NY Acad. Sci. 2004; 1016: 438–462.

97. Meitzen J, Perkel DJ, Brenowitz EA. Seasonal changes in intrinsicelectrophysiological activity of song control neurons in wild songsparrows. J. Comp. Physiol. A, Neuroethol. Sens. Neural Behav. Physiol.2007; 193: 677–683.

98. Park KH, Meitzen J, Moore IT, Brenowitz EA, Perkel DJ. Seasonal-likeplasticity of spontaneous firing rate in a songbird pre-motor nucleus.J. Neurobiol. 2005; 64: 181–191.

99. Celotti F, Melcangi RC, Negri-Cesi P, Ballabio M, Martini L. Differentialdistribution of the 5-alpha-reductase in the central nervous system ofthe rat and the mouse: are the white matter structures of the braintarget tissue for testosterone action? J. Steroid Biochem. 1987; 26:125–129.

100. Melcangi RC, Celotti F, Ballabio M, Poletti A, Castano P, Martini L.Testosterone 5 alpha-reductase activity in the rat brain is highlyconcentrated in white matter structures and in purified myelinsheaths of axons. J. Steroid Biochem. 1988; 31: 173–179.

101. Ishibashi T, Dakin KA, Stevens B, Lee PR, Kozlov SV, Stewart CL, FieldsRD. Astrocytes promote myelination in response to electricalimpulses. Neuron, 2006; 49: 823–832.

102. Alvarez-Buylla A, Theelen M, Nottebohm F. Birth of projectionneurons in the higher vocal center of the canary forebrain before,during, and after song learning. Proc. Natl. Acad. Sci. USA, 1988; 85:8722–8726.

www.interscience.wiley.com/journal/nbm Copyright � 2010 John Wiley & Sons, Ltd. NMR Biomed. (2010)

G. DE GROOF AND A. VAN DER LINDEN

10

103. Thompson CK, Bentley GE, Brenowitz EA. Rapid seasonal-likeregression of the adult avian song control system. Proc. Natl. Acad.Sci. USA, 2007; 104: 15 520–15 525.

104. Schmidt MF, Ashmore RC, Vu ET. Bilateral control and interhemi-spheric coordination in the avian song motor system. Ann. N. Y.Acad. Sci. 2004; 1016: 171–187.

105. Bhagat YA, Beaulieu C. Diffusion anisotropy in subcortical whitematter and cortical gray matter: changes with aging and the role ofCSF-suppression. J. Magn. Reson. Imaging, 2004; 20: 216–227.

106. Lebel C, Rasmussen C, Wyper K, Walker L, Andrew G, Yager J,Beaulieu C. Brain diffusion abnormalities in children with fetalalcohol spectrum disorder. Alcohol Clin. Exp. Res. 2008; 32:1732–1740.

107. Pfefferbaum A, Adalsteinsson E, Rohlfing T, Sullivan EV. Diffusiontensor imaging of deep gray matter brain structures: effects of ageand iron concentration. Neurobiol Aging. 2010; 31: 482–493.

108. Pierpaoli C, Righini A, Linfante I, Tao-Cheng JH, Alger JR, Di CG.Histopathologic correlates of abnormal water diffusion in cerebralischemia: diffusion-weighted MR imaging and light and electronmicroscopic study. Radiology, 1993; 189: 439–448.

109. Beaulieu C. The basis of anisotropic water diffusion in the nervoussystem – a technical review. NMR Biomed. 2002; 15: 435–455.

110. Hasan KM, Halphen C, Kamali A, Nelson FM, Wolinsky JS, NarayanaPA. Caudate nuclei volume, diffusion tensor metrics, and T(2) relaxa-tion in healthy adults and relapsing–remitting multiple sclerosispatients: implications for understanding gray matter degeneration.J. Magn. Reson. Imaging, 2009; 29: 70–77.

111. Newman SW. The medial extended amygdala in male reproductivebehavior. A node in the mammalian social behavior network. Ann.NY Acad. Sci. 1999; 877: 242–257.

112. Goodson JL. The vertebrate social behavior network: evolutionarythemes and variations. Horm. Behav. 2005; 48: 11–22.

113. Riters LV, Ball GF. Lesions to themedial preoptic area affect singing inthe male European starling (Sturnus vulgaris). Horm. Behav. 1999; 36:276–286.

114. Maney DL, Ball GF. Fos-like immunoreactivity in catecholaminergicbrain nuclei after territorial behavior in free-living song sparrows.J. Neurobiol. 2003; 56: 163–170.

115. Heimovics SA, Cornil CA, Ball GF, Riters LV. D1-like dopamine receptordensity in nuclei involved in social behavior correlates with song in acontext-dependent fashion in male European starlings. Neuro-science, 2009; 159: 962–973.

116. Heimovics SA, Riters LV. ZENK labeling within social behavior brainregions reveals breeding context-dependent patterns of neuralactivity associated with song in male European starlings (Sturnusvulgaris). Behav. Brain Res. 2007; 176: 333–343.

117. Maney DL, Goode CT, Lange HS, Sanford SE, Solomon BL. Estradiolmodulates neural responses to song in a seasonal songbird. J. Comp.Neurol. 2008; 511: 173–186.

118. Ball GF, Balthazart J. Hormonal regulation of brain circuits mediatingmale sexual behavior in birds. Physiol. Behav. 2004; 83: 329–346.

119. Balthazart J, Baillien M, Charlier TD, Cornil CA, Ball GF. The neuro-endocrinology of reproductive behavior in Japanese quail. Domest.Anim. Endocrinol. 2003; 25: 69–82.

120. Saldanha CJ, Tuerk MJ, Kim YH, Fernandes AO, Arnold AP, SchlingerBA. Distribution and regulation of telencephalic aromatase expres-sion in the zebra finch revealed with a specific antibody. J. Comp.Neurol. 2000; 423: 619–630.

121. Schoeniger JS, Aiken N, Hsu E, Blackband SJ. Relaxation-time anddiffusion NMRmicroscopy of single neurons. J. Magn. Reson. B, 1994;103: 261–273.

122. Szafer A, Zhong J, Gore JC. Theoretical model for water diffusion intissues. Magn. Reson. Med. 1995; 33: 697–712.

123. Fusani L, Van’t Hof T, Hutchison JB, Gahr M. Seasonal expression ofandrogen receptors, estrogen receptors, and aromatase in thecanary brain in relation to circulating androgens and estrogens.J. Neurobiol. 2000; 43: 254–268.

124. Riters LV, Alger SJ. Neuroanatomical evidence for indirect connec-tions between the medial preoptic nucleus and the song controlsystem: possible neural substrates for sexually motivated song. CellTissue Res. 2004; 316: 35–44.

125. Alger SJ, Riters LV. Lesions to the medial preoptic nucleus differen-tially affect singing and nest box-directed behaviors within andoutside of the breeding season in European starlings (Sturnusvulgaris). Behav. Neurosci. 2006; 120: 1326–1336.

126. Gentner TQ. Neural systems for individual song recognition in adultbirds. Ann. NY Acad. Sci. 2004; 1016: 282–302.

127. Lucas JR, Freeberg TM, Long GR, Krishnan A. Seasonal variation inavian auditory evoked responses to tones: a comparative analysis ofCarolina chickadees, tufted titmice, and white-breasted nuthatches.J. Comp. Physiol. A, Neuroethol. Sens. Neural Behav. Physiol. 2007;193: 201–215.

128. Terleph TA, Lu K, Vicario DS. Response properties of the auditorytelencephalon in songbirds change with recent experience andseason. PLoS ONE, 2008; 3: e2854.

129. Hultcrantz M, Simonoska R, Stenberg AE. Estrogen and hearing: asummary of recent investigations. Acta Otolaryngol. 2006; 126:10–14.

130. Maney DL, Cho E, Goode CT. Estrogen-dependent selectivity ofgenomic responses to birdsong. Eur. J. Neurosci. 2006; 23:1523–1529.

131. Calhoun S, Hulse SH, Braaten RF, Page SC, Nelson RJ. Responsivenessto conspecific and alien song by canaries (Serinus-canaria) andEuropean starlings (Sturnus-vulgaris) as a function of photoperiod.J. Comp. Psychol. 1993; 107: 235–241.

132. Dancause N, Barbay S, Frost SB, Plautz EJ, Chen D, Zoubina EV, StoweAM, Nudo RJ. Extensive cortical rewiring after brain injury. J. Neuro-sci. 2005; 25: 10 167–10 179.

133. Hihara S, Notoya T, Tanaka M, Ichinose S, Ojima H, Obayashi S, Fujii N,Iriki A. Extension of corticocortical afferents into the anterior bank ofthe intraparietal sulcus by tool-use training in adult monkeys.Neuropsychologia, 2006; 44: 2636–2646.

134. Draganski B, May A. Training-induced structural changes in the adulthuman brain. Behav. Brain Res. 2008; 192: 137–142.

NMR Biomed. (2010) Copyright � 2010 John Wiley & Sons, Ltd. www.interscience.wiley.com/journal/nbm

DTI OF BRAIN PLASTICITY IN SONGBIRDS

11