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Structural and functional neuroimaging phenotypes in dysbindin mutant mice
Evan Lutkenhoff a,1, Katherine H. Karlsgodt b,c,⁎,1, Boris Gutman d, Jason L. Stein a,d, Paul M. Thompson d,Tyrone D. Cannon b,c,d,f,2, J. David Jentsch a,b,e,2
a Interdisciplinary Neuroscience Program, University of California, Los Angeles, USAb Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, USAc Staglin Center for Cognitive Neuroscience, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, USAd Laboratory of Neuro Imaging, Department of Neurology, University of California, Los Angeles, USAe Department of Psychology, University of California, Los Angeles, USAf Department of Human Genetics, University of California, Los Angeles, USA
Abbreviations: MRI, Magnetic resonance imaging; Cschizophrenia; PFC, Prefrontal cortex; wt, Wild-type;magnetic resonance imaging; BBB, Blood–brain barrier;etry; PCR, Polymerase chain reaction; MSME, Multi-sview; MDT, Minimum deformation target; KL-MI, Kulltion; ROI, Region of interest; ERP, event related potenti⁎ Corresponding author at: Department of Psychiatr
UCLA, Franz Hall Box 951563, Los Angeles, CA 90095, UE-mail address: [email protected] (K.H. Karlsgodt).
1 These authors contributed equally to this work.2 These senior authors contributed equally to this wo
Schizophrenia is a highly heritable psychiatric disorder that is associated with a number of structural andfunctional neurophenotypes. DTNBP1, the gene encoding dysbindin-1, is a promising candidate gene forschizophrenia. Use of a mouse model carrying a large genomic deletion exclusively within the dysbindingene permits a direct investigation of the gene in isolation. Here, we use manganese-enhanced magnetic res-onance imaging (MEMRI) to explore the regional alterations in brain structure and function caused by loss ofthe gene encoding dysbindin-1. We report novel findings that uniquely inform our understanding of the re-lationship of dysbindin-1 to known schizophrenia phenotypes. First, in mutant mice, analysis of the rate ofmanganese uptake into the brain over a 24-hour period, putatively indexing basal cellular activity, revealeddifferences in dopamine rich brain regions, as well as in CA1 and dentate subregions of the hippocampus for-mation. Finally, novel tensor-based morphometry techniques were applied to the mouse MRI data, providingevidence for structural volume deficits in cortical regions, subiculum and dentate gyrus, and the striatum ofdysbindin mutant mice. The affected cortical regions were primarily localized to the sensory cortices in par-ticular the auditory cortex. This work represents the first application of manganese-enhanced small animalimaging to a mouse model of schizophrenia endophenotypes, and a novel combination of functional andstructural measures. It revealed both hypothesized and novel structural and functional neural alterations re-lated to dysbindin-1.
DTNBP1, the gene encoding dysbindin-1, is a candidate gene forschizophrenia (SZ), which was identified through linkage (Schwabet al., 2003; Straub et al., 2002) and association studies (for compre-hensive review, see (Riley et al., 2009; Talbot, 2009)). DTNBP1 varia-tion in humans has been associated with cognitive impairments(Burdick et al., 2006; Donohoe et al., 2007; Zinkstok et al., 2007), as
A1, Cornu Ammonis area; SZ,MEMRI, Manganese-enhancedTBM, Tensor-based morphom-lice multi-echo; FOV, Field ofback–Leibler mutual informa-al.y and Biobehavioral Sciences,SA.
rk.
rights reserved.
have other chromosome 6p loci (Hallmayer et al., 2005; Posthumaet al., 2005). Dysbindin-1 protein levels are reduced 60–90% in multi-ple brain areas of patients with schizophrenia, including the auditorycortices (Talbot et al., 2011), dorsolateral prefrontal cortex (Tanget al., 2009), and the hippocampal formation (Talbot et al., 2004;Talbot et al., 2011). These percentages are far higher than are the fre-quencies of occurrence of DTNBP1 risk haplotypes or SNPs found inthe population, indicating that the degree of dysbindin-1 proteinreductions in schizophrenia is separate from known risk haplotypes.As such, these findings emphasize reduced dysbindin-1 protein ex-pression in schizophrenia pathophysiology, and that effect is mim-icked in the sandy (dys−/−) mouse model.
Mice carrying a null mutation in the mouse dtnbp1 gene (dys−/−mice) do not express dysbindin-1 protein (Li et al., 2003). Dysbindin-1 is expressed throughout the brain, including in the axon terminalsof glutamatergic pyramidal neurons (Talbot, 2009). Chen et al. dem-onstrated that a lack of dysbindin-1 protein leads to larger butfewer glutamatergic vesicles, narrower synaptic clefts, and thickerpost-synaptic densities within CA1 asymmetrical synapses, which
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support their electrophysiological observations of slow dynamics andreduced probability of glutamate release (Chen et al., 2008). Stimulus-induced glutamate release in cultures of cortical neurons is enhancedwhen dysbindin-1 is over-expressed by those cells and decreasedwhen dysbindin-1 is knocked down in them (Numakawa et al., 2004).Accordingly, there is interest in relating this finding to the hypothesisthat alterations in glutamatergic transmission are the “unifyingmechanism” by which otherwise diverse insults converge to producethe pathophysiology characteristic of SZ. In addition, there is evidencethat down regulation of dysbindin-1 in mice increases surface expres-sion of dopamine D2 receptors, but not dopamine D1 receptors in corti-cal neurons (Ji et al., 2009). The effect of increased D2 receptors on thecell surface is increased sensitivity to excitability changes in response toD2 agonists, both in Layer V interneurons and Layers II/III pyramidalneurons (Ji et al., 2009; Papaleo et al., 2012).
It remains difficult to bridge the high-precision, mechanistic findingsin mice with the data available in human subjects. One solution is to usemagnetic resonance imaging (MRI); MRI measures are non-invasive, ap-plicable to both humans and small animals, and have been previouslyshown to index genetic SZ liability in humans (Cannon et al., 1994). Weemployed neuroimaging in dys−/− and wild-type (wt) mice to achievetwo goals. First, we sought to develop an in vivo technique to assesschanges previously observable only ex vivo or in vitro. This would enablelongitudinal or multimodal evaluations of intact murine systems. Byusing MRI, we can come closer to collecting parallel assessments in
Fig. 1. Mn2+ uptake: Regions where wild-type mice had greater Mn2+ uptake over 24 h (hinificance, cool colors less). Rostral axial view of significant regions including dentate, CA1, anand CA1 (middle row). Coronal view of significant regions including the bilateral habenula (and significant regions (in red) where wild-types had greater activity than mutants (bottom
humans and animal models, strengthening the translational validity ofstudies. Second, capitalizing on the enhanced gray/white contrast gener-ated by the manganese, we used tensor-based morphometry (TBM) todetect anatomical local volume differences between groups.
Manganese-enhanced MRI (MEMRI) has become an increasinglypopular technique for imaging small animals at high magnetic fieldstrengths (e.g. 7 T magnetic field strength in this study) (Pautler et al.,1998). Intracellular accumulation of Mn2+ ions results in shorteningof thewater proton T1 relaxation time (Kang andGore, 1984). After sys-temic administration,Mn2+ ions enter the brain via the blood brain bar-rier (BBB) and blood–cerebrospinalfluid barrier (Rabin et al., 1993). TheMn2+ ions enter neurons primarily through voltage-gated calciumchannels in an activity-dependent manner (Lin and Koretsky, 1997).Regardless of the administration route, the contrast enhancement pri-marily reflects the functional neural processing of specific brain systems(Watanabe et al., 2004); consequently, the rate of Mn2+ uptake is a re-liablemarker of normal tissue function (Silva et al., 2004). It may also bea proxy for levels of cellular activity (Angenstein et al., 2007), in partbecause MEMRI maps are based on calcium influx rather than second-ary measures such as cerebral hemodynamics (Jaksch et al., 2008).Structurally, MEMRI has been employed in a rat model of schizophrenia(Chin et al., 2011). Functionally, thismethod has been applied to hypox-ic events and brain ischemia (Chan et al., 2008;Wideroe et al., 2009), vi-sual processing (Bissig and Berkowitz, 2009), auditory processing (Yu etal., 2005; Yu et al., 2008), and plasticity (Van der Linden et al., 2004), but
gher basal cellular activity) than mutant littermates (warm colors indicate greatest sig-d CA2/3 (top row). Axial view of significant regions including lateral habenula, dentatebottom left). Three-dimensional volume rendering of MDT, hippocampus ROI (in blue),right).
Ventral thalamus R 114 3.37Lateroposterior nucleus ofthalamus
L 169 3.50L 50 2.96
CA2/CA3 R 121 2.49Strata oriens plus pyramidal cells of thehippocampus
R 120 2.96L 92 2.51
Fimbria of hippocampus R 63 2.41
Functional analysis, list of regions from Fig. 1 where wild-types had significantly great-er Mn2+ uptake over 24 h (higher basal cellular activity) than mutant littermates. Re-gions with multiple significant clusters have a number indicating the number of voxelsfor each cluster.
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not yet to genetic mouse models of schizophrenia. Further, this is thefirst application of this technique focused on baseline, rather than stim-ulus driven, neural function.
We employ two novel approaches to analyzingmanganese-enhancedimages. First, we used the rate of Mn2+ uptake to perform a functionalanalysis of activity at rest (‘baseline’ activation) in dys−/− mice and lit-termate wt controls. This whole-brain analysis tested whether wt miceshowed greater levels of activation, representing basal cellular activity,than null mutant mice. Second, capitalizing on the enhanced gray/whitecontrast generated by the Mn2+, we used tensor-based morphometry(TBM) to detect anatomical local volume differences between groups.Overall, both the functional and structural analyseswere sensitive to sub-tle differences consistent with those previously observed with humanswith SZ. These techniques may offer a unique window into neuralassessments of intact genetic models.
Methods
Animals
Adult (>60 days of age) male and female mice carrying aspontaneously-occurring null mutation in the gene encoding thedysbindin-1 protein (Li et al., 2003) were used in these studies. Theline had been backcrossed onto the C57Bl/6J background for at least 6generations (Jackson Laboratories, Bar Harbor, Maine). Animals werebred on site from heterozygous parents; genotypes were determined
by PCR (Jentsch et al., 2009). Twenty-eight total mice were imaged;14 dys−/− (142.28±79.84 days old, 3 females/11 males) and 14 wtmice (164.42±84.45 days old, 2 females/12males). There were no dif-ferences between genotypes in either age or sex distribution. All proto-cols were approved by the Chancellor's Animal Research Committee atthe University of California at Los Angeles.
Manganese administration
Subjects were given intraperitoneal injections of anhydrous man-ganese chloride (MnCl2) (Sigma Aldrich, St. Louis, MO) dissolved insterile 0.9% saline 24-h prior to scanning. Previous MEMRI experi-ments have been safely performed using systemic administration of175 mg/kg MnCl2 in mice (Lee and Park, 2005). We safely used adose of 62.5 mg/kg (496.66 μmol/kg) that provided excellent regionalcontrast, while avoiding effects of Mn2+ toxicity; side effects primar-ily consisted of reduced motor activity and reactivity to sensory stim-uli, lasting for approximately 3 h. Contrast enhancement reaches itsfinal pattern 24-hours after MnCl2 administration, with a subsequentdegradation of the signal over the following 2–3 weeks (Silva et al.,2004).
Anesthesia
After brief transport to the UCLA Brain Mapping Center, anesthesiawas induced using 3–5% isoflurane mixed with air (3 L/min); it wasmaintained using 1–2% isoflurane in oxygen (0.5 L/min). Mice wereplaced in a species-specific holding apparatus equipped with earbars, a stabilizing bite bar, a nose cone to allow a continuous flow ofanesthetizing gas, and warm airflow to maintain a body temperatureof 37 °C. Subjects breathed spontaneously throughout the entire ex-periment. Temperature and respiration were monitored throughoutthe scan.
Imaging
Mice were imaged on a 7 T Bruker MRI BioSpec 70/30 USR 30 cmbore scanner using a 35 mm volume coil for transmit and receive. AT1-weighted multi-slice multi-echo (MSME) scan was used: FOV2.2 cm, 35 axial slices of 0.35 mm, Repetition time (TR)=831.1,Echo Time (TE)=10.7 ms, flip angle=180, 20 averages, 256×128matrix zero filled to 256×256 resulting in 0.086×0.086×0.35 mmvoxel resolution. Images were acquired in 36 min per subject with re-spiratory gating. All images were reconstructed using Paravision(PV4.0).
Image post-processing
Images were converted from native Bruker format to ANALYZEformat using pvconv (M. Brett; http://pvconv.sourceforge.net/) thenmanually skull-stripped using MultiTracer (Woods, 2003). Afterskull stripping, intensity inhomogeneities were corrected using N3correction (Sled et al., 1998). For statistical analysis, to create an un-biased average from the group of images, a minimum deformationtarget (MDT) representative of the entire group was created(Kochunov et al., 2001; MacKenzie-Graham et al., 2006). All imageswere aligned to a representative individual with a 12-parameter fullaffine transformation using Alignlinear (AIR5.2.5) (Woods et al.,1998) and the least squares with intensity rescaling cost function.The resulting transformations were used to calculate a commonspace for all the images with Define Common AIR (AIR5.2.5). The im-ages were then re-sliced into this common space and averaged to-gether to produce the MDT. Each of the images was then linearlyaligned to the MDT with a 12-parameter full-affine transformationusing Alignlinear and the least squares intensity rescaling cost func-tion followed by a 5th-order polynomial warp using Align_warp
Fig. 2. Mn2+ uptake: Regions where mutant mice had greater Mn2+ uptake over 24 h (higher basal cellular activity) than wild-type littermates (warm colors indicate greatest sig-nificance, cool colors less). Axial view of significant regions including a fimbrial region encompassing both the septofimbrial nucleus and fimbria (top row). More caudal axial viewof significant regions including oriens hippocampus and paraventricular nucleus of the thalamus (middle row). Coronal view of significant regions including the paraventricularnucleus of the thalamus (bottom left). Three-dimensional volume rendering of MDT, hippocampus ROI (in blue), and significant regions (in yellow) where mutants had greater ac-tivity than wild-types (bottom right).
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(AIR5.2.5). These images were then re-sliced and averaged to producethe final minimum deformation target. The MDT had an isotropic0.0525 mm (52.5 μm) resolution. All automated image processingwas performed within the LONI Pipeline Processing Environment(Vinogradov et al., 2003).
Functional analysis: MEMRI
Due to the potential for differences in globalMn2+ accumulation be-tween subjects based on factors such as injection variability, the imageintensity was normalized. The pituitary gland is outside the BBB and itssignal shows strong dose-dependent contrast enhancement. Therefore,it was used as an internal reference for any systemic effects, a techniquealso employed by other groups using MEMRI in mice (Chuang andKoretsky, 2009; Eschenko et al., 2010). In our sample, pituitary contrastenhancement between genotypes differed by 3.3% (p=0.517) indicat-ing no systematic differences in manganese uptake in the pituitary. Aregion of interest was manually traced around the pituitary gland ineach subject and, each brain's image intensity at every voxel was divid-ed by its own pituitary gland mean intensity. Then each intensity-normalized image was spatially normalized across subjects bynonlinearly aligning all individual images to the MDT for groupanalyses.
Tensor-based morphometry and 3D Jacobian maps
TBM examines the spatial derivatives of deformationmaps calculat-ed from the nonlinear registration of brain images to a common ana-tomical template in order to infer 3D patterns of statistical differencesin brain volume or shape (Chung et al., 2008). TBM treats the deforma-tion maps as discrete vector fields in order to take the gradients at eachelement in the vector field to make a Jacobian matrix, in which eachelement is a tensor describing the relative positions of the neighboringelements (Ashburner and Friston, 2000). The field obtained by takingthe determinants at each point of the Jacobian matrix gives a map ofthe structure volumes relative to those of a reference image, alsoknown as the Jacobian map (Freeborough and Fox, 1998). Statisticalparametric maps of these determinant fields can then be used to com-pare the anatomy of groups of subjects (Ashburner and Friston, 2000).
Individual Jacobian maps illustrating local expansion or compres-sion were created to estimate 3D patterns of structural brain variationacross genotypes. The nonlinear registration algorithm employed usesa mutual information cost function and the symmetrized Kullback–Leibler (KL-MI) distance as a regularizing term (Leow et al., 2007;Yanovsky et al., 2009). The registration parameters of sigma=6 andlambda=2 were defined to control the Jacobian field smoothness andconstrain the range of Jacobians, respectively (Yanovsky et al., 2009).The KL-MI algorithm is incorporated into a multi-scale bundle that
Table 2List of regions from Fig. 2 where mutants had significantly greater Mn2+ uptake over24 h than wild-type controls.
Region Hemisphere Number of significantvoxels in each cluster
T-statistic at peakvoxel in cluster
Paraventricular nucleusof thalamus
L+R 1710 4.54
Lateral septal nucleus R 397 3.25L 114 3.18
Lateroposterior nucleusof thalamus
L 58 2.62
Dorsal hippocampalcommissure
L+R 390 3.54
Subiculum R 302 2.37R 259 4.30L 62 2.55
Amygdala L 214 3.70Cortex, auditory R 82 2.70Caudate-putamencomplex
R 64 2.49
Pretectal area R 55 3.02L 62 2.67
Cingulate gyrus R 59 3.13Interpeduncularnucleus
L+R 56 2.54
Oriens hippocampus L 250 2.76L 51 3.10
Fimbrial region L 51 2.16
Functional analysis, regions from Fig. 2 where mutants had significantly greater Mn2+
uptake over 24 h than wild-type controls. Regions with multiple significant clustershave a number indicating the number of voxels for each cluster.
Fig. 3. Structural analysis:Mapof themean percent difference,which illustrates on averagewheretypemean regional volume. Rostral axial viewof significant regions including auditory cortex, andand temporal association cortex (middle row). Coronal view of significant regions including bilateMDT, hippocampus ROI (in blue, for reference only), and significant regions (in yellow) where m
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utilizes multiple grid sizes to capture volumetric differences at a globaland local scale. It starts with a small working grid of 32×34×34, then64×34×68, followed by 128×68×126 and finishes with a fullresolution grid of 170×170×170. This approach allows for larger initialdeformationswhere necessary, allowing the finest (full) resolution stepto be entirely devoted to local deformations, which ensures precisealignment across groups. The input to the KL-MI algorithm is a set ofeach animal's image and the MDT. All images were first scaled to havethe same intensity distributions. All images were then linearlyregistered to the MDT with 9 degrees of freedom using a leastsquares cost function within the AIR program. The multiscale KL-MI algorithm was performed within the LONI Pipeline ProcessingEnvironment (Vinogradov et al., 2003). Color-codedmaps of the Jaco-bian determinants were created to illustrate regions of volume expansionor contraction. These maps of tissue change were spatially normalizedacross subjects by nonlinearly aligning all individual Jacobian maps tothe MDT, for regional comparisons and group statistical analyses.
Statistical analyses
To examine the differences between genotypes in Mn2+ uptake, thepituitary-normalized images were compared with a voxel-by-voxel t-test across the whole brain using the 3dttest function in the AFNIneuroimaging software package (Cox, 1996). To examine the genotypedifferences in structural brain morphometry, the Jacobian maps werecompared with a voxel-wise whole brain three-dimensional t-test(AFNI, 3dttest tool). To control for multiple comparisons in both analy-ses, a Monte Carlo simulation was completed using the Alphasim pro-gram in AFNI to determine a threshold and cluster size combination
themutant brains are significantly smaller than thewild-type brains, as a percent of thewild-lateral habenula (top row). Axial viewof significant regions including bilateral auditory, visual,ral auditory cortex and dentate gyrus, (bottom left). Three-dimensional volume rendering ofutants had smaller relative volumes compared with wild-types (bottom right).
Cortex, prelimbic R 196 2.10Rostral corpuscallosum
R 167 3.09
CA1 L 134 2.30Anteriorcommissure
R 54 2.19L 121 2.58
Cortex, insula L 119 2.65Cortex, visual L 70 2.40
List of regions from Fig. 3, structural analysis. Regions where mutant brains weresignificantly smaller than the wild-type brains. Regions with multiple significant clus-ters have a number indicating the number of voxels for each cluster.
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with a low false positive discovery rate. We used a threshold of pb .005and a cluster size of 49 voxels, which yielded a false positive rate ofαb .01. Statistically significant regions were coded using a color mapand displayed on the minimum deformation template.
Results
Functional analyses: manganese uptake
In the analysis ofMn2+uptake over 24 h during rest in the home cage,therewere a number of regions inwhichwtmice showed greaterMEMRIsignal, a findingwhich is theoretically consistent with greater basal cellu-lar activity, than dys−/− mice (see Fig. 1, Table 1). In particular, wtshowed greater baseline activation in bilateral hippocampal subregions,specifically in the CA1 and dentate. These regions have previouslyshown alterations in dysbindin-1 expression in patients with SZ (Talbotet al., 2004). There were also differences in the interpeduncular nucleus,pretectal regions, and thalamus. In addition, dys−/− mice showedlower activity in some key regions associated with dopamine function,namely the substantia nigra, a key component of the nigrostriatal dopa-mine pathway, and the habenula. Several studies have provided evidencethat lateral habenula neurons functionally, if indirectly, inhibit dopamineneurons; namely, electrical stimulation of the lateral habenula inhibits ac-tivity of dopamine neurons in the substantia nigra pars compacta (SNc)and ventral tegmental area (VTA) (Christoph et al., 1986; Ji andShepard, 2007;Matsumoto andHikosaka, 2007), and habenula lesions in-crease dopamine release in the cortex and striatum (Nishikawa et al.,1986).
There were also a few regions in which dys−/−mice showed greaterMn2+ uptake, indexing greater cellular activity than wt. Areas of hyper-activation in mutants were limited to a few sparsely distributed regions
(see Fig. 2, Table 2), with the largest area of activation in theparaventricular nucleus of the thalamus.
Structural analysis: maps of regional volumetric differences
We found evidence for relative volume differences, both expansionand contraction, representing local tissue gain or loss, in dys−/− micerelative to wt littermates. Differences were apparent in both bilateralcortical and subcortical structures, including regions associated withsensory input and processing. The regions showing the most evidencefor local volume decreases in the dys−/− mice were cortical regions,in particular, a large region of the auditory cortex, insular cortex,piriform cortex, cingulate cortex, and a temporal region encompassingtemporal cortex, temporal association cortex, perirhinal cortex, anddorsolateral entorhinal cortex. In addition, there were smaller regionalchanges in the caudate–putamen complex, and lateral habenula.While most differences were observed in gray matter, there was alsosignificant region of contraction in the anterior commissure (seeFig. 3, Table 3).
There were relatively fewer areas in which dys−/− showed volumeincreases relative to wt. Significant regions included a fimbrial regionencompassing both the septofimbrial nucleus and fimbria, the rightCA3, and a hypothalamic region encompassing the mamillary complexand posterior commissural area (see Fig. 4, Table 4). All structuralchanges represent relative differences in relation to the commonatlas, rather than absolute quantitative decreases.
From the aforementioned structural and functional changes, severalnetwork patterns emerged. First, substantial volume changes were ob-served in the auditory cortex and this was also the only cortical regionin which we measured functional differences. To accompany these cor-tical changes were functional changes in the medial geniculate nucleusof the thalamus, the thalamic relay nucleus associated with auditoryprocessing. Second, while therewere no volumetric decreases in hippo-campal regions in dys−/−mice, there were functional changes across anumber of components of the hippocampal formation. Moreover, thesechanges were more prominent in dorsal hippocampal regions com-pared with ventral hippocampus. Finally, there were a number ofregions (pretectal area, fimbrial area, stratum oriens of the hippocam-pus, interpeduncular nucleus, laterodorsal nucleus of the thalamus)that showed both increases and decreases in activity. In addition, theanterior commissure shows both increases and decreases in volume.This may reflect different effects of subfields of these structures andwarrants further study into the net effect of dysbindin-1 reductionson these regions.
Discussion
Our results suggest that MEMRI is sensitive to structural and func-tional changes in mice lacking dysbindin-1 potentially contributing toschizophrenia-related dopaminergic and hippocampal dysfunction,as well as cerebrocortical volume changes. Thus, MEMRI has potentialas a powerful tool for translational investigations in small animalmodels of neurological and psychiatric disorders. Moreover, usingthis method to detect structural and basal activity levels revealedsubstantia nigra/VTA and habenular dysfunction in dys−/− mice,which suggests a novel means by which dysbindin-1 can affect dopa-mine transmission. We also show novel evidence of CA1 and dentategyrus dysfunction in intact dys−/− mice, as well as the first clear ev-idence that dysbindin-1 is associated with cortical volume decreases.
The MEMRI functional analysis reported here reveals changes inbasal activation in intact dys−/− mice, consistent with the well-known dopamine dysfunction in humans with SZ (Abi-Darghamand Moore, 2003; Guillin et al., 2007; Howes and Kapur, 2009), aswell as dopaminergic changes previously observed in dys−/− miceusing more invasive measures (Papaleo and Weinberger, 2011). Thenovel finding of changes in the substantia nigra may be particularly
Fig. 4. Structural analysis: Map of the mean percent difference, which illustrates on average where the mutant brains are significantly larger than the wild-type brains, as a percentof the wild-type mean regional volume. Axial view of significant regions including hypothalamic regions, potentially including the mamillary bodies (top row). More rostral axialview of significant regions including a fimbrial region encompassing both the septofimbrial nucleus and fimbria (middle row). Coronal view of significant regions including a fim-brial region encompassing both the septofimbrial nucleus and fimbria (bottom left). Three-dimensional volume rendering of MDT, hippocampus ROI (in blue, for reference only),and significant regions (in red) where mutants had larger relative volumes compared with wild-types (bottom right).
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important, due to the substantia nigra's role in dopamine release instriatal and cortical brain regions involved in the pathophysiology ofSZ. In mice, dysbindin-1 protein is present in the substantia nigra(Benson et al., 2001), and in humans, dysbindin-1 mRNA is promi-nently expressed in the substantia nigra and basal ganglia, areas oforigination and termination of dopamine neurons, suggesting thatdysbindin-1 dysfunction may affect regions with prominent dopa-mine neurotransmission (Weickert et al., 2004). However, thisstudy presents the first in vivo evidence showing that this pathwayis affected by a dysbindin-1 mutation and may indicate that this isan important target for future mechanistic studies.
The habenula has been proposed as a key regulator of dopamine,serotonin, and acetylcholine neurotransmission (Hikosaka, 2010). Ithas been implicated in reward function (Matsumoto and Hikosaka,2007) and may have a role in feedback processing deficits in SZ(Shepard et al., 2006). Interestingly, the lateral habenula inhibits do-pamine transmission, if indirectly (via the rostromedial tegmentalnucleus) (Balcita-Pedicino et al., 2011; Ji and Shepard, 2007). Unliketraditional functional MRI measures that rely on hemodynamic indi-ces, as a secondary measure of neural function, MEMRI is advanta-geous as it directly indexes cellular function: both inhibitory andexcitatory firing may cause an increase in Mn2+ uptake. This maybe of particular interest given that some structures showed both in-creases and decreases in MEMRI signal.
In addition to the dopaminergic findings, altered activity wasfound in non-dopaminergic areas of the hippocampal formation pre-viously found abnormal in dys−/−mice and schizophrenia cases. Forinstance, changes in dysbindin-1 in the dentate have been observedin humans with SZ (Talbot et al., 2004). There is also evidence forthe presence of dysbindin-1 in CA1, CA2, and CA3 (Talbot et al.,2006, 2004; Taneichi-Kuroda et al., 2009; Weickert et al., 2008).From a network perspective, the volume reductions seen in auditory,somatosensory, and visual cortices, aswell as in the gateway for sensoryinput to the hippocampal formation (i.e., the dorsolateral entorhinalcortex) may help explain the reduced activity seen in the major feed-forward pathway of the hippocampal pathway and its fimbrial output,namely the multisynaptic pathway from the dentate gyrus to CA3 toCA2 and CA1 to the fimbria. Dysbindin-1 reductions occur throughoutmuch of this pathway in schizophrenia (Talbot et al., 2004). The ob-served hippocampal changes may in part explain the relationship be-tween DTNBP1 genotype and cognition (Bhardwaj et al., 2009; Feng etal., 2008; Hashimoto et al., 2010; Karlsgodt et al., 2011; Kircher et al.,2009). This work is also consistent with the large body of literatureshowing evidence of deficits in hippocampal-dependent declarativememory in patients with SZ (Saykin et al., 1991), as well as volumetric(van Erp et al., 2004) and functional changes (Heckers et al., 1998;Lutkenhoff et al., 2010; Schobel et al., 2009). In addition, our findingsof alterations in primary sensory regions are consistent with evidence
Table 4List of regions from Fig. 4, structural analysis. Regions where mutant brains were signif-icantly larger than the wild-type brains.
Region Hemisphere Number ofsignificant voxels ineach cluster
T-statistic atpeak voxel incluster
CA3 R 717 3.27R 352 2.75
Posteriorcommissural area
R 500 2.59
Hypothalamus (mamillarybodies)
R 325 2.42R 245 2.17R 71 2.51L 81 2.11
Superior colliculus R 268 2.83Fimbrial region L+R 239 2.86Anteriorcommissure
L 172 2.37
Nucleus accumbens R 81 2.35
List of regions from Fig. 4, structural analysis. Regions where mutant brains weresignificantly larger than the wild-type brains. Regions with multiple significant clustershave a number indicating the number of voxels for each cluster.
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linking dysbindin-1 to visual perception in children (Mechelli et al.,2010), and individuals with schizophrenia (Donohoe et al., 2008).Genetic variation in the DTNBP1 locus has also been associated withfunctional changes in frontal regions measured using event related po-tentials (ERP) in individuals with (Fallgatter et al., 2010) and without(Fallgatter et al., 2006) schizophrenia.
Structural changes related to dysbindin-1 have not been thor-oughly investigated using MRI. Of the currently published studies,volume decreases (Donohoe et al., 2010), volume increases (Narr etal., 2009), no volume differences (Dutt et al., 2009), and alterationsin cortical thickness (Cerasa et al., 2011) have been reported. Howev-er, there are well-documented structural changes associated with SZ,including volumetric decreases in gray matter, white matter, and hip-pocampus, as well as regionally decreased cortical thickness (Cannonet al., 1998b; Karlsgodt et al., 2008; Narr et al., 2005; van Erp et al.,2004). Our study represents the first morphological assessment ofneuroanatomical structure in dys−/− mice and the first applicationof tensor based morphometry in any mouse model of schizophrenia.An important strength of MRI based assessment of shape and volumeis that observations occur in vivo, without the deformations associat-ed with brain extraction or potential shrinkage from fixation tech-niques. Using this method, we observed significant changes acrossthe cortex and subcortical regions. Notably, in addition to finding pro-nounced structural changes in the auditory cortex, it was also theonly cortical region to show functional decreases, as did the medialgeniculate nucleus, the auditory thalamic relay nucleus. The observedeffects on the auditory cortex are consistent with findings of de-creased dysbindin-1 in the auditory cortex of SZ patients (Talbot etal., 2011), as well as with electrophysiological abnormalities reportedin the cortical areas of dys−/− mice (Carlson et al., 2011a).
A potential limitation of our studies is the use of an animal modelthat does not display the left–right structural abnormalities seen inschizophrenia (Carlson et al., 2011b). Further, previous ex vivo MRIwork in the C57Bl/6J background mouse strain identified several re-gions that had significant left–right asymmetries, with left hemi-sphere structures consistently being larger (Spring et al., 2010).Therefore this study may not be sensitive to finding asymmetricalchanges. Secondly, since SZ is a complex, multi-gene psychiatric dis-order (Cannon et al., 1998a; Cardno and Gottesman, 2000), likely in-volving both numerous common genes of small effect and raregenetic variants with relatively larger effects, it is not feasible to ma-nipulate a single gene and fully reproduce structural changes ob-served in SZ. However, this analysis is focused specifically on the
contribution of dysbindin-1 to neural function and structure indepen-dent of other genes or epigenetic effects. The strength of geneticmouse models is that we have the power to assess genes in isolation,something not possible in humans. Since the genetic variance withinthe C57Bl/6J inbred mouse strain is very small compared with humanstudies, we conclude that the differences identified between the micegenotypes are linked to dysbindin-1 expression. While we focused onfull mutant and wild-type mice for this analysis, in order to make aninitial establishment of the effect of a lack of dysbindin-1 expression,future studies should include wild-type, mutant, and heterozygousmice in order to model the intermediate effects that may better rep-resent the changes present in patients with schizophrenia.
MEMRI is useful for translationalmodels of disease because it providescontrast enhancement in specific brain regions, it can be used to trace ax-onal pathways, and it indirectly assesses regional cellular activity (Silvaand Bock, 2008). However, the mechanisms underlying the contrast en-hancement are not fully understood and it remains possible that differ-ences in axonal transport, BBB permeability, divalent cation channeldistribution or affinity, or choroid transport may influence group differ-ences. A methodological limitation of this study is that the direct impactof dysbindin-1 expression on manganese uptake mechanisms has notbeen empirically studied. However, here, manganese is being used as aproxy for cellular function, based on its well-established utility as an MRcontrast agent, in amanner consistentwith thepreviously published liter-ature. As such, while theremay be some limitations in our understandingof themechanisms ofmanganese uptake, in the context of the larger liter-ature, MEMRI is still able to provide meaningful insights about neuralfunction and the localization of genetic effects. In addition, global perme-ability differences would likely cause widespread activity differencesrather than the observed regionally specific changes in areas implicatedin schizophrenia.
Even thoughMn2+ preferentially accumulates in neurons, Mn2+ ionscan also be found in glial cells (Tiffany-Castiglioni and Qian, 2001). In thisstudy, the anatomical proximity of the choroid plexus to the habenula aswell as paraventricular nucleus of the thalamus in conjunction with thelimited resolution inherent to MRI, makes it difficult to dissociate activa-tion in these regions, and the region labeled as the habenula may alsoinclude some portions of the adjacent choroid plexus. Furthermore,while MEMRI is a powerful tool that can provide information similar tohuman imaging, the technique cannot be directly applied to humansdue to manganese toxicity. Although we were able to acquire very highresolution images, the MRI slice thickness when combined with theinter-slice interval is equivalent to several histologically-based Paxinosatlas slices. Therefore, each figure displays one representative Paxinosatlas slice to serve as a general reference. Future studies seeking higherlevels of anatomic specificity should pursue a combination of histologyand imaging.
In summary, we have employed small animal imaging to revealnovel findings regarding functional and structural effects of thedysbindin-1 model of SZ. Using MEMRI, this study provides the first invivo evidence that dys−/− mice potentially model key features ofschizophrenia, namely cerebrocortical volume reductions, hippocampalformation hypofunction, and dopaminergic dysregulation. In general,this type of study is well-suited to create a bridge between humanand murine studies of psychiatric disease. In the future, there is thepotential for longitudinal studies that can investigate the neu-rodevelopmental aspects of SZ, studies linking baseline dysfunction tobehavioral changes, and studies linking MEMRI findings with changesin other imaging modalities, such as cerebral blood flow and diffusiontensor imaging.
Conflict of interest
The authors, Drs Lutkenhoff, Karlsgodt, Gutman, Stein, Thompson,Cannon and Jentsch, report no conflict of interest.
128 E. Lutkenhoff et al. / NeuroImage 62 (2012) 120–129
Acknowledgments
Thisworkwas funded byRL1MH083269 (to TDC/JDJ), R01HD050735(to PMT), R01 EB008432 (to PMT), R01 EB008281 (to PMT), R01EB007813 (to PMT), and pilot funding from the UCLA Consortium forNeuropsychiatric Phenomics (UL1-DE019580 to KHK). We would alsolike to acknowledge the contributions of Andy Frew, Jeffrey Alger, NeilHarris, Cristina Duffin, Alice Zhang, Allan MacKenzie-Graham, and CoreyJairl in the implementation and analysis of this project and the contribu-tion of Jason Michael Hall to the creation of the figures.
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