Brain Struct Funct DOI 10.1007/s00429-013-0700-x
ORIGINAL ARTICLE
Anterior–posterior dissociation of the default mode network in dogs Sreenath P. Kyathanahally • Hao Jia • Oleg M. Pustovyy • Paul Waggoner • Ronald Beyers • John Schumacher • Jay Barrett • Edward E. Morrison • Nouha Salibi • Thomas S. Denney • Vitaly J. Vodyanoy • Gopikrishna Deshpande
Received: 28 July 2013 / Accepted: 26 December 2013 Ó Springer-Verlag Berlin Heidelberg 2014
Abstract The default mode network (DMN) in humans has been extensively studied using seed-based correlation analysis (SCA) and independent component analysis (ICA). While DMN has been observed in monkeys as well, there are conflicting reports on whether they exist in rodents. Dogs are higher mammals than rodents, but cognitively not as advanced as monkeys and humans. Therefore, they are an interesting species in the evolutionary hierarchy for probing the comparative functions of the DMN across species. In this study, we sought to know whether the DMN, and consequently its functions such as self-referential processing, are exclusive to humans/monkeys or can we also observe the DMN in animals such as dogs. To address this issue, resting state functional MRI data from the brains of lightly sedated dogs and unconstrained and fully awake dogs were acquired, and ICA and SCA were performed for identifying the DMN. Since Electronic supplementary material The online version of this article (doi:10.1007/s00429-013-0700-x) contains supplementary material, which is available to authorized users. S. P. Kyathanahally H. Jia R. Beyers N. Salibi T. S. Denney G. Deshpande (&) AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA e-mail: [email protected] O. M. Pustovyy E. E. Morrison V. J. Vodyanoy Department of Anatomy, Physiology and Pharmacology, Auburn University, Auburn, AL, USA
anesthesia can alter resting state networks, confirming our results in awake dogs was essential. Awake dog imaging was accomplished by training the dogs to keep their head still using reinforcement behavioral adaptation techniques. We found that the anterior (such as anterior cingulate and medial frontal) and posterior regions (such as posterior cingulate) of the DMN were dissociated in both awake and anesthetized dogs. Keywords Dog Resting state Awake animal imaging Rest animal imaging Functional MRI Default mode network (DMN) Independent component analysis
Introduction Resting state functional magnetic resonance imaging (fMRI) studies have revealed correlated spontaneous low frequency (\0.1 Hz) blood oxygenation level-dependent (BOLD) fluctuations in anatomically distinct regions called J. Barrett College of Veterinary Medicine, Auburn University, Auburn, AL, USA N. Salibi MR R&D, Siemens Healthcare, Malvern, PA, USA T. S. Denney G. Deshpande Department of Psychology, Auburn University, Auburn, AL, USA
P. Waggoner Canine Detection Research Institute, Auburn University, Auburn, AL, USA J. Schumacher Department of Clinical Sciences, Auburn University, Auburn, AL, USA
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‘‘resting state networks’’ (RSNs) in humans (Biswal et al. 1995; Fox and Raichle 2007; Greicius et al. 2003; Gusnard and Raichle 2001; Beckmann et al. 2005; Damoiseaux et al. 2006; Van den Heuvel et al. 2008a, b, Cordes et al. 2000). Resting state studies have gained importance since then they were introduced by Biswal et al. (1995). In resting state fMRI (RS-fMRI), the subjects merely lie awake and still inside the scanner with their eyes closed or open and do not perform any cognitive task. This paradigm is especially useful among clinical population who cannot perform cognitive tasks and also it unburdens the experimental design (Auer 2008; Bokde et al. 2006; Castellanos et al. 2008; Greicius et al. 2007; Greicius et al. 2004; Rombouts and Scheltens 2005; Tian et al. 2006; Whalley et al. 2005). Neurological or psychiatric diseases have been shown to be associated with the alterations in the RSNs (Fornito and Bullmore 2010). Owing to these advantages, RS-fMRI studies are gaining importance. Default mode network (DMN), consisting of precuneus, medial frontal, inferior parietal cortical regions and medial temporal lobe (Van den Heuvel and Hulshoff 2010), is one of the RSNs that has been studied with high interest. DMN is robustly seen in the resting state studies (Damoiseaux et al. 2008; Fox and Raichle 2007; Greicius et al. 2003; Gusnard and Raichle 2001). DMN is active during rest and becomes less active during most external tasks that require attention (Fox and Raichle 2007; Greicius 2008a; Raichle et al. 2001). DMN is linked to the process of human cognition and self-referential processing (Greicius 2008a; Gusnard and Raichle 2001; Buckner et al. 2008). Given the role of the DMN in self-referential processing and consciousness, previous studies have investigated the existence of the DMN in many species, so understanding the biological and evolutionary role of the DMN is important. The DMN has been shown to exist in humans and monkeys (Mantini et al. 2011), but there has been no resting state study in dogs. Although resting state networks have been shown to exist in rodents, some studies have not identified a DMN (Becerra et al. 2011; Zhang et al. 2010; Liang et al. 2011), while others have found either a DMN (Lu et al. 2011) or a DMN-like network (Upadhyay et al. 2011). Dogs are higher mammals than rodents, but cognitively not as advanced as monkeys and humans. Therefore, they are an interesting species in the evolutionary hierarchy for probing the biological and evolutionary role of the DMN. In this study, we sought to know whether DMN and its functions, such as consciousness and self-referential processing, are exclusive in humans/monkey or do we also observe in animals such as dogs. To address this issue, resting state functional MRI data from the brains of anesthetized dogs and awake dogs were acquired and analyzed
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Fig. 1 An awake dog positioned inside the scanner with its head inserted inside the human knee coil
using seed-based correlation analysis and data-driven independent component analysis (ICA). In general, performing fMRI on animals requires them to be anesthetized so that head movement artifacts can be minimized. Past studies have shown that network connectivity decreases with the increase in anesthesia level (Lu et al. 2007; Wang et al. 2011; Greicius et al. 2008b) and possibly degrades the reliability of the networks found in anesthetized animals. Therefore, we performed functional MR imaging using awake, unconstrained and fully conscious dogs as well. This was achieved using dogs trained to position themselves in the scanner and hold motionless for the duration of the scan (Berns et al. 2012). Resting state studies carried out using seed-based analysis need a priori definition of a seed region in the brain which can bias results (Fox and Raichle 2007). On the other hand, ICA does not need a priori information of seed regions (Beckmann et al. 2005); however, ICA is limited by other assumptions such as the number of components in the model (Cole et al. 2010). Other studies have reported that both ICA and seed based method yields the same result (Seeley et al. 2007; Bluhm et al. 2008; Long et al. 2008; Ma et al. 2007). Therefore, we first applied ICA on resting state fMRI data obtained from both anesthetized and awake dogs, and to cross verify those results, we also applied the seed-based correlation analysis with a seed placed at posterior cingulate. For determining the consistent and most reproducible independent components across dogs, we applied the generalized Ranking and Averaging Independent Component Analysis by Reproducibility (gRAICAR) algorithm (Yang et al. 2012) which gives the components that are consistent and reproducible. Our results indicate that the anterior and posterior parts of the DMN are dissociated in dogs.
Brain Struct Funct Fig. 2 Step 1 and step 2 of the proposed spatial normalization procedure
Methods
Data acquisition
All methods and experiments were approved by the Auburn University Institutional Animal Care and Use Committee. The experiment included six Labrador retriever dogs that were recruited from the Auburn University Canine Detection Research Institute with ages in the range 12–60 months. Data acquisition included 89 MRI scans of 5 anesthetized dogs and 74 MRI scans of 4 awake dogs. The 5 anesthetized dogs were scanned 13, 22 19, 17 and 18 times, respectively, and the 4 awake dogs were scanned for 19, 22 17 and 16 times, respectively. A human knee coil was used as a head coil for imaging the dog’s brain in a clinical 3 Tesla (T) MRI scanner. For anesthetized dog imaging, dogs were sedated and lightly anesthetized with intramuscularly administered xylazine (2.2 mg/kg) and ketamine HCL (11 mg/kg,), respectively. No drugs were administered for awake dog imaging. The awake dogs were trained with positive reinforcement behavior shaping procedures to enter the scanner to the required position, insert their heads inside a human knee coil, and remain motionless for the time required for imaging (Fig. 1; also see attached video for illustration).
All MR imaging was performed on a 3 T clinical scanner (Siemens AG, Erlangen, Germany). For each dog, threedimensional structural images were acquired using a T1weighted, magnetization-prepared rapid gradient echo (MPRAGE) (Brant-Zawadzki et al. 1992) sequence with parameters: repetition time (TR) = 1,550 ms, echo time (TE) = 2.64 ms, flip angle (FA) = 9 deg, in-plane matrix = 192 9 192, number of 1.0 mm thick slices = 104, voxel size = 0.79 9 0.79 9 1.0 mm3, for overall volume of view = 152 9 152 9 140 mm3. Each functional image set scan acquired 200 temporal T2*-weighted image sets of the entire brain using a single-shot gradientrecalled echo-planar imaging (EPI) sequence (Butts et al. 1994). An optimal TE = 29 ms was used for maximum sensitivity to blood oxygen level-dependent (BOLD) contrast, and other parameters included: TR = 1,000 ms, FA = 90 deg, in-plane matrix = 64 9 64, number of 3 mm thick slices = 14, voxel size = 3.0 9 3.0 9 3.0 mm3, for overall volume of view = 192 9 192 9 90 mm3.
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Brain Struct Funct Fig. 3 Six motion parameters calculated relative to the first functional slice by realignment procedure. a Mean time series of 6 affine parameters for anesthetized dogs; b standard deviation time series of 6 affine parameters for anesthetized dogs; c sample time series of 6 affine parameters for the worst behaved dog at anesthetized state; d sample time series of 6 affine parameters for the best behaved dog at anesthetized state; e mean time series of 6 affine parameters for awake dogs; f standard deviation time series of 6 affine parameters for awake dogs; g sample time series of 6 affine parameters for the worst behaved dog at awake state; h sample time series of 6 affine parameters for the best behaved dog at awake state
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Pre-processing The Pre-processing was carried out using SPM (Statistical Parametric Mapping), DPARSF (Data Processing Assistant for Resting State fMRI), and REST (Resting State fMRI Data Analysis Toolkit) toolboxes and the dog brain mask was created using Brain Suite toolbox. (Friston et al. 1995; Chao-Gan and Yu-Feng et al. 2010; Song et al. 2011; Shattuck and Leahy 2002). The pre-processing steps are explained below. Slice-timing correction While acquiring the fMRI data, instead of all slices being collected at once at each TR, it is collected at different time instants during the length of TR. To correct the slice acquisition delays between individual slices, temporal data interpolation was performed using SPM8 toolbox (Friston et al. 1995) such that the slices appear to have been collected at the same time. Realignment Movement during scanning may affect the quality of the data by causing spurious changes in the signal intensity over the scans and also misalign the contiguous brain image slices. Therefore, we applied volume realignment based on a rigid-body model of the head and brain (Friston et al. 1996) in the least square sense, by calculating 6 motion parameters (three translations and three rotations). Spatial normalization Spatial normalization is used to convert the functional images acquired from different subjects with different brain sizes to a standard template such that analyzing the subjects as a group becomes feasible (Ashburner and Friston 1999). Standard templates such as EPI-MNI template for humans and monkeys are derived from hundreds of subjects, whereas a recently reported standard template for dogs (Datta et al. 2012) is derived from \ 10 dogs. Since the dogs’ brain sizes vary a lot according to the dog breed, we cannot use this template for dogs since it does not capture the entire spectrum of head size variability. Therefore, we adopted a two-step spatial normalization such that there is a proper co-registration across the dogs’ functional images after spatial normalization. A flow chart illustrating steps 1 and 2 of our proposed spatial normalization procedure is shown in Fig. 2, respectively. This twostep approach is based on two assumptions. First, we assume that the co-registration between the functional and anatomical images from the same session has higher accuracy. Second, we surmise that co-registration across
Fig. 4 Result from the first step of the proposed spatial normalization procedure (a) the functional image (top) is normalized to the anatomical image (bottom), both from the same session. b The images are bounded so as to show only the brain part
subjects from the same imaging modality is more reliable and accurate. Directly normalizing all functional images of different dogs to one anatomical template may create large errors. Therefore, in the first step, we chose a high-quality anatomical image from one of the anesthetized dog runs (since anesthetized dogs do not move, their anatomical images are of superior quality compared to those obtained from awake dogs) and then chose a functional image from the same session (a single session consisted of both functional and anatomical scans). This functional image was then normalized to the chosen anatomical image (Fig. 2) with 8 mm FWHM template smoothing, 4 mm FWHM source image smoothing, 4th order B-spline interpolation
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Brain Struct Funct Fig. 5 Second step of the proposed spatial normalization procedure. a Co-registration between the functional image of one dog (top) and functional image of another dog (bottom) before the 2nd step of spatial normalization. b Co-registration between the functional image of one dog (top) and functional image of another dog (bottom) after 2nd step of spatial normalization. c Co-registration between the bounded functional image of one dog (top) to bounded functional image of another dog (bottom) after 2nd step of spatial normalization
and with a voxel sixe of [2 2 2] mm, to get a good estimate of the spatial transformation. In the second step (Fig. 2), the functional images from all the other sessions were warped onto the space of the normalized functional image obtained from the first step with the following parameters: 4 mm FWHM template smoothing, 4 mm FWHM source image smoothing, 4th order B-spline interpolation and with a voxel sixe of [2 2 2] mm. It can be noted that the template smoothing kernel used in step-1 is wider compared to step2. This is because the structural image had higher resolution than the functional image, and hence we could afford to use a wider smoothing kernel. Since the images involved in the second step were of the same modality, the normalization was relatively accurate. Spatial smoothing The bounded normalized functional images were spatially smoothed by removing high-frequency information using a
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Gaussian filter with 4 mm full width half max kernel to increase the signal-to-noise ratio of an image. Smoothing with a Gaussian kernel also transforms the signal distribution in the image such that the statistical assumptions underlying fMRI analysis are met. Detrending, filtering and brain masking Detrending is a mathematical operation which is used to remove the linear trend from the fMRI data. Previous studies using RS-fMRI have shown that low frequency fluctuations (0.01–0.1 Hz) display the strongest temporal correlations and are most likely to be free from other physiological and measurement-related artifacts (Logothetis et al. 2001). Therefore, we band-pass filtered the fMRI data in the range 0.01–0.1 Hz. A binary mask for the whole brain was then created using Brain Suite (Shattuck and Leahy 2002) toolbox. This mask was subsequently used in connectivity analysis.
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Fig. 6 Separate ICA components showing anterior (a) and posterior (b) regions of the DMN for anesthetized dogs (z [ 2.5). Note that the anterior (a) and posterior (b) regions were dissociated as they appear in different components
Regressing out nuisance covariates The six head motion parameters and the global mean signal contribute to the variance in resting state fMRI fluctuations which are likely of non-neural origin and are considered as nuisance effects. Therefore, they were regressed out of the fMRI voxel time series.
(MDL) criterion for each individual subject. For checking the reproducibility of the independent components across the subjects, we adopted gRAICAR algorithm (Yang et al. 2012) which is a very efficient approach to estimate the reproducible components across the subjects. 39 independent components were found to be reproducible for anesthetized dogs and 42 independent components were found to be reproducible for awake dogs.
Independent component analysis (ICA) Seed-based analysis (SCA) ICA is a blind source separation technique which segregates the observed signal into statistically independent source signals (Brown et al. 2001). The pre-processing steps for the fMRI data before performing ICA included slice-timing correction, realignment, spatial normalization, smoothing, detrending and filtering. Regression of the global mean signal was not performed because physiological noise and other nuisance variables appear as independent components after ICA analysis and can be discarded then. Spatial ICA was carried out on the preprocessed fMRI data using INFOMAX algorithm (Bell and Sejnowski 1995) using GIFT toolbox (Calhoun et al. 2001, 2008). The number of independent components from ICA was estimated using the minimum description length
Seed-based correlation analysis involves the calculation of Pearson’s correlation between a time series obtained from a seed region and every other voxel time series in the brain. The resultant functional connectivity map will have t values for each voxel corresponding to the statistical significance of the covariance of the given voxel time series with the seed time series. The pre-processing steps before performing SCA included slice-timing correction, realignment, normalization, smoothing, detrending, filtering and global mean signal removal. A seed at (0,-41,-10) location with a sphere of 4 mm radius in MNI space was chosen and the functional connectivity map for each subject’s preprocessed fMRI data was found. The resulting
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Fig. 7 Separate ICA components showing anterior (a) and posterior (b) regions of the DMN for awake dogs (z [ 2.5). Note that the anterior (a) and posterior (b) regions were dissociated as they appear in different components
functional connectivity maps were then populated into a sample and a one-sample t test was performed to get the group level seed-based connectivity map.
Likewise, Fig. 3g, h shows affine parameters for the worst and best performing awake dogs, respectively. Spatial normalization
Results Realignment Mean and standard deviation time series of 6 affine parameters for anesthetized dogs and awake dogs were plotted to check whether there is significant head movement in the awake and anesthetized dogs. There was not much head movement in the awake dogs, but the motion for anesthetized dogs was significantly smaller as expected and this is evident from the following figures. Figure 3a, b shows mean and standard deviation time series of affine parameters for anesthetized dogs, respectively, while Fig. 3e, f shows mean and standard deviation time series for awake dogs, respectively. Figure 3c, d shows affine parameters for the worst and best performing dogs, respectively, under anesthesia.
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The proposed two-step approach for the spatial normalization gave very good co-registration across the different dogs’ functional images. The co-registration across different dogs’ functional images is required for carrying out group functional connectivity analysis. Figure 4a, b illustrates the first step in the proposed spatial normalization procedure. A good quality structural image was chosen and then the functional image from the same session as of structural image was normalized to it. Figure 5 illustrates the second step of the proposed spatial normalization procedure. It can be seen that before the second step of normalization, co-registration between the functional images from different dogs was poor (Fig. 5a), whereas very good co-registration can be observed after the second step (Fig. 5b). Once the spatial normalization was done, a bounding box was defined to the final structural and functional images, so as to show up only the region of interest (Figs. 4b, 5c).
Brain Struct Funct Fig. 8 Seed-based connectivity map shows the t values for anesthetized dogs thresholded at FDR corrected p \ 0.0001; t [ 4.4. The posterior cingulate seed is shown in the figure by an arrow
Independent component analysis Figures 6 and 7 show the independent components thresholded at Z [ 2.5 for anesthetized and awake dogs. The color of the activation map indicates the magnitude of the activity, with higher being shown in yellow and lower in red. The results indicate that for both awake and anesthetized dogs, the anterior cingulate/medial prefrontal area and posterior cingulate, which are parts of DMN and shown as one component in humans, were dissociated and appeared in different components. Seed-based analysis Even though ICA has the advantage of being a data-driven method and does not require a priori assumptions, the spatially independent components generated with the ICA method are usually more difficult to explain in comparison with those generated with seed-based correlation analysis and require experience to determine whether a given spatial
source is of neuronal origin or an artifact. So, to cross verify the result obtained from ICA, we found the connectivity maps using seed-based correlation analysis. Figures 8 and 9 show the seed-based correlation maps for anesthetized and awake dogs, respectively, with a seed of 4 mm radius in MNI space being placed at (0,-41,-10) location. The color map indicates the t value, with higher being shown in yellow and lower being in red. The withincondition statistical threshold, for anesthetized dog was set at t [ 4.4 which corresponds to corrected p \ 0.0001 and the within-condition statistical threshold, for awake dog was set at t [ 4.5 which corresponds to corrected p \ 0.0001. The results indicate that for both awake and anesthetized dogs, connectivity was observed only around the seed in posterior parietal areas and there was no connectivity between posterior cingulate and anterior regions such as medial prefrontal cortex and anterior cingulate. We directly compared seed-based maps from awake and anesthetized dogs using a two-sample t test. There were no voxels which had statistically greater connectivity with the
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Brain Struct Funct Fig. 9 Seed-based connectivity map shows the t values for awake dogs thresholded at FDR corrected p \ 0.0001; t [ 4.5. The posterior cingulate seed is shown in the figure by an arrow
posterior cingulate seed in awake as compared to anesthetized dogs. On the other hand, many voxels (14,212, to be precise) had significantly (corrected p \ 0.0001) greater connectivity with the posterior cingulate seed in anesthetized as compared to awake dogs as shown in Fig. 10. Most of such voxels were in the posterior regions of the brain.
Discussion Connectivity maps from the seed-based analysis and ICA for both awake and anesthetized dogs showed that, unlike in humans and monkeys, dogs have localized networks in the anterior and posterior parts. In humans and monkeys, the anterior and posterior parts would be correlated, forming one distributed network which is known as DMN. On the other hand, in dogs, the anterior and posterior parts of the DMN seem to be dissociated. Even though the above conclusion was supported by results from both awake and anesthetized dogs, there were
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other differences that were found between the two. Specifically, anesthetized dogs had many voxels in the posterior regions of the brain which showed higher connectivity with the posterior cingulate seed as compared to awake dogs. This increase in connectivity in anesthetized dogs can be attributed to the fact that when the dogs are sedated, there is significant reduction in discriminable neuronal firing patterns which makes the neural activity less informative. Stamatakis et al. (2010) suggest that the increase in connectivity could be the BOLD correlate of the reduced neural activity caused by sedation. Our results seem to replicate the findings by Stamatakis et al. (2010). It is noteworthy that voxels in the anterior regions of the dog brain which had significantly greater connectivity with the posterior cingulate seed in anesthetized as compared to awake dogs, were not significantly connected to the posterior cingulate seed in either awake or anesthetized dogs when considered individually. This implies that the anterior–posterior dissociation of the DMN was evident in both awake and anesthetized states, but the amount of dissociation varied.
Brain Struct Funct Fig. 10 A map showing the voxels in the dog brain which had significantly (FDR corrected p \ 0.0001; t [ 4.28) greater connectivity with the posterior cingulate seed in anesthetized as compared to awake dogs
In humans, the brain’s DMN is more active in passive settings and during tasks when the brain is not involved in direct attention to the external stimuli. The functions of the DMN can be broadly categorized by two hypotheses, one is ‘‘The sentinel hypothesis’’ (Buckner et al. 2008), where the DMN is thought to be playing the role of monitoring the external environment (Gusnard and Raichle 2001; Gilbert and Wilson 2007), and the other one is ‘‘The internal mentation hypothesis’’ which says that DMN is involved in internal mentation such as theory of mind, envisioning the future, and autobiographical remembering (Buckner et al. 2008). The DMN is considered to be made up of many subsystems (brain regions) which have specialized functions. These subsystems converge into three main hubs in posterior cingulate cortex, anterior cingulate/medial prefrontal cortex and inferior parietal lobule. Each of the subsystems and hubs have their specialized functions. The anterior cingulate cortex is associated with emotional and social processing (Wager et al. 2003; Phan et al. 2002;
Mitchell et al. 2002). The posterior cingulate is involved in self-reflection, recollection and prospection (Johnson et al. 2002; Buckner and Carroll 2007). The posterior cingulate is also implicated in processing of simple emotions (Neafsey et al. 1993; George et al. 1995, 1996; Mayberg et al. 1999), but the role of anterior cingulate and posterior cingulate in the emotion processing is different, even though both are activated during the emotion tasks (Vogt et al. 2006). It has been proposed that the emotional content is stored in the subregions of the anterior cingulate and when an emotional consequence is demanded, the posterior cingulate provides the code for relevant information from sensory systems to evaluate the emotional content. Therefore, the posterior cingulate is associated with retrieval of episodic memories but is not directly a part of the emotion system (Vogt et al. 2006; Lou et al. 2004; Lundstrom et al. 2005). The subsystem consisting of dorsomedial prefrontal cortex has been shown to be involved in self-referential processing (Northoff et al. 2006). Taken together with
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reports attributing similar functions to the posterior cingulate and inferior parietal regions (Harrison et al. 2008), it is reasonable to expect that the temporal correlations between these regions serve the purpose of supporting those functions. It is evident from the above discussion that within the DMN there are at least two different subsystems in the anterior and posterior parts of the brain which interact together to process any information. For example, the present or upcoming events are anticipated by the brain in anterior parts such as dorsomedial prefrontal cortex with the help of analogies from the previous experience which is stored in the hippocampus and accessed by dorsomedial prefrontal cortex through posterior cingulate (Deshpande et al. 2011; Gilbert and Wilson 2007). Thus, the correlation between the hubs plays an important role and the alteration of these hubs may be associated with brain diseases, loss of consciousness and generally lower level of information processing. For example, a study on epilepsy patients reports that the alteration in the DMN activity is associated with loss of consciousness (Danielson et al. 2011). In another study it has been shown that children have significantly weaker functional connectivity between the anterior medial prefrontal and posterior cingulate regions of the DMN and children showed lesser DMN activity compared to adults (Supekar et al. 2010) suggesting that anterior– posterior connectivity is critical for supporting higher level cognitive processing in adult humans (Fransson et al. 2007). The correlation across different brain networks including DMN gets disrupted with advanced aging and reduced cognitive abilities (Andrews-Hanna et al. 2007). These results suggest that anterior–posterior dissociation in the DMN of dogs may suggest lower level of cognitive processing in dogs as compared to humans. Future studies must explore whether calibrating and quantifying the anterior– posterior association in DMNs can be used as a quantitative metric for the level of cognitive processing. The organization of multiple functional resting networks shifts from a ‘‘local’’ anatomical emphasis in children to a more ‘‘distributed’’ architecture in young adults (Fair et al. 2009). Since human development mimics the evolutionary hierarchy, there is reason to believe that dogs represent the part of evolution where DMN is localized, which then becomes more distributed in humans and monkeys. DMN in humans is directly related to cognitive abilities (Seeley et al. 2007), attention (Hampson et al. 2006; Leech et al. 2011), and memory retrieval (Buckner et al. 2008; Gilbert and Wilson 2007). The impairments in attention are associated with the disruption in the activation of DMN (Bonnelle et al. 2011) and the working memory functions depend on the DMN functional connectivity pattern. The involvement of the cingulate regions within the DMN can be used as a working memory efficiency predictor (Esposito
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et al. 2009). In addition to being a predictor of behavioral performance, the DMN in humans has also been shown to be a predictor of individual traits (Lei et al. 2013; Knyazev 2012; Wei et al. 2011) and personality. These human data raise the possibility of using characterization of the DMN in dogs as a predictor of behavioral performance and individual traits. Potentially, this principle can be used to identify dogs with traits and capabilities that are useful to humans.
Conclusions In this study, we performed functional imaging of anesthetized and awake dogs to investigate the DMN in dog brains. Imaging of awake dogs, achieved through reinforcementbased behavioral adaptation techniques, was performed to rule out anesthesia-related effects. We found that, unlike humans and monkeys, the anterior and posterior regions of the DMN were dissociated in both awake and anesthetized dogs. These results provide insights into the comparative role of the DMN across species. In addition, they raise the possibility of using DMN-based characterization for choosing dogs with traits and abilities suitable for training them for tasks in which they are utilized by humans. Acknowledgments We thank Yang Zhi from National Institutes of Health for providing gRAICAR code and assisting with its analysis. The authors acknowledge financial support for this work from Auburn University Intramural Level-3 research grant from the Office of the Vice President for Research, Auburn University. This work was also supported by The Defense Advanced Research Projects Agency (government contract/grant number W911QX-13-C-0123). The views, opinions, and/or findings contained in this article are those of the authors and should not be interpreted as representing the official views or policies, either expressed or implied, of the Defense Advanced Research Projects Agency, Department of Defense or the United States Government.
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ORIGINAL ARTICLE
Anterior–posterior dissociation of the default mode network in dogs Sreenath P. Kyathanahally • Hao Jia • Oleg M. Pustovyy • Paul Waggoner • Ronald Beyers • John Schumacher • Jay Barrett • Edward E. Morrison • Nouha Salibi • Thomas S. Denney • Vitaly J. Vodyanoy • Gopikrishna Deshpande
Received: 28 July 2013 / Accepted: 26 December 2013 Ó Springer-Verlag Berlin Heidelberg 2014
Abstract The default mode network (DMN) in humans has been extensively studied using seed-based correlation analysis (SCA) and independent component analysis (ICA). While DMN has been observed in monkeys as well, there are conflicting reports on whether they exist in rodents. Dogs are higher mammals than rodents, but cognitively not as advanced as monkeys and humans. Therefore, they are an interesting species in the evolutionary hierarchy for probing the comparative functions of the DMN across species. In this study, we sought to know whether the DMN, and consequently its functions such as self-referential processing, are exclusive to humans/monkeys or can we also observe the DMN in animals such as dogs. To address this issue, resting state functional MRI data from the brains of lightly sedated dogs and unconstrained and fully awake dogs were acquired, and ICA and SCA were performed for identifying the DMN. Since Electronic supplementary material The online version of this article (doi:10.1007/s00429-013-0700-x) contains supplementary material, which is available to authorized users. S. P. Kyathanahally H. Jia R. Beyers N. Salibi T. S. Denney G. Deshpande (&) AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA e-mail: [email protected] O. M. Pustovyy E. E. Morrison V. J. Vodyanoy Department of Anatomy, Physiology and Pharmacology, Auburn University, Auburn, AL, USA
anesthesia can alter resting state networks, confirming our results in awake dogs was essential. Awake dog imaging was accomplished by training the dogs to keep their head still using reinforcement behavioral adaptation techniques. We found that the anterior (such as anterior cingulate and medial frontal) and posterior regions (such as posterior cingulate) of the DMN were dissociated in both awake and anesthetized dogs. Keywords Dog Resting state Awake animal imaging Rest animal imaging Functional MRI Default mode network (DMN) Independent component analysis
Introduction Resting state functional magnetic resonance imaging (fMRI) studies have revealed correlated spontaneous low frequency (\0.1 Hz) blood oxygenation level-dependent (BOLD) fluctuations in anatomically distinct regions called J. Barrett College of Veterinary Medicine, Auburn University, Auburn, AL, USA N. Salibi MR R&D, Siemens Healthcare, Malvern, PA, USA T. S. Denney G. Deshpande Department of Psychology, Auburn University, Auburn, AL, USA
P. Waggoner Canine Detection Research Institute, Auburn University, Auburn, AL, USA J. Schumacher Department of Clinical Sciences, Auburn University, Auburn, AL, USA
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‘‘resting state networks’’ (RSNs) in humans (Biswal et al. 1995; Fox and Raichle 2007; Greicius et al. 2003; Gusnard and Raichle 2001; Beckmann et al. 2005; Damoiseaux et al. 2006; Van den Heuvel et al. 2008a, b, Cordes et al. 2000). Resting state studies have gained importance since then they were introduced by Biswal et al. (1995). In resting state fMRI (RS-fMRI), the subjects merely lie awake and still inside the scanner with their eyes closed or open and do not perform any cognitive task. This paradigm is especially useful among clinical population who cannot perform cognitive tasks and also it unburdens the experimental design (Auer 2008; Bokde et al. 2006; Castellanos et al. 2008; Greicius et al. 2007; Greicius et al. 2004; Rombouts and Scheltens 2005; Tian et al. 2006; Whalley et al. 2005). Neurological or psychiatric diseases have been shown to be associated with the alterations in the RSNs (Fornito and Bullmore 2010). Owing to these advantages, RS-fMRI studies are gaining importance. Default mode network (DMN), consisting of precuneus, medial frontal, inferior parietal cortical regions and medial temporal lobe (Van den Heuvel and Hulshoff 2010), is one of the RSNs that has been studied with high interest. DMN is robustly seen in the resting state studies (Damoiseaux et al. 2008; Fox and Raichle 2007; Greicius et al. 2003; Gusnard and Raichle 2001). DMN is active during rest and becomes less active during most external tasks that require attention (Fox and Raichle 2007; Greicius 2008a; Raichle et al. 2001). DMN is linked to the process of human cognition and self-referential processing (Greicius 2008a; Gusnard and Raichle 2001; Buckner et al. 2008). Given the role of the DMN in self-referential processing and consciousness, previous studies have investigated the existence of the DMN in many species, so understanding the biological and evolutionary role of the DMN is important. The DMN has been shown to exist in humans and monkeys (Mantini et al. 2011), but there has been no resting state study in dogs. Although resting state networks have been shown to exist in rodents, some studies have not identified a DMN (Becerra et al. 2011; Zhang et al. 2010; Liang et al. 2011), while others have found either a DMN (Lu et al. 2011) or a DMN-like network (Upadhyay et al. 2011). Dogs are higher mammals than rodents, but cognitively not as advanced as monkeys and humans. Therefore, they are an interesting species in the evolutionary hierarchy for probing the biological and evolutionary role of the DMN. In this study, we sought to know whether DMN and its functions, such as consciousness and self-referential processing, are exclusive in humans/monkey or do we also observe in animals such as dogs. To address this issue, resting state functional MRI data from the brains of anesthetized dogs and awake dogs were acquired and analyzed
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Fig. 1 An awake dog positioned inside the scanner with its head inserted inside the human knee coil
using seed-based correlation analysis and data-driven independent component analysis (ICA). In general, performing fMRI on animals requires them to be anesthetized so that head movement artifacts can be minimized. Past studies have shown that network connectivity decreases with the increase in anesthesia level (Lu et al. 2007; Wang et al. 2011; Greicius et al. 2008b) and possibly degrades the reliability of the networks found in anesthetized animals. Therefore, we performed functional MR imaging using awake, unconstrained and fully conscious dogs as well. This was achieved using dogs trained to position themselves in the scanner and hold motionless for the duration of the scan (Berns et al. 2012). Resting state studies carried out using seed-based analysis need a priori definition of a seed region in the brain which can bias results (Fox and Raichle 2007). On the other hand, ICA does not need a priori information of seed regions (Beckmann et al. 2005); however, ICA is limited by other assumptions such as the number of components in the model (Cole et al. 2010). Other studies have reported that both ICA and seed based method yields the same result (Seeley et al. 2007; Bluhm et al. 2008; Long et al. 2008; Ma et al. 2007). Therefore, we first applied ICA on resting state fMRI data obtained from both anesthetized and awake dogs, and to cross verify those results, we also applied the seed-based correlation analysis with a seed placed at posterior cingulate. For determining the consistent and most reproducible independent components across dogs, we applied the generalized Ranking and Averaging Independent Component Analysis by Reproducibility (gRAICAR) algorithm (Yang et al. 2012) which gives the components that are consistent and reproducible. Our results indicate that the anterior and posterior parts of the DMN are dissociated in dogs.
Brain Struct Funct Fig. 2 Step 1 and step 2 of the proposed spatial normalization procedure
Methods
Data acquisition
All methods and experiments were approved by the Auburn University Institutional Animal Care and Use Committee. The experiment included six Labrador retriever dogs that were recruited from the Auburn University Canine Detection Research Institute with ages in the range 12–60 months. Data acquisition included 89 MRI scans of 5 anesthetized dogs and 74 MRI scans of 4 awake dogs. The 5 anesthetized dogs were scanned 13, 22 19, 17 and 18 times, respectively, and the 4 awake dogs were scanned for 19, 22 17 and 16 times, respectively. A human knee coil was used as a head coil for imaging the dog’s brain in a clinical 3 Tesla (T) MRI scanner. For anesthetized dog imaging, dogs were sedated and lightly anesthetized with intramuscularly administered xylazine (2.2 mg/kg) and ketamine HCL (11 mg/kg,), respectively. No drugs were administered for awake dog imaging. The awake dogs were trained with positive reinforcement behavior shaping procedures to enter the scanner to the required position, insert their heads inside a human knee coil, and remain motionless for the time required for imaging (Fig. 1; also see attached video for illustration).
All MR imaging was performed on a 3 T clinical scanner (Siemens AG, Erlangen, Germany). For each dog, threedimensional structural images were acquired using a T1weighted, magnetization-prepared rapid gradient echo (MPRAGE) (Brant-Zawadzki et al. 1992) sequence with parameters: repetition time (TR) = 1,550 ms, echo time (TE) = 2.64 ms, flip angle (FA) = 9 deg, in-plane matrix = 192 9 192, number of 1.0 mm thick slices = 104, voxel size = 0.79 9 0.79 9 1.0 mm3, for overall volume of view = 152 9 152 9 140 mm3. Each functional image set scan acquired 200 temporal T2*-weighted image sets of the entire brain using a single-shot gradientrecalled echo-planar imaging (EPI) sequence (Butts et al. 1994). An optimal TE = 29 ms was used for maximum sensitivity to blood oxygen level-dependent (BOLD) contrast, and other parameters included: TR = 1,000 ms, FA = 90 deg, in-plane matrix = 64 9 64, number of 3 mm thick slices = 14, voxel size = 3.0 9 3.0 9 3.0 mm3, for overall volume of view = 192 9 192 9 90 mm3.
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Brain Struct Funct Fig. 3 Six motion parameters calculated relative to the first functional slice by realignment procedure. a Mean time series of 6 affine parameters for anesthetized dogs; b standard deviation time series of 6 affine parameters for anesthetized dogs; c sample time series of 6 affine parameters for the worst behaved dog at anesthetized state; d sample time series of 6 affine parameters for the best behaved dog at anesthetized state; e mean time series of 6 affine parameters for awake dogs; f standard deviation time series of 6 affine parameters for awake dogs; g sample time series of 6 affine parameters for the worst behaved dog at awake state; h sample time series of 6 affine parameters for the best behaved dog at awake state
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Pre-processing The Pre-processing was carried out using SPM (Statistical Parametric Mapping), DPARSF (Data Processing Assistant for Resting State fMRI), and REST (Resting State fMRI Data Analysis Toolkit) toolboxes and the dog brain mask was created using Brain Suite toolbox. (Friston et al. 1995; Chao-Gan and Yu-Feng et al. 2010; Song et al. 2011; Shattuck and Leahy 2002). The pre-processing steps are explained below. Slice-timing correction While acquiring the fMRI data, instead of all slices being collected at once at each TR, it is collected at different time instants during the length of TR. To correct the slice acquisition delays between individual slices, temporal data interpolation was performed using SPM8 toolbox (Friston et al. 1995) such that the slices appear to have been collected at the same time. Realignment Movement during scanning may affect the quality of the data by causing spurious changes in the signal intensity over the scans and also misalign the contiguous brain image slices. Therefore, we applied volume realignment based on a rigid-body model of the head and brain (Friston et al. 1996) in the least square sense, by calculating 6 motion parameters (three translations and three rotations). Spatial normalization Spatial normalization is used to convert the functional images acquired from different subjects with different brain sizes to a standard template such that analyzing the subjects as a group becomes feasible (Ashburner and Friston 1999). Standard templates such as EPI-MNI template for humans and monkeys are derived from hundreds of subjects, whereas a recently reported standard template for dogs (Datta et al. 2012) is derived from \ 10 dogs. Since the dogs’ brain sizes vary a lot according to the dog breed, we cannot use this template for dogs since it does not capture the entire spectrum of head size variability. Therefore, we adopted a two-step spatial normalization such that there is a proper co-registration across the dogs’ functional images after spatial normalization. A flow chart illustrating steps 1 and 2 of our proposed spatial normalization procedure is shown in Fig. 2, respectively. This twostep approach is based on two assumptions. First, we assume that the co-registration between the functional and anatomical images from the same session has higher accuracy. Second, we surmise that co-registration across
Fig. 4 Result from the first step of the proposed spatial normalization procedure (a) the functional image (top) is normalized to the anatomical image (bottom), both from the same session. b The images are bounded so as to show only the brain part
subjects from the same imaging modality is more reliable and accurate. Directly normalizing all functional images of different dogs to one anatomical template may create large errors. Therefore, in the first step, we chose a high-quality anatomical image from one of the anesthetized dog runs (since anesthetized dogs do not move, their anatomical images are of superior quality compared to those obtained from awake dogs) and then chose a functional image from the same session (a single session consisted of both functional and anatomical scans). This functional image was then normalized to the chosen anatomical image (Fig. 2) with 8 mm FWHM template smoothing, 4 mm FWHM source image smoothing, 4th order B-spline interpolation
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Brain Struct Funct Fig. 5 Second step of the proposed spatial normalization procedure. a Co-registration between the functional image of one dog (top) and functional image of another dog (bottom) before the 2nd step of spatial normalization. b Co-registration between the functional image of one dog (top) and functional image of another dog (bottom) after 2nd step of spatial normalization. c Co-registration between the bounded functional image of one dog (top) to bounded functional image of another dog (bottom) after 2nd step of spatial normalization
and with a voxel sixe of [2 2 2] mm, to get a good estimate of the spatial transformation. In the second step (Fig. 2), the functional images from all the other sessions were warped onto the space of the normalized functional image obtained from the first step with the following parameters: 4 mm FWHM template smoothing, 4 mm FWHM source image smoothing, 4th order B-spline interpolation and with a voxel sixe of [2 2 2] mm. It can be noted that the template smoothing kernel used in step-1 is wider compared to step2. This is because the structural image had higher resolution than the functional image, and hence we could afford to use a wider smoothing kernel. Since the images involved in the second step were of the same modality, the normalization was relatively accurate. Spatial smoothing The bounded normalized functional images were spatially smoothed by removing high-frequency information using a
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Gaussian filter with 4 mm full width half max kernel to increase the signal-to-noise ratio of an image. Smoothing with a Gaussian kernel also transforms the signal distribution in the image such that the statistical assumptions underlying fMRI analysis are met. Detrending, filtering and brain masking Detrending is a mathematical operation which is used to remove the linear trend from the fMRI data. Previous studies using RS-fMRI have shown that low frequency fluctuations (0.01–0.1 Hz) display the strongest temporal correlations and are most likely to be free from other physiological and measurement-related artifacts (Logothetis et al. 2001). Therefore, we band-pass filtered the fMRI data in the range 0.01–0.1 Hz. A binary mask for the whole brain was then created using Brain Suite (Shattuck and Leahy 2002) toolbox. This mask was subsequently used in connectivity analysis.
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Fig. 6 Separate ICA components showing anterior (a) and posterior (b) regions of the DMN for anesthetized dogs (z [ 2.5). Note that the anterior (a) and posterior (b) regions were dissociated as they appear in different components
Regressing out nuisance covariates The six head motion parameters and the global mean signal contribute to the variance in resting state fMRI fluctuations which are likely of non-neural origin and are considered as nuisance effects. Therefore, they were regressed out of the fMRI voxel time series.
(MDL) criterion for each individual subject. For checking the reproducibility of the independent components across the subjects, we adopted gRAICAR algorithm (Yang et al. 2012) which is a very efficient approach to estimate the reproducible components across the subjects. 39 independent components were found to be reproducible for anesthetized dogs and 42 independent components were found to be reproducible for awake dogs.
Independent component analysis (ICA) Seed-based analysis (SCA) ICA is a blind source separation technique which segregates the observed signal into statistically independent source signals (Brown et al. 2001). The pre-processing steps for the fMRI data before performing ICA included slice-timing correction, realignment, spatial normalization, smoothing, detrending and filtering. Regression of the global mean signal was not performed because physiological noise and other nuisance variables appear as independent components after ICA analysis and can be discarded then. Spatial ICA was carried out on the preprocessed fMRI data using INFOMAX algorithm (Bell and Sejnowski 1995) using GIFT toolbox (Calhoun et al. 2001, 2008). The number of independent components from ICA was estimated using the minimum description length
Seed-based correlation analysis involves the calculation of Pearson’s correlation between a time series obtained from a seed region and every other voxel time series in the brain. The resultant functional connectivity map will have t values for each voxel corresponding to the statistical significance of the covariance of the given voxel time series with the seed time series. The pre-processing steps before performing SCA included slice-timing correction, realignment, normalization, smoothing, detrending, filtering and global mean signal removal. A seed at (0,-41,-10) location with a sphere of 4 mm radius in MNI space was chosen and the functional connectivity map for each subject’s preprocessed fMRI data was found. The resulting
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Fig. 7 Separate ICA components showing anterior (a) and posterior (b) regions of the DMN for awake dogs (z [ 2.5). Note that the anterior (a) and posterior (b) regions were dissociated as they appear in different components
functional connectivity maps were then populated into a sample and a one-sample t test was performed to get the group level seed-based connectivity map.
Likewise, Fig. 3g, h shows affine parameters for the worst and best performing awake dogs, respectively. Spatial normalization
Results Realignment Mean and standard deviation time series of 6 affine parameters for anesthetized dogs and awake dogs were plotted to check whether there is significant head movement in the awake and anesthetized dogs. There was not much head movement in the awake dogs, but the motion for anesthetized dogs was significantly smaller as expected and this is evident from the following figures. Figure 3a, b shows mean and standard deviation time series of affine parameters for anesthetized dogs, respectively, while Fig. 3e, f shows mean and standard deviation time series for awake dogs, respectively. Figure 3c, d shows affine parameters for the worst and best performing dogs, respectively, under anesthesia.
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The proposed two-step approach for the spatial normalization gave very good co-registration across the different dogs’ functional images. The co-registration across different dogs’ functional images is required for carrying out group functional connectivity analysis. Figure 4a, b illustrates the first step in the proposed spatial normalization procedure. A good quality structural image was chosen and then the functional image from the same session as of structural image was normalized to it. Figure 5 illustrates the second step of the proposed spatial normalization procedure. It can be seen that before the second step of normalization, co-registration between the functional images from different dogs was poor (Fig. 5a), whereas very good co-registration can be observed after the second step (Fig. 5b). Once the spatial normalization was done, a bounding box was defined to the final structural and functional images, so as to show up only the region of interest (Figs. 4b, 5c).
Brain Struct Funct Fig. 8 Seed-based connectivity map shows the t values for anesthetized dogs thresholded at FDR corrected p \ 0.0001; t [ 4.4. The posterior cingulate seed is shown in the figure by an arrow
Independent component analysis Figures 6 and 7 show the independent components thresholded at Z [ 2.5 for anesthetized and awake dogs. The color of the activation map indicates the magnitude of the activity, with higher being shown in yellow and lower in red. The results indicate that for both awake and anesthetized dogs, the anterior cingulate/medial prefrontal area and posterior cingulate, which are parts of DMN and shown as one component in humans, were dissociated and appeared in different components. Seed-based analysis Even though ICA has the advantage of being a data-driven method and does not require a priori assumptions, the spatially independent components generated with the ICA method are usually more difficult to explain in comparison with those generated with seed-based correlation analysis and require experience to determine whether a given spatial
source is of neuronal origin or an artifact. So, to cross verify the result obtained from ICA, we found the connectivity maps using seed-based correlation analysis. Figures 8 and 9 show the seed-based correlation maps for anesthetized and awake dogs, respectively, with a seed of 4 mm radius in MNI space being placed at (0,-41,-10) location. The color map indicates the t value, with higher being shown in yellow and lower being in red. The withincondition statistical threshold, for anesthetized dog was set at t [ 4.4 which corresponds to corrected p \ 0.0001 and the within-condition statistical threshold, for awake dog was set at t [ 4.5 which corresponds to corrected p \ 0.0001. The results indicate that for both awake and anesthetized dogs, connectivity was observed only around the seed in posterior parietal areas and there was no connectivity between posterior cingulate and anterior regions such as medial prefrontal cortex and anterior cingulate. We directly compared seed-based maps from awake and anesthetized dogs using a two-sample t test. There were no voxels which had statistically greater connectivity with the
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Brain Struct Funct Fig. 9 Seed-based connectivity map shows the t values for awake dogs thresholded at FDR corrected p \ 0.0001; t [ 4.5. The posterior cingulate seed is shown in the figure by an arrow
posterior cingulate seed in awake as compared to anesthetized dogs. On the other hand, many voxels (14,212, to be precise) had significantly (corrected p \ 0.0001) greater connectivity with the posterior cingulate seed in anesthetized as compared to awake dogs as shown in Fig. 10. Most of such voxels were in the posterior regions of the brain.
Discussion Connectivity maps from the seed-based analysis and ICA for both awake and anesthetized dogs showed that, unlike in humans and monkeys, dogs have localized networks in the anterior and posterior parts. In humans and monkeys, the anterior and posterior parts would be correlated, forming one distributed network which is known as DMN. On the other hand, in dogs, the anterior and posterior parts of the DMN seem to be dissociated. Even though the above conclusion was supported by results from both awake and anesthetized dogs, there were
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other differences that were found between the two. Specifically, anesthetized dogs had many voxels in the posterior regions of the brain which showed higher connectivity with the posterior cingulate seed as compared to awake dogs. This increase in connectivity in anesthetized dogs can be attributed to the fact that when the dogs are sedated, there is significant reduction in discriminable neuronal firing patterns which makes the neural activity less informative. Stamatakis et al. (2010) suggest that the increase in connectivity could be the BOLD correlate of the reduced neural activity caused by sedation. Our results seem to replicate the findings by Stamatakis et al. (2010). It is noteworthy that voxels in the anterior regions of the dog brain which had significantly greater connectivity with the posterior cingulate seed in anesthetized as compared to awake dogs, were not significantly connected to the posterior cingulate seed in either awake or anesthetized dogs when considered individually. This implies that the anterior–posterior dissociation of the DMN was evident in both awake and anesthetized states, but the amount of dissociation varied.
Brain Struct Funct Fig. 10 A map showing the voxels in the dog brain which had significantly (FDR corrected p \ 0.0001; t [ 4.28) greater connectivity with the posterior cingulate seed in anesthetized as compared to awake dogs
In humans, the brain’s DMN is more active in passive settings and during tasks when the brain is not involved in direct attention to the external stimuli. The functions of the DMN can be broadly categorized by two hypotheses, one is ‘‘The sentinel hypothesis’’ (Buckner et al. 2008), where the DMN is thought to be playing the role of monitoring the external environment (Gusnard and Raichle 2001; Gilbert and Wilson 2007), and the other one is ‘‘The internal mentation hypothesis’’ which says that DMN is involved in internal mentation such as theory of mind, envisioning the future, and autobiographical remembering (Buckner et al. 2008). The DMN is considered to be made up of many subsystems (brain regions) which have specialized functions. These subsystems converge into three main hubs in posterior cingulate cortex, anterior cingulate/medial prefrontal cortex and inferior parietal lobule. Each of the subsystems and hubs have their specialized functions. The anterior cingulate cortex is associated with emotional and social processing (Wager et al. 2003; Phan et al. 2002;
Mitchell et al. 2002). The posterior cingulate is involved in self-reflection, recollection and prospection (Johnson et al. 2002; Buckner and Carroll 2007). The posterior cingulate is also implicated in processing of simple emotions (Neafsey et al. 1993; George et al. 1995, 1996; Mayberg et al. 1999), but the role of anterior cingulate and posterior cingulate in the emotion processing is different, even though both are activated during the emotion tasks (Vogt et al. 2006). It has been proposed that the emotional content is stored in the subregions of the anterior cingulate and when an emotional consequence is demanded, the posterior cingulate provides the code for relevant information from sensory systems to evaluate the emotional content. Therefore, the posterior cingulate is associated with retrieval of episodic memories but is not directly a part of the emotion system (Vogt et al. 2006; Lou et al. 2004; Lundstrom et al. 2005). The subsystem consisting of dorsomedial prefrontal cortex has been shown to be involved in self-referential processing (Northoff et al. 2006). Taken together with
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reports attributing similar functions to the posterior cingulate and inferior parietal regions (Harrison et al. 2008), it is reasonable to expect that the temporal correlations between these regions serve the purpose of supporting those functions. It is evident from the above discussion that within the DMN there are at least two different subsystems in the anterior and posterior parts of the brain which interact together to process any information. For example, the present or upcoming events are anticipated by the brain in anterior parts such as dorsomedial prefrontal cortex with the help of analogies from the previous experience which is stored in the hippocampus and accessed by dorsomedial prefrontal cortex through posterior cingulate (Deshpande et al. 2011; Gilbert and Wilson 2007). Thus, the correlation between the hubs plays an important role and the alteration of these hubs may be associated with brain diseases, loss of consciousness and generally lower level of information processing. For example, a study on epilepsy patients reports that the alteration in the DMN activity is associated with loss of consciousness (Danielson et al. 2011). In another study it has been shown that children have significantly weaker functional connectivity between the anterior medial prefrontal and posterior cingulate regions of the DMN and children showed lesser DMN activity compared to adults (Supekar et al. 2010) suggesting that anterior– posterior connectivity is critical for supporting higher level cognitive processing in adult humans (Fransson et al. 2007). The correlation across different brain networks including DMN gets disrupted with advanced aging and reduced cognitive abilities (Andrews-Hanna et al. 2007). These results suggest that anterior–posterior dissociation in the DMN of dogs may suggest lower level of cognitive processing in dogs as compared to humans. Future studies must explore whether calibrating and quantifying the anterior– posterior association in DMNs can be used as a quantitative metric for the level of cognitive processing. The organization of multiple functional resting networks shifts from a ‘‘local’’ anatomical emphasis in children to a more ‘‘distributed’’ architecture in young adults (Fair et al. 2009). Since human development mimics the evolutionary hierarchy, there is reason to believe that dogs represent the part of evolution where DMN is localized, which then becomes more distributed in humans and monkeys. DMN in humans is directly related to cognitive abilities (Seeley et al. 2007), attention (Hampson et al. 2006; Leech et al. 2011), and memory retrieval (Buckner et al. 2008; Gilbert and Wilson 2007). The impairments in attention are associated with the disruption in the activation of DMN (Bonnelle et al. 2011) and the working memory functions depend on the DMN functional connectivity pattern. The involvement of the cingulate regions within the DMN can be used as a working memory efficiency predictor (Esposito
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et al. 2009). In addition to being a predictor of behavioral performance, the DMN in humans has also been shown to be a predictor of individual traits (Lei et al. 2013; Knyazev 2012; Wei et al. 2011) and personality. These human data raise the possibility of using characterization of the DMN in dogs as a predictor of behavioral performance and individual traits. Potentially, this principle can be used to identify dogs with traits and capabilities that are useful to humans.
Conclusions In this study, we performed functional imaging of anesthetized and awake dogs to investigate the DMN in dog brains. Imaging of awake dogs, achieved through reinforcementbased behavioral adaptation techniques, was performed to rule out anesthesia-related effects. We found that, unlike humans and monkeys, the anterior and posterior regions of the DMN were dissociated in both awake and anesthetized dogs. These results provide insights into the comparative role of the DMN across species. In addition, they raise the possibility of using DMN-based characterization for choosing dogs with traits and abilities suitable for training them for tasks in which they are utilized by humans. Acknowledgments We thank Yang Zhi from National Institutes of Health for providing gRAICAR code and assisting with its analysis. The authors acknowledge financial support for this work from Auburn University Intramural Level-3 research grant from the Office of the Vice President for Research, Auburn University. This work was also supported by The Defense Advanced Research Projects Agency (government contract/grant number W911QX-13-C-0123). The views, opinions, and/or findings contained in this article are those of the authors and should not be interpreted as representing the official views or policies, either expressed or implied, of the Defense Advanced Research Projects Agency, Department of Defense or the United States Government.
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