Science of the Total Environment 408 (2010) 2600–2607
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Science of the Total Environment
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Human brain activation in response to visual stimulation with rural and urbanscenery pictures: A functional magnetic resonance imaging study
Tae-Hoon Kim a, Gwang-Woo Jeong a,b,⁎, Han-Su Baek a, Gwang-Won Kim a, Thirunavukkarasu Sundaram a,Heoung-Keun Kang b, Seung-Won Lee c, Hyung-Joong Kim d, Jin-Kyu Song e
a Interdisciplinary Program of Biomedical Engineering, Chonnam National University, Gwangju, Republic of Koreab Department of Radiology, Chonnam National University Hospital, Chonnam National University Medical School, # 8 Hackdong, Donggu, Gwangju, 501-757, Republic of Koreac Department of Anatomy, Chonnam National University Medical School, Gwangju, Republic of Koread Impedance Imaging Research Center, Kyung Hee University, Yongin, Republic of Koreae Department of Architectural Engineering, Chonnam National University, Gwangju, Republic of Korea
Article history:Received 16 April 2009Received in revised form 9 February 2010Accepted 15 February 2010Available online 17 March 2010
Keywords:Brain activationRuralUrbanFunctional magnetic resonance imaging(fMRI)
Human brain activation was assessed in terms of eco-friendliness while viewing still photographs depictingrural and urban surrounding environments with the use of a functional magnetic resonance imagingtechnique. A total of 30 subjects who had both rural and urban life experiences participated in this study.In order to explore the common and differential activation maps yielded by viewing two extreme types ofscenery, random effect group analysis was performed with the use of one-sample and two-sample t-tests.Activation of the anterior cingulate gyrus, globus pallidus, putamen and head of the caudate nucleus wasdominant during rural scenery viewing, whereas activation of the hippocampus, parahippocamus andamygdala was dominant during urban scenery viewing (pb0.01). These findings allow better characteriza-tion of neural activation, suggesting an inherent preference towards nature-friendly living. Such a theoreticalacquisition may have an important practical impact in view of potential applications for bio-housing and thedevelopment of environmental psychology-related areas.
With rapid urbanization, the boundary of urban has been ex-panding to the undeveloped regions, and the rural areas arecontinuously diminished (Farmer et al., 2006; Martin and Zürcher,2008). The rapid urbanization caused environmental changes such asincreased traffic, polluted air and water, exhausted local resources,and reduced agricultural land and natural open space (Pronczuk andSurdu, 2008). Especially, these environmental changes threaten thehuman health and quality of life (Pronczuk and Surdu, 2008). Duringthe last of two decades, lots of researches on the living environmentin conjunction with psychology and psychiatry have been performed(Bartels and Zeki, 2004; Edgerton et al., 2007; Farmer et al., 2006;Gregg et al., 2003; Hartig et al., 2003; Hartig and Staats, 2006; Kaplan,1995; Laumann et al., 2003). Even though the rural life has variousinherent advantages (Braun-Fahrländer, 2000; Gregg et al., 2003;Kilpeläinen et al., 2000), most people prefer urban life to rural lifebecause of its advantages related to a modern lifestyle.
Recent studies have shown that the human body might be in-fluenced by physiological or psychological activity in response to thesurrounding environment. Laumann et al. (2003) have measuredheart rate responses before and after viewing a video of either anatural or an urban environment. While viewing the videos, the par-ticipants who viewed nature scenes had a lower heart rate measuredas a difference from baseline as compared to participants who viewedurban scenes. As related to environmental psychology, various re-search studies (Hartig et al., 1989, 1991, 2003; Herzog et al., 2002;Kaplan, 1995; Stokols, 1978) have been published that have exploredthe impact of natural and artificial environments on psychologicalrestoration. Hartig and Staats (2006) have measured participantattitudes towards walking in the forest or in an urban environment.
With the help of the use of neuroimaging technology such asfunctional magnetic resonance imaging (fMRI) (Jeong et al., 2005;Lang et al., 1998; Ueda et al., 2003), single photon emission computedtomography (SPECT) (George et al., 1993) and positron emissiontomography (PET) (George et al., 1993; Kosslyn et al., 1996), scientistsare beginning to ascertain the pivotal cerebral areas associated withcognitive and emotional functions (Kosslyn et al., 1996; Lane et al.,1999, 1997a, b; Lang et al., 1998; Rauch et al., 1999).
A few studies have been published on the topics concerning brainactivity in response to natural scenery. Bartels and Zeki (2004) have
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used fMRI to measure brain activity when human observers viewedfreely dynamic natural scenes (a James Bondmovie). Xiao et al. (2004)have characterized the neural representation of human portraits andnatural scenery.
In this study, we adapted two extreme types of living environ-ments, i.e., rural andurban, in order to differentiate the brain activationassociated with neural responses by using a high field fMR imaging(Matthews and Jezzard, 2004) on the basis of the blood-oxygenation-level- dependant (BOLD) signal (Ogawa et al., 1990).
2. Subjects and methods
2.1. Subjects
A total of 30 subjects (age, 20–39 years; mean age, 27.3±3.7 years) consisting of 18 males (mean age, 26.7±1.4 years) and12 females (mean age, 28.2±5.6 years) participated in the study.All of the subjects were right-handed 4-year college students withno history of neurological or psychiatric illness. The subjects hadboth urban and rural living experiences: 13.5±5.7 years in rural and13.8±4.4 years in urban. As far as the types of dwellings wherethe subjects resided, 15 subjects lived in apartments, 11 subjects livedin individual houses, two subjects lived in apartment complexes andthe remaining two subjects lived in traditional types of houses. Re-garding the environment of the residences, six subjects lived in theheart of a busy city, 18 subjects lived on the outskirts of a city and sixsubjects lived in rural villages or suburbs at the time of the study.The study protocol was approved by the ChonnamNational UniversityHospital (CNUH) Institutional Review Board (IRB). All subjects sub-mitted informed written consent prior to participation in this study.
2.2. MR imaging and fMR image acquisition
Subjects were examined on a 3 Tesla Magnetom Trio MRI unit(Siemens Medical Solutions, Erlangen, Germany) with the use of abirdcage head coil. BOLD fMRI data were acquired using gradientecho planner imaging (GRE-EPI) sequenceswith the following param-eters. The parameters included TR/TE=2000 ms/30 ms, flip angle=90°, matrix size=64×64, FOV=22×22 cm2, NEX=1, slice thick-ness=5 mm without a slice gap, total number of transverse slices=25 parallel to the anterior and posterior commissure line on a sagittalplane for covering the entire cerebral and cerebellar cortices. A totalof 4125 raw data were acquired per individual.
Fig. 1. The activation paradigm for visual stimulation with still pho
2.3. Stimulus and paradigm for brain activation
Activation stimuli were presented in the form of still photographs.The photographs were projected from the outside of themagnet roomonto a translucent screen, and these images were presented to thesubjects through a mirror angled at 45° located at the top of the headcoil. The photographs excluded an audio component. The stimulationparadigm for this study consisted of three cycles of “rest period” andtwo cycles of “activation period” (Fig. 1). Prior to the experiment,subjects were informed that they would be viewing various scenesrelated to rural and urban settings alternatively with rest. Each restperiod afforded a view of a white cross in the middle of a black screen(Mechelli et al., 2003). Each activation set of rural and urban envi-ronment scenes lasted 2 minutes and each set was preceded and wasfollowed by a 30-second rest period. During each activation set, 40pictures were presented to the subjects at the rate of 1.5 s/picture inorder to avoid boredom and to exclude other cognitions.
The living environment pictures presented in this study weredownloaded from the “fotosearch” internet website (http://www.fotosearch.com) and other websites. The scenery pictures used forvisual stimulation were chosen with a consensus from a similar agegroup (Examples are shown in Fig. 2). In order to select the scenicpictures for demonstrating the rural and urban environment, weshowed each 80–100 rural and urban images to thirty college students,followed by selecting each 40 rural and urban pictures. To mimic ruralareas, we showed natural scenes such as forests, gardens, parks andhills. Similarly, to mimic urban areas, we showed city environmentssuch as apartments, high buildings, offices, electrical cables, garbagecollections, factories and traffic.
After the fMRI experiments, each subject was asked to completea questionnaire related to life styles, living experiences in rural andurban environments, and current living environment. In addition, theMental Status Examination (Gehlert and Browne, 2006; Trzepacz andBaker, 1993) was used to evaluate the perceived subjective feelingsabout the scenes presented in this examination.
2.4. Post-processing and statistical analysis of fMRI data
Image processing and statistical analyses were carried out usingStatistical Parametric Mapping (SPM 99) software (Wellcome Depart-ment of Cognitive Neurology, London, UK). Due to the T1 saturationeffects, the first three scans of each acquisition were discarded fromthe analysis. All volume images were realigned in order to removemotion artifacts and were spatially normalized to the Montreal
tographs of rural and urban living environments is presented.
Fig. 2. Samples of still scenery pictures demonstrating rural (a) and urban (b) environments are shown.
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Neurological Institute (MNI) EPI template images for group analysis.The images were smoothed with an 8 mm full-width half-maximumGaussianfilter. After specifying the appropriate designmatrix, changesin the hemodynamic response produced by the different experimentalconditions were assessed at each voxel using a general linear modelwith a box-car method.
Brain activation was quantified by using random effect groupanalysis with the use of the one-sample t-test (pb0.01). After onesample t-testing, the brain activities, which are represented by thepercentage of activated pixels of each anatomical area with the fol-lowing Eq. (1) were measured using FALBA (Functional and Anatom-ical Labeling of Brain Activation) software (Jeong et al., 2005; Leeet al., 2004). FALBA software was useful to identify and to quantifythe local sensitivity of brain activation in each anatomical and/orfunctional area. In other words, 100% activity in an anatomical areaindicates the highest sensitivity, whereas 0% activity indicates nosensitivity.
Differences between the rural and urban scenery views wereassessed by using random effect group analysis with the use of thetwo-sample t-test (pb0.01). The x, y, and z coordinates, which arebased on MNI brain space provided by the use of SPM99 software,were converted to coordinates of Talairach and Tournoux (1988)brain space with the help of the MNI2tal tool. Labels for brain acti-vation foci were obtained in Talairach coordinates using Talairach
Demon software (Lancaster et al., 2000), and were confirmed witha comparison of activation maps overlaid on MNI-normalized struc-tural MRI images.
3. Results
3.1. Subjective emotional response to visual scenic views
The subjective responses to the scenic picture viewswere assessedfrom the completed questionnaires for three different levels: peaceful,accustomed and suffocated. The rural scenery views showed levels of‘peaceful’ for 27 subjects (90%), ‘accustomed’ for two subjects (6.7%)and ‘suffocated’ for one subject (3.3%). The urban scenery viewsshowed levels of ‘peaceful’ for zero subjects (0%), ‘suffocated’ for 16subjects (53.3%) and ‘accustomed’ for 14 subjects (46.7%).
3.2. Brain activation for rural and urban scenes
Fig. 3 shows the BOLD fMRI images obtained from the use ofthe one sample t-test for 30 volunteers. Fig. 4 compares the brainactivities (in percentage) in a given brain structure during the viewingof rural and urban scenes, whichweremeasured based on the numberof activated voxels of the total voxels. The averaged activities over thewhole brain were 11.2% and 10.7% during rural and urban sceneryviewing, respectively, giving similar brain activities. However, theaverage activities corresponding to the cortical regions showed dif-ferent results: frontal lobe (6.8±6.8, mean±standard deviation),temporal lobe (5.2±6.5), parietal lobe (5.2±3.5) and occipital lobe(45.6±19.2) for rural scenes and frontal lobe (10.9±12.7), temporal
Fig. 3. A series of the BOLD fMR images for brain areas activated following visual stimulation with still pictures of rural (a) and urban (b) environments in 30 volunteers (pb0.01) areshown.
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Fig. 4. A comparison of activities of the cerebrocortical regions in response to rural and urban scenic views is shown.
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lobe (2.9±3.5), parietal lobe (2.6±1.8) and occipital lobe (40.6±21.1) for urban scenes. The brain regions, including the temporal,parietal and occipital lobes, showed greater activity during ruralscenery viewing while the frontal lobe was dominant during urbanscenery viewing (pb0.01).
Fig. 5. A comparison of activities of the basal ganglia, limbic system and other regions inresponse to rural and urban scenic views is shown.
Fig. 5 shows the activities (in percentage) of the basal ganglia,limbic system and other areas. The insula and the splenium of thecorpus callosum showed much higher activities during rural sceneryviewing while the hippocampus, parahippocampal gyrus and amyg-dala showed more activity during urban scenery viewing (pb0.01).
3.3. Differential activation for rural versus urban scenes
Fig. 6 demonstrates the brain areas that were dominantly activatedduring visual stimulation from viewing of rural and urban scenes.The dominant brain regions associated with rural scenery viewingincluded the superior parietal gyrus (z-score, 3.72), anterior cingulategyrus (z-score, 3.12), postcentral gyrus (z-score, 2.94), globus pallidus(z-score, 2.58), putamen (z-score, 2.46) and head of the caudatenucleus (z-score, 2.28) (pb0.01). The dominant brain areas whileviewing urban scenes included the middle occipital gyrus (z-score,3.86), inferior occipital gyrus (z-score, 3.09), hippocampus (z-score,2.57), parahippocampal gyrus (z-score, 2.43), amygdala (z-score, 2.38)and lingual gyrus (z-score, 2.32) (pb0.01).
4. Discussion
Functional information of increased cerebral activation is associatedwith increased availability of oxygenandnutrients to the activatedbrainregions to support neuronal functions (Heeger and Ress, 2002). Thispresent study was initiated on the presumption of differential brainactivationpatterns betweenviewing twoextremeenvironmental scenicviews, rural and urban. However, the comparative study of the brainactivities in response to the rural and urban scenic viewing showed onlya 0.5% difference (Fig. 4); in addition, most of the activated regionsoverlapped (as shown in Fig. 3). Therefore, we used the two-sample t-test to unveil the specific activation differences. The two-sample t-testprovided information on the pivotal brain areas in response to viewingthe rural and urban scenery pictures (as shown in Table 1 and Fig. 6).
Fig. 6. Three-dimensional images of a differential activationmap yielded from the use of the two-sample t-test (pb0.01). The contrasts of rural vs. urban (a) and urban vs. rural (b) duringvisual stimulation with environmental scenery pictures are shown. Amg, amygdala; ACgG, anterior cingulate gyrus; GP, globus pallidus; HCd, head of the caudate nucleus; Hi,hippocampus; IOG, inferior occipital gyrus; LiG, lingual gyrus;MOG,middle occipital gyrus; PHG, parahippocampal gyrus; PoG, postcental gyrus; Pu, putamen; SPG, superior parietal gyrus.
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Table 1Comparison of the dominant brain centers resulting from the use of two sample t-testswith rural and urban scenic views.
Dominant brain areas Talairach co-ordinates z-scoresa
a z-scores of the corresponding brain areas were measured by contrast of urbanversus rural scenery viewing, respectively.
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During rural scenery viewing, the brain areas, including thesuperior parietal gyrus, anterior cingulate gyrus, postcentral gyrus,globus pallidus, putamen and head of the caudate nucleus werepredominantly activated with urban scenery viewing. Note that thesuperior parietal and postcentral gyri were predominantly activatedwith high levels of z-scores. The parietal lobe can be divided intotwo functional regions. One region involves sensation and perceptionand the other region is concerned with integrating sensory input,primarily with the visual system. Moreover, the postcentral gyrus isresponsible for somatosensation. This region receives input from thesomatosensory relays of the thalamus and represents informationabout touch and sense (Kandel et al., 1991).
The anterior cingulate cortex (ACC) is involved in a form of atten-tion that serves to regulate both cognitive and emotional processing(Bush et al., 2000;Whalen et al., 1998). According toUeda et al. (2003),the ACC corresponds to unpleasant pictures only, whereas Laneet al. (1997a) reported that activation of the ACC is found both forpleasant and unpleasant picture-viewing. In our study, ACC activationwas seen during rural picture viewing. Ueda et al. (2003) have arguedthat modulation of the ACC occurs as an input of negative informationin order to affect an adaptive response. However, our results wereconsistent with anatomical tracing studies that have suggested thatthe ACC is subdivided into a rostral-ventral affective division anda dorsal cognitive division, based on distinct efferent and afferentprojection systems (Bush et al., 2000; Devinsky et al., 1995; Uedaet al., 2003). Activation of the ACC during rural scenery viewing needsfurther investigation in order to be clarified.
The basal ganglia, consisting of the globus pallidus, putamen andhead of the caudate nucleus showed predominant activity for ruralscene viewing. The notion that this region is important for positiveemotions such as happiness gains support from multiple in vivo in-vestigations of addictive substances and behaviors (Breiter et al., 1997;Koch et al., 1996), reward processing and participating in enjoyableactivities such as playing a video game (Koepp et al., 1998). Activationin the basal ganglia, including the ventral striatum and putamen, hasbeen observed in response to viewing happy faces (Morris et al., 1998;Whalen et al., 1998), viewing pleasant pictures (Lane et al., 1999,1997b), happiness-induced recall (Damasio et al., 2000; George et al.,1996) and pleasant sexual and successful competitive arousal (Rauchet al., 1999; Redouté et al., 2000). Therefore, rural scenes could beconsidered as pleasant stimuli. These activations are consistentwith thesubjective responses in our study and other findings (Liberzon et al.,2000; Teasdale et al., 1999; Ueda et al., 2003).
During urban scenery viewing, the middle and inferior occipitalgyri, hippocampus, parahippocampal gyrus, amygdala and lingual gyrus
were predominantly activated as compared to rural scenery viewing.Activation of the occipital cortex in response to visual stimulationwith pleasant and aversive pictures is very common (Kalin et al., 1997;Kosslyn et al., 1996; Lane et al., 1999, 1997a; Lang et al., 1998; Reimanet al., 1997). The neural network associated with primary visual area(PVA) related spontaneous activity includes the visual association areasincluding the precuneus, precentral/postcentral gyri, middle frontalgyrus, fusiform gyrus, inferior/middle temporal gyri and parahippo-campal gyrus (Wang et al., 2008). After considering the functions ofthese regions, we speculated that PVA-related spontaneous activitymight be associatedwithmemory-relatedmental imagery and/or visualmemory consolidation processes. Emotional films, pictures and recallinvolving either positive or negative emotion or the mixture of bothseparately have engaged the medial prefrontal cortex (Lane et al.,1997a; Lang et al., 1998; Reiman et al., 1997).
Thehippocampus is primarily involved inmemory processing (Asturand Constable, 2004) and the surrounding parahippocampal gyrus wasfound to be visually responsive. Damage to the parahippocampal areadue to stroke often leads to a syndrome where patients cannot visuallyrecognize scenes even though they can recognize individual objects inscenes such as people and furniture (Sato and Nakamura, 2003).
The amygdala plays a vital role in the evaluation of the emotionalsignificance of stimuli (Irwin et al., 1996; Liberzon et al., 2000; Reimanet al., 1997; Ueda et al., 2003). Animal studies have suggested that theamygdala is involved in the evaluation of cues that predict danger tothe organism (Aggleton et al., 1992; Ueda et al., 2003). Amygdalaactivation by unpleasant stimuli plays a broad role in coordinatingresponse to upcoming danger. In addition, several lines of evidencesupport the notion that the amygdala is responsible for detecting,generating and maintaining fear-related emotions. The amygdala alsoappears to be involved in the detection of environment threats(Isenberg et al., 1999; Scott et al., 1997) as well as in the coordinationof appropriate responses to threats and danger (Weiskrantz, 1956).
Interestingly, not only the primary visual cortex (lingual gyrus,middle and inferior occipital gyri) but also the hippocampus andparahippocampal gyrus show dominant activation under input ofnegative stimuli during viewing urban pictures. These activationswere consistent with a previous study of emotional stimuli (Uedaet al., 2003). Activation of these areas during urban scenery viewingobviously explains the causes of negative emotions that correlate withthe urban environment.
According to the central limit theorem, the study population of 30subjects was sufficient for normal distribution. Also, variations ofwithin and between subjects by using random effect group analysiswere eliminated. However, drawbacks of this study included theexperimental environment, the stimulation paradigm and differencein individual perception. As far as the nature of our study is concerned,we considered two important aspects: one aspect concerned theMRI scanner environment and another concerned the nature of thepictures viewed during the fMRI experiments. During MR scanningwhile lying inside a tunnel among the gradient coils vibration noises,all subjects found MRI scanning generally tolerable. In regard to theparticipants' concentration of the pictures presented under the givenscanner environment, the question responses yielded a mean score of2.83±0.65 on a 4-point scale. In addition, there was no significantdifference that all of the subjects felt while viewing the two extremetypes of scenery pictures. In this study, we used still pictures insteadof motion pictures, which are only two extremes of depicting ruraland urban living environments.
We tried to interpret brain activation from significant BOLDsignals, subjective emotional responses with questionnaires andrelated research studies without the help of additional techniquessuch as PET, electroencephalography and magnetoencephalography.Functional MRI detects the brain regions associated with neuronalactivity, which indirectly alters the local deoxyhemoglobin concen-trations in the activated cortices. Therefore, fMRI is limited in the
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measurement of relative neural activity, whereas PET can provideeither absolute or relative measurements (Jeong et al., 2005). Othersupplementary methods such as an event-related, multiple conjunc-tion analysis and dynamic causal modeling might be desired to beperformed to gain information that is more accurate for a future study,as such methods would allow the isolation of individual trial eventsrelated to emotional response.
5. Conclusion
For the first time, we have utilized functional MRI to evaluatedifferential brain activation areas in response to viewing rural andurbanliving environments. These findings allow better characterization ofneural activation, suggesting an inherent preference towards nature-friendly living. Such a theoretical acquisition may have an importantpractical impact in view of potential applications for bio-housing andthe development of environmental psychology-related areas.
Acknowledgement
This work was supported by the Korea Research Foundation Grantfunded by the KoreanGovernment (MOEHRD) (The Regional ResearchUniversities Program/Bio-housing Research Institute).
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