科技部補助專題研究計畫成果報告
期末報告
利用波動聚集方法探討老化對靜息態腦網路與心率變異度之影
響
計 畫 類 別 ： 個別型計畫 計 畫 編 號 ： MOST 104-2112-M-004-001-執 行 期 間 ： 104年08月01日至105年07月31日 執 行 單 位 ： 國立政治大學應用物理研究所 計 畫 主 持 人 ： 蕭又新 計畫參與人員： 碩士班研究生-兼任助理人員：林俊鴻 碩士班研究生-兼任助理人員：邱建堯 博士班研究生-兼任助理人員：謝佳宏 報 告 附 件 ： 出席國際學術會議心得報告中 華 民 國 105 年 10 月 30 日
中 文 摘 要 ： 本研究藉由種子體素關聯性分析這些中樞自主腦網路的核心腦區 ，我們的結果顯示這些所屬於中樞自主腦網路下的交感和副交感網 路是潛在地存在於靜息態大腦中，其中自發性血氧濃度相依訊號的 正反相關現象將其劃分為交感和副交感相關的網路。進一步的驗證 指出與年齡相關的退化發生在右前腦島與邊緣整合皮質的功能性聯 結，而在中樞自主腦網路中這些老化而降低的功能性聯結，很可能 與老年人在減弱的記憶功能和調節穩定的身體狀態有所相關。 中 文 關 鍵 詞 ： 老化, 功能性連結, 腦島, 中樞自主腦網路, 靜息態
英 文 摘 要 ： Autonomic nervous system (ANS) plays a critical role of quick reactions in the adaptation to the environment
change, which constantly modulates visceral reflex function for a stable bodily state of task preparedness. More
recently, the resting-state functional connectivity (rsFC) without subjects performing a specific cognitive task provides an alternative tool for tracking the potential neural substrates but less is known in the spontaneous neuronal activity of central autonomic network (CAN), where the CAN components encompass a sympathetic/parasympathetic network and anti-correlations with each other. We found the evidence of aging that is related to weak coupling of CAN components between right anterior insula (aINS.R) and limbic regions, which may affect the regulation of autonomic control and more specifically with memory and emotion function. Our study also provided insights about the central processing map of autonomic functions in the resting brain.
英 文 關 鍵 詞 ： aging, functional connectivity, insula, central autonomic network, resting state
1. Introduction
The autonomic nervous system (ANS) controls the unconscious action of internal organs to accommodate the environmental change in human and animals. The ANS is further classified into a complementary dichotomy between sympathetic and parasympathetic nervous system. The arousal of sympathetic nervous system is associated with task, engagement, and the use of metabolic energy; parasympathetic nervous system with rest, disengagement, and the restoration of metabolic energy. Perhaps because this dynamic modulation of autonomic function usually occurs without directly conscious control or awareness, autonomic effect on the human brain function has been conceptually underestimated by many neuroimaging researchers and psychologists (Critchley et al., 2011). More recently, the combination of human neuroimaging across a range of different experimental tasks with autonomic monitoring, such as heart rate, sympathetic skin response, blood pressure, and baroreflex suppression, have provided clinically relevant data to insight into the association between the visceral influences and brain behavior (Critchley et al., 2013). In a systematic review article, the estimation of meta-analysis of human brain neuroimaging identified the core regions of central autonomic network (CAN), comprising the midcingulate cortices, left amygdala, right anterior and left posterior insula across different tasks (Beissner et al., 2013). Their anatomical projections and functional neuroimaging evidence in humans show the integration of ANS with cognitive function, such as perception, cognition, and emotion (Critchley, 2005; Critchley and Harrison, 2013). In addition, the ANS continuously controls the internal organs (viscera) for a stable bodily state of task preparedness but less is known about the spontaneous activity of CAN when our minds wander or called the resting state. We anticipated that the functional connectivity of the core regions within CAN would provide a more underlying physiology interpretation on the human brain functional organization.
Over the last two decades, resting-state functional connectivity (rsFC) has gain a great attraction to provide a powerful tool for tracking the neural substrates of cognitive abilities across lifespan (Mevel et al., 2013) and more advances in neurodegenerative diseases such as autism, schizophrenia and Alzheimer’s disease, termed the default mode network (DMN) exhibiting high levels of baseline metabolic activity (Greicius et al., 2003; Greicius et al., 2004; Jones et al., 2011; Mevel et al., 2011; Raichle et al., 2001). The different spatial patterns of resting state networks (RSNs) are classified into the sensory-motor integration (Biswal et al., 1995), dorsal and ventral attention systems (Fox et al., 2006), executive control systems and
hippocampal-parietal memory systems (Vincent et al., 2008; Vincent et al., 2006). The restless brain consuming enormous energy not only maintains the basal condition but also plausibly traces the unconstrained conscious cognition (Raichle, 2006, 2011). Specifically, the brain task-related dichotomy between externally attention system and DMN is also consistently manifested in the absence of tasks (Buckner et al., 2013; Fox et al., 2005; Uddin et al., 2009) but the physiological interpretation on the mechanism of spontaneous activities still remains debatable (Morcom and Fletcher, 2007), especially the anti-correlation in the low- frequency blood-oxygen-level dependent (BOLD) signal (Fox et al., 2009; Murphy et al., 2009). A striking study by using Granger causality analysis may shed light on the dynamical processing of competing relationship that proposed a third network initiated and coordinated in switching between central executive network (CEN) and DMN, called salience network (SN) for insular function (Menon and Uddin, 2010; Sridharan et al., 2008). As mentioned earlier, insular cortex also plays a core region of autonomic function and involves in cognitive control and mental processing (Nagai et al., 2010), which may link the underlying relation between CAN and RSNs to form an extensive physiology framework in the resting brain.
Aging is commonly associated with changes in cognitive functions and leads to behavior decline in the elderly, such as those involving in perception, attention, memory, and emotion. By using the human functional neuroimaging to examine brain activity in vivo, age-related declines in evoking activation (deactivation) of task-related (default-mode) regions across memory tasks had been reported (Grady et al., 2006), which clued about an impaired control ability between CEN and DMN. Concerning the effect of age on the ANS, the elderlies are concurrent with the reductions of autonomic regulation suffering from the impaired adaptation to environment and visceral stimulus, which may increase the risk of cerebrovascular accidents and contribute to the neurodegenerative diseases (Hotta and Uchida, 2010).
Based on the past findings reviewed above, we hypothesized that the functional connectivity of CAN from the four core regions would encompass the most associated region of autonomic function both in young and aged adults. Although there were several studies on the functional connectivity of insular cortex, the CAN is also a vital substrate of the integrated mechanisms for adapting ourselves to new environments. Therefore, we addressed the relationship between the four core regions of CAN and their functionally
connected regions across young and elder adults. Since the ANS is of paramount importance to daily life— even in the resting state, the functional connectivity of CAN would be a substantial neural substrate in healthy adults and potentially affected by age. To probe the age-related differences, a resting CAN functional connectivity analysis was performed using the midcingulate cortices, left amygdala, right anterior and left posterior insula as seed regions. We expected that normal aging would impact on the resting CAN associating with the interoceptive awareness and the engagement in external environment or personal experience.
2. Methods
2.1. Subjects and MRI data acquisition
We used a public pilot datasets of the Nathan K line Institute / Rockland Sample (NKI-RS), which is aimed to generate a large scale, extensively phenotyped dataset for the purpose of discovery science in psychiatry (Nooner et al., 2012). The data collection and release at the International Neuroimaging Data-sharing Initiative (INDI) online database was approved by the institutional review board at the Nathan Kline Institute and Montclair State University with the written informed consent was obtained from all subjects. Two group of right- handed healthy subjects, matched on gender and body mass index (BMI), were selected in this study: (1) 23 young subjects (aged 22–29, mean 24.6±2.2, female 10, BMI 24.0±4.9), (2) 23 elderly subjects (aged 60–85, mean 69.3 6.9± , female 11, BMI 26.5±4.5). Specifically, there are three articles used the NKI/RS data to study the human brain functional connectome (Cao et al., 2014; Yang et al., 2014) and the changes in functional and structural connectivity (Betzel et al., 2014) across the human lifespan.
Subjects were received instructions to keep their eyes closed, relax their minds, and not to fall asleep. Images were gathered on a 3T MRI scanner (Magnetom Trio Tim, Siemens Medical Systems, Germany). Resting-state fMRI scans were collected using an echo-planar imaging (EPI) sequence with a repetition time (TR) of 2500 ms, an echo time (TE) of 30 ms, and a 80° flip angle. The acquisition matrix was 72×72, with a 216 mm field of view (FoV). Each scan session was 650s long and comprised 260 functional volumes, with each volume consisting of 38 axial slices. T1-weighted images were acquired using the following magnetization-prepared rapid gradient echo (MPRAGE) sequence: TR / TE = 2500 / 3.5 ms, time inversion (TI) = 1200 ms, FA = 8°, FOV = 256 × 256 mm2, voxel size = 1.0 × 1.0 × 1.0 mm3, number of slices = 192. The T1-weighted images were subsequently used for spatial normalization and localization.
2.2. fMRI data preprocessing
Standard preprocessing was performed by using Data Processing Assistant for rs-fMRI (DPARSF) tools (Chao-Gan and Yu-Feng, 2010) based on some functions in Statistical Parametric Mapping (SPM8) (http://www.fil.ion.ucl.ac.uk/spm) and Resting-State fMRI Data Analysis Toolkit (REST) (Song et al., 2011). Briefly, the preprocessing included the following: (1) the removal of first 10 functional volumes for the individuals' adaptation to the environment, (2) slice timing correction for timing offsets using sinc interpolation, (3) head motion correction using a six-parameter spatial transformation. Three young and four elderly subjects were excluded under the criterion with head motion more than 1.5mm or 1.5° of head rotation. To limit the nuisance covariates, (4) the functional data were then removed from the data through multiple regression analysis: (i) head motion, (ii) white matter, (iii) cerebrospinal fluid, and (iv) global signal (Fox et al., 2005; Kelly et al., 2008). The resulting functional data for each subject was then (5) co-registered to their corresponding MPRAGE images and (6) subsequently spatially normalized to the Montreal Neurological Institute (MNI) space using the normalization parameters estimated during unified segmentation and resampled to 3-mm isotropic voxels and (7) a Gaussian kernel of 4 mm (full width at half maximum) for spatial smoothing. Finally, (8) the temporal band-pass filtering (0.01-0.08 Hz) were carried to reduce low-frequency drift and high-frequency physiological noise (Biswal et al., 1995; Lowe et al., 1998).
2.3. SCA of the cores of CAN
For the current study we examined correlations associated with four predefined seed regions in the Montreal Neurological Institute (MNI) coordinate from the meta-analysis for central processing of autonomic function (Beissner et al., 2013): two regions, referred to as sympathetic regions, associating sympathetic regulation, and two regions with a dual function, both sympathetic and parasympathetic regulation. Sympathetic regions were centered in the midcingulate cortex (MCC; 4, 0, 48) and left posterior insula (pINS.L; -32, -18, 12). The other two dual regions were centered in the right anterior insula (aINS.R; 34, 20, 4) and left amygdala (AMYG.L; -22, -8, -16). Pearson correlation coefficients between the four time series of the average signal of 6- mm radius seed sphere and time series of each voxel within the entire brain were calculated and converted to z- maps using Fisher ’s z-transform to improve the normality. Individual z- maps underwent two-tailed one-sample t test to determine brain regions with significant positive or negative correlations to the seeds voxel by voxel, respectively. An false discovery rate (FDR) method with a threshold of q<0.01 was used to correct for multiple comparisons within the whole brain mask from a
20 adjacent voxels (540 mm3). The three-dimensional surface visualizations of the results were generated using the BrainNet Viewer (http://www.nitrc.org/projects/bnv/) (Xia et al., 2013).
2.4 Comparisons between groups
To delineate the age-related differences in CAN components, the voxels positively and negatively correlated with the pMCC seed were respectively regarded as the pMCC(+) and pMCC(-) networks following the previous multiple comparisons (q<0.01, FDR corrected) and cluster size larger than 540 mm3. Correspondingly, the significant voxels correlated with the pINS.L, aINS.R and AMYG.L were referred to as the pINS.L(+), pINS.L(-), aINS.R(+), aINS.R(-), AMYG.L(+) and AMYG.L(-) networks. Before group comparison, the union sets of each network in young and elderly group were used for masking in subsequent between-group analysis. Individual z- maps underwent two-tailed two-sample t test for group comparison. Multiple comparisons were corrected using an FDR method with thresholds of p<0.05 and cluster size > 10 adjacent voxels (270 mm3). The individual modulated gray matter volumes were entered as covariates to regress out the confound of brain volume atrophy (Oakes et al., 2007). We also used Cohen’s d (Parker and Hagan-Burke, 2007) to describe the effect size for the significant difference.
3. Results
3.1. Functional connectivity of the core regions of CAN
Within group analysis was used to study the integration and consistency of the CAN components in the both group (Fig. 1 and 2, young: left column and elderly: right column). As shown in the pMCC(+) and pINS.L(+) networks (Fig. 1, red color), the two sympathetic-associated seeds were positively correlated with each other, sensorimotor cortex (SMC) and supplementary motor area (SMA) in both groups and the additional regions of thalamus (THAL, data shown in Supplementary Fig. 1) in the young group. For the pMCC(-) network, the correlated regions included posterior cingulate cortex (PCC), bilateral angular gyrus (AG) and middle temporal gyrus (MTG) in both group and the additional regions with ventromedial prefrontal cortex (vmPFC) and dorsomedial prefrontal cortex (dmPFC) were involved in the young group. The pINS.L(-) network included the right precuneus (PCUN.R) and the bilateral AG in the young group. The positive maps of the two core regions were similar, whereas more additional area of anti-correlated regions was presented in the pMCC(-) network.
The distribution of another two core regions was illustrated in Fig. 2. The aINS.R(+) network showed that bilateral dorsal anterior cingulate cortex (dACC), THAL (Data shown in the Supplementary Fig. 2),
temporal-parietal junction (TPJ), ventral frontal cortex (VFC), frontal eye field (FEF) and dorsolateral prefrontal cortex (dlPFC) were involved in both groups (top row of Fig. 2, red color). For the aINS.R(-) network, bilateral PCC, AG, MTG, vmPFC and dmPFC were involved in both group and additional regions of the temporal pole (TPO) and hippocampal formation (HF) in young group (top row of Fig. 2, blue color). An interesting finding is that the above three core regions, namely aINS.R, pINS.L and pMCC, were positively correlated with THAL and negatively correlated with the default mode network (DMN). Finally, the AMYG.L(+) networks exhibited positive correlation with bilateral HF and TPO in both group and an additional region of left ventromedial prefrontal cortex (vmPFC.L) in the young group (bottom row of Fig. 2, red color). For the AMYG.L(-) network, the right dorsolateral prefrontal cortex (dlPFC.R) was anti-correlated in the young group (bottom row of Fig. 2, blue color). A combination of previous results, AMYG.L negatively correlated with the other core regions of CAN via HF, TPO and vmPFC.L to create a functional framework of CAN.
3.2. Comparisons between groups
Significantly decreased functional connectivity in the elderly group was found in the aINS.R(-) network including the left temporal pole (TPO.L), left dorsomedial prefrontal cortex (dmPFC.L), left ventromedial prefrontal cortex (vmPFC.L), and right hippocampal formation (HF.R) (Fig. 3A, blue color. Details are in Table 1). The bar plot in Fig. 3B demonstrated these significantly decreased anti-correlation with TPO.L (p<0.001, effect size = 2.405), dmPFC.L ( p<0.001, effect size = 1.745), vmPFC.L ( p<0.001, effect size = 1.688) and HF.R (p<0.001, effect size = 1.543) between the young and elderly groups.
Fig. 1. T-score maps showing voxels significantly correlated (red) or anti-correlated (blue) with
the two sympathetic-associated core regions (green circles) in the young (left column) and elderly (right column) group.
Fig. 2. T-score maps of aINS.R (top row) and AMYG.L (bottom row) networks in the young (left column)
and elderly (right column) group. Red color represents positive correlations. Blue color represents negative correlations with the seed regions in aINS.R and AMYG.L (green circles).
Fig. 3. Direct comparisons for (A) the aINS.R(-) network between the young and elderly groups.
Blue color means reduced anti-correlation for elderly vs. young. (B) Bar plot showing group differences in the significant clusters of aINS.R(-) network affected by aging. Abbreviations: TPO.L, left temporal pole; dmPFC.L, left dorsomedial prefrontal cortex; vmPFC.L, left ventromedial prefrontal cortex; HF.R, right hippocampal formation.
Table 1. Main regions showing significant connectivity alterations in the aINS.R(-) network
between the young and elderly groups.
Regions MNI coordinates Peak T score Brodmann areas Cluster size (mm3) x y z aINS.R
( )
−
TPO.L -42 15 -27 -6.11 38 1809 dmPFC.L -12 63 24 -5.17 10 729 vmPFC.L -6 33 -15 -4.82 11 1026 HF.R 30 -6 -24 -4.71 378Thresholds were set at p<0.05(FDR corrected) and cluster size > 10 voxels (270 mm3). aINS.R, right anterior insular; TPO.L, left temporal pole; dmPFC.L, left dorsomedial prefrontal cortex; vmPFC.L, left ventromedial prefrontal cortex; HF.R, right hippocampal formation.
Fig. 4. Diagrammatic representation of the central autonomic network (CAN) and the reconstruction of its
main functional connectivity between the four core regions (oval) and the significantly correlated regions (rectangle). Red color represents the positive correlation starting from the two sympathetic-associated core regions, namely pMCC and pINS.L; blue color represents the negatively correlated regions.
Supplementary figures
Suppleme ntary Fig.1. Axial section of T-score maps for pMCC (top) and pINS.L (bottom)
Suppleme ntary Fig.2. Axial section of T-score maps for aINS.R (top) and AMYG.L (bottom)
networks in the young (left) and elderly (right) group. L: left.
4. Discussion
Using the seed-voxel based correlation analysis of the core regions of CAN, our current results showed that the dynamic activity of sympathetic and parasympathetic CAN subdivisions is inherently represented in the resting human brain. Most brain areas, identified as commonly activated regions for specific ANS responses to different stimuli/tasks, exhibit organized functional activity with correlated spontaneous BO LD signal fluctuations within a sympathetic/parasympathetic associated network and anti-correlations with each other. Based on these functional distinctions, one would predict that a potential neural substrate emerging from the ANS responses, i.e., the interplay between the sympathetic and parasympathetic activity, involving in the insular circuitry. Further investigation with gray matter atrophy correction indicated the age-related declines in the functional connectivity of aINS.R with the limbic integration cortex. The decreased functional connectivity of CAN with aging plausibly related to the impaired abilities in the memory function and the regulation of stable bodily state for task preparedness in the elderlies. Below we will discuss the potential functional competition, or differentiation, of CAN between the sympathetic and parasympathetic networks and their implications of normal aging.
4.1. Functional connectivity of pINS.L and pMCC
correlations with each other that is consistent with prior studies on functional connectivity analyses of these two regions (Cauda et al., 2011; Deen et al., 2011; Taylor et al., 2009) and data driven methods with independent component analysis (ICA) (Beckmann et al., 2005). Their functional connected regions also included primary and secondary motor and somatosensory cortices and SMA suggested that the original finding about intrinsic behavior of skeletomotor body orientation (Biswal et al., 1995) gravitates to the sympathetic network. Furthermore, pMCC was additionally correlated with sensorimotor association cortex (SAC) and supramarginal gyrus (SMG), having a high-order role for integrating sensorimotor information (Vogt, 2005), while pINS.L is mainly correlated with SMC for the somatosensory role (Craig, 2002). Alternatively, the anti-correlated network for these two sympathetic core regions included the commonly activated in brain areas associated with parasympathetic regulation, such as PCC and AG. They have also been widely investigated the deactivation profile in a variety of perceptual and cognitive tasks (Golland et al., 2007; Sridharan et al., 2008). Altogether, the sensorimotor system, such as SMC, SAC, and SMA, correlates with the two sympathetic cores show a preference to sympathetic regulation (Critchley, 2004) and the regions of DMN, such as PCC, vmPFC, dmPFC and AG, anti-correlates with the two sympathetic cores show a preference to parasympathetic regulation (Goswami et al., 2011). These results showed that the divergent functional connectivity tended to aggregate their sympathetic/parasympathetic division of the autonomic function in CAN (Beissner et al., 2013).
4.2. Functional connectivity of aINS.R and AMYG.L
The observed pattern of aINS.R in this study resembles the previous studies in the intrinsic functional connectivity of insular cortex (Cauda et al., 2011; Deen et al., 2011; Taylor et al., 2009). First, the positive correlation of aINS.R with dACC is consistent with the specific distribution of SN which may initiate and mediate the response of human awareness at the beginning of different sets of tasks (Craig, 2009; Dosenbach et al., 2006; Sridharan et al., 2008). In addition, the anti-correlated relationship between the attention system and default mode system is also illustrated (Fransson, 2005; Uddin et al., 2009), which is compatible with the recurrent competition in the task-related dichotomy (Kelly et al., 2008). These spontaneous functional connectivity are constrained by the anatomic connectivity of INS that connected to subdivisions of the frontal, parietal, and temporal lobe as well as of the cingulate gyrus in primates (Augustine, 1985; Augustine, 1996) by the large axons of the von Economo neurons (VENs) (Allman et al., 2005; Craig, 2009; Menon and Uddin, 2010). In conjunction with the functional connectivity of the two sympathetic core regions, i.e. pINS.L and pMCC, the sympathetic-associated functions encompass the external environmental monitoring and bottom- up attention, whereas parasympathetic-associated functions mainly associate with the
introspectively oriented and mental activity. The important role of bilateral INS and dACC in autonomic function has been known to support the generation of cardiovascular arousal during effortful cognitive and motor behavior (Critchley et al., 2003). As the mentioned above, the aINS.R not only may initiate the control signals responsible in cognitive tasks but also potentially mediate between the sympathetic and parasympathetic nerve systems within CAN.
Finally, a dual role as both sympathetic and parasympathetic regulation center across tasks, namely AMYG, functionally connected with vmPFC and TPO that echoed the temporal-amygdala-orbitofrontal network of limbic system anatomically connected by the uncinate fasciculus, which is associated with visceral sensation and emotion with semantic memory and behavior (Bechara and Naqvi, 2004; Catani et al., 2013). In this study, the network intrinsically predisposed to parasympathetic activity with DMN and hippocampal-parietal memory network (Vincent et al., 2006) forming a broad range of introspection function, such as episodic memory and emotion systems. Importantly, there were two vital connectors, i.e. THAL and vmPFC, respectively resided in the sympathetic and parasympathetic networks. THAL represented as a critical role in the sympathetic networks connected with the three core regions that is consistent with the correlation mapping between motor/premotor and somatosensory and the ventral posterior nucleus in THAL (Zhang et al., 2008). And, based on the anatomical organization of THAL in interoceptive neural pathways, the important autonomic function about the visceral information (Critchley and Harrison, 2013) is included in the sympathetic associated network. In our analysis, vmPFC regarded as another crucial connector in the parasympathetic network which is negatively correlated with the two sympathetic cores of pMCC and aINS.R and synchronized with AMYG. The probable active function of vmPFC has been recognized as the association with autonomic control in cardiovascular system (Harper et al., 2000; Jennings et al., 2016; Verberne and Owens, 1998) and more investigated to involve in modulating the parasympathetic efferent outflow to the heart (Wong et al., 2007).
In brief summary, taken together with the results of all four core regions (Fig. 4), the functional connectivity of CAN mainly overlapped the cortical sites of motor association, visceral sensorimotor and externally cued attention system engaging in sympathetic activity, otherwise the introspection, episodic memory, and emotion systems engaging the parasympathetic activity. The well anti-correlation between the sympathetic and parasympathetic network plausibly mediated by the aINS.R that supports a autonomic neurobiological model for accompanying and perhaps facilitating cognitive and behavior function in adults (Critchley et al., 2013).
4.3. Age-related difference in CAN
Growing evidence indicates that human cognitive decline in normal aging is highly associated with the alterations of inter- and intra-region functional connectivity among RSNs (Betzel et al., 2014; Ferreira and Busatto, 2013), including the many studies focused on DMN (Damoiseaux et al., 2008; Mevel et al., 2013) and the advanced model of SN switching between DMN and CEN (He et al., 2013). O ur findings are important in the weak coupling of CAN with gray matter atrophy correction where the negative connections of aINS.R with dmPFC.L, HIP.R, vmPFC.L, and TPO.L were significantly decreased in the elders. The dmPFC.L partly overlapped the key component in DMN for characterizing the maturing development of the intra-connected default network form children to adults (Fair et al., 2008; Power et al., 2010). The critical memory role of HIP.R at rest has also documented an altered metabolism of that network and led to a more functionally isolated hippocampal network from DMN, which might contribute to memory dysfunction in elderly people (Salami et al., 2014). It is also compatible with the functional connectivity of SN for cognitive control between DMN and CEN may be impaired in elderly individuals (He et al., 2013). Given the autonomic role of insular cortex, the weaker coupling with temporal-amygdala-orbitofrontal network may reflect on a functional deficit in autonomic balance (Zhang, 2007). As shown in the delayed recovery of heart rate after standardized exercise in elderly subjects, it would inevitably lead to a decline of visceral arousal with unconscious processing, (Norris et al., 1953). More recently, the functional disconnection between the aINS.R and DMN is associated with an impaired working memory performance in a common autonomic dysregulation for obstructive sleep apnea (OSA) patients (Zhang et al., 2015).
4.4 Limitations
There are some limitations of this study. First, the spontaneous neural activity is indirectly measured via the low frequency fluctuation (~0.05Hz) of blood-oxygen- level-dependent (BO LD) signal, which depends on the ratio of oxy- to deoxyhemoglobin with the subtle metabolic and hemodynamic processes during rest. There are several physiology conditions for the ongoing activity, such as Birn and colleagues investigated that the respiratory volume (~0.03 Hz) appears to be localized to DMN and near large veins (Birn et al., 2006). In addition, the high frequency of cardiac signals (~0.9 Hz) near large arteries, especially for the insular circuitry, may be aliased into lower frequencies when the BOLD signal is sampled at a lower frequency (~0.5 Hz) than the physiology signals (the Nyquist theorem). Those physiological effects would partially interfere in observing the interaction between the CAN components. Second, the functional connectivity of AMYG.L is less significant than that of the other core regions that is more likely due to the
functional image distortions, such as the local magnetic field inhomogeneity near nasal cavity. Further works will need to clarify the dual role of AMYG.L on central autonomic processing (Beissner et al., 2013). Third, the functional connectivity of CAN was observed after regressing out the global BO LD signal. Even though it is a controversial step to induce the negative correlations (Chai et al., 2012; Murphy et al., 2009), it has also been shown that it can improve the cortical-thalamic relationships (Fox et al., 2009) as well as the results in this paper.
Conclusions
In summary, the data presented here suggests that the well coupling of CAN is associated with the healthy extensive and reciprocal activation of the sympathetic and parasympathetic nerves, which supports the previous findings of the extrinsic system negatively correlated with the intrinsic system. We further provided evidence that the decreased functional connectivity within the CAN was present at the connection between a critical role of switching function, e.g. right anterior insula, and parasympathetic network, mainly connected with anterior DMN, hippocampus and limbic system but not in sympathetic network. These findings suggest that increased age is related to weak coupling of CAN components, which may affected on modulation of autonomic function during the unconscious processing and more specifically with cognitive ability and emotion regulation.
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Conference Venue: Premier Hotel Tsubaki Sapporo
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Acceptance
L
etter
2016 GCEAS
2016 Global Conference on Engineering and Applied Science
July 19-21, 2016 Hokkaido, Japan Dear Yuo-Hsien Shiau,
We sincerely appreciate your paper submission. On behalf of the Conference Organizing Committee of GCEAS 2016, we are pleased to inform you that the following submission was accepted as one of the Poster Presentation in this conference:
Paper ID: 464
Title: Functional Connectivity of the Central Autonomic Network in the
Resting Brain
The exact time and room of your presentation session will be specified in the GCEAS Conference Program online at http://gceas-conf.org/ in the end of June 2016. Please make sure your manuscripts conform to the writing format
which is available on the conference website. Manuscripts conform to the format guidelines are required to be printed in the proceedings.
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Best regards,
Functional connectivity of the central autonomic network
in the resting brain
Jia-Hong Sie
a
, Jun-Hong Lin
b
, Shang-Yueh Tsai
b,c
, Woei-Chyn Chu
a
, Yuo-Hsien Shiau
b,c,*
a
Institute of Biomedical Engineering, National Yang-Ming University, Taipei 11221, Taiwan (ROC)
b
Graduate Institute of Applied Physics,
c
Research Center for Mind, Brain, and Learning, National Chengchi University, Taipei 11605, Taiwan (ROC)
*
E-mail address: yhshiau@nccu.edu.tw
References
[1] Critchley, H.D. et al. (2011). Autonomic Neuroscience, 161, 34-42.
[2] Critchley, H.D. et al. (2013). Handbook of Clinical Neurology, 117, 59-77.
[3] Beissner, F. et al. (2013). The Journal of Neuroscience, 33, 10503-10511.
[4] Taylor, K.S. et al. (2009). Human brain mapping 30, 2731-2745.
[5] Cauda, F. et al. (2011). Neuroimage 55, 8-23.
[6] Uddin, L.Q. et al. (2009). Human brain mapping 30, 625-637.
[7] Sridharan, D. et al. (2008). PNAS 105, 12569-12574.
Abstract
Autonomic nervous system (ANS) plays a critical role of visceral reflex function in the adaptation to
the environment change. Recently, combining with the autonomic monitoring, considerable efforts
have been devoted to the functional studies of human brain imaging exploring the associated sites of
ANS in the central autonomic network (CAN). An important question is whether this functional
repre-sentation emerges only in response to different stimuli/tasks or is represented more fundamentally in
the internal dynamics of brain activity. Therefore, we employed resting-state data to examine the core
regions of CAN in 23 right-handed healthy subjects. The results of CAN components exhibit
orga-nized functional activity with correlated spontaneous BOLD signal fluctuations within a
sympathetic/parasympathetic network and anti-correlations between them. Additionally, by using the
graph theoretic analysis, the sympathetic and parasympathetic networks exhibit small-world attributes
(high clustering and short paths). Those results are consistent with anatomical and functional
connec-tivity studies in primates and human, and provide insights into the understanding the central
process-ing of autonomic functions in human brain.
Keywords: central autonomic network, resting state, graph theoretical analysis, insula, central executive network
Methods
We used a public pilot datasets of the Nathan Kline
Institute / Rockland Sample (NKI-RS), which is aimed to
generate a large scale, extensively phenotyped dataset
for the purpose of discovery science in psychiatry.
Twenty three right-handed healthy subjects (aged 22–29,
mean 24.6±2.2, female 10) were selected in this study.
During the resting state, subjects received instructions to
keep their eyes closed, relax their minds, and to not
move. Resting-state fMRI scans were collected using an
echo-planar imaging (EPI) sequence. T1-weighted
images were acquired using the magnetization-prepared
rapid gradient echo (MPRAGE) sequence. The
T1-weighted images were subsequently used for spatial
normalization and localization.
In order to construct the brain functional network,
we adopted automated anatomical labeling (AAL) atlas
to parcellate the whole brain into 90 regions of interests
(ROIs) as the nodes of brain network. We selected the
ROIs involving in the sympathetic (36 nodes) and
parasympathetic (24 nodes) activity to construct two
different matrices of Pearson’s correlation coefficient.
There were two efficient parameter (global efficiency
and local efficiency) and small-world value to examine
the global network property. Also, the nodal network
parameters included nodal degree, regional efficiency,
betweenness and vulnerability to identify the different
improtance within a network.
Results
Introduction
The autonomic nervous system (ANS) is of
paramount importance to daily life that controls
the unconscious action of internal organs to
ac-commodate the environmental change in
human and animals. Perhaps because this
dy-namic regulation of autonomic function usually
occurs without directly conscious control or
awareness, autonomic effect has been
concep-tually regarded as “noise” by many
neuroimag-ing researchers and psychologists before [1].
However, more recently, the combination of
human neuroimaging with autonomic
monitor-ing, such as heart rate, sympathetic skin
re-sponse, blood pressure, and baroreflex
suppres-sion, have provided clinically relevant data to
insight into the association between the visceral
influences and brain behavior [2]. In a
system-atic review of the neuroimaging studies, the
es-timation of meta-analysis identified the core
re-gions of central autonomic network (CAN),
comprising the midcingulate cortices, left
amygdala, right anterior and left posterior
insu-lar across different tasks [3]. To our
knowl-edge, few studies have delineated the intrinsic
functional connectivity of CAN, which may
further provide a more underlying physiology
interpretation on the human brain functional
or-ganization.
Fig. 1. T-score maps showing voxels
significantly correlated (red) or anti-correlated
(blue) with the four core regions (green circles)
Fig. 2. Graph theoretical analysis of the
sympathetic and parasympathetic network.
Discussion
Even though there are several studies on the
functional connectivity of insular cortex [4,5],
few studies have integrated the relationship
between the RSNs and CAN. Using the
seed-voxel based correlation analysis of the
core regions of CAN, our current results
showed that the close relationship between the
sympathetic and parasympathetic CAN
subdivisions is inherently represented in the
resting human brain. The data presented here
suggests that the well coupling of CAN is
associated with the reciprocal activation of
autonomic modulation, which is compatible
with the previous findings of the extrinsic
system negatively correlated with the intrinsic
system [6].
Furthermore, the subdivision of CAN
between the sympathetic and parasympathetic
networks also represent higher clustering (local
efficiency) and short path length (glocal
efficiency) that exhibit small-world attributes.
The regional network parameters identify the
critical role of insula (INS) in sympathetic
network and posterior cingular cortex (PCC) in
parasympathetic network. Both of them have
been implicated an important role to initiate
and mediate the response of human awareness
[7].
Table 1. Hubs of the central autonomic network.
Region
Nodal degree
Nodal
efficiency
Betweenness
centrality
Vulnerability
Sympathetic network
INS.L
1.55
1.21
2.06
1.24
INS.R
1.57
1.22
2.06
1.24
RO.L
1.59
1.23
1.82
1.24
RO.R
1.54
1.21
-
1.19
T1.L
1.50
1.20
-
1.21
Parasympathetic network
PCC.L
1.56
1.26
2.49
1.38
GR.L
1.46
1.22
-
1.23
F1M.L
1.41
1.20
-
1.23
F1MO.L
1.38
1.20
-
1.23
科技部補助計畫衍生研發成果推廣資料表
日期:2016/10/25科技部補助計畫
計畫名稱: 利用波動聚集方法探討老化對靜息態腦網路與心率變異度之影響 計畫主持人: 蕭又新 計畫編號: 104-2112-M-004-001- 學門領域: 電漿、非線性、流體及統計物理 －理論無研發成果推廣資料
104年度專題研究計畫成果彙整表
計畫主持人：蕭又新 計畫編號： 104-2112-M-004-001-計畫名稱：利用波動聚集方法探討老化對靜息態腦網路與心率變異度之影響 成果項目 量化 單位 質化 （說明：各成果項目請附佐證資料或細 項說明，如期刊名稱、年份、卷期、起 訖頁數、證號...等） 國 內 學術性論文 期刊論文 0 篇 研討會論文 0 專書 0 本 專書論文 0 章 技術報告 0 篇 其他 0 篇 智慧財產權 及成果 專利權 發明專利 申請中 0 件 已獲得 0 新型/設計專利 0 商標權 0 營業秘密 0 積體電路電路布局權 0 著作權 0 品種權 0 其他 0 技術移轉 件數 0 件 收入 0 千元 國 外 學術性論文 期刊論文 0 篇 研討會論文 1 2016 Global Conference onEngineering and Applied Science July 19-21, 2016 Hokkaido, Japan ""
""Functional Connectivity of the Central Autonomic Network in the Resting Brain"" 專書 0 本 專書論文 0 章 技術報告 0 篇 其他 0 篇 智慧財產權 及成果 專利權 發明專利 申請中 0 件 已獲得 0 新型/設計專利 0 商標權 0
營業秘密 0 積體電路電路布局權 0 著作權 0 品種權 0 其他 0 技術移轉 件數 0 件 收入 0 千元 參 與 計 畫 人 力 本國籍 大專生 0 人次 碩士生 2 博士生 1 博士後研究員 0 專任助理 0 非本國籍 大專生 0 碩士生 0 博士生 0 博士後研究員 0 專任助理 0 其他成果 （無法以量化表達之成果如辦理學術活動 、獲得獎項、重要國際合作、研究成果國 際影響力及其他協助產業技術發展之具體 效益事項等，請以文字敘述填列。） 無.
