1
|
Betzel RF, Cutts SA, Tanner J, Greenwell SA, Varley T, Faskowitz J, Sporns O. Hierarchical organization of spontaneous co-fluctuations in densely sampled individuals using fMRI. Netw Neurosci 2023; 7:926-949. [PMID: 37781150 PMCID: PMC10473297 DOI: 10.1162/netn_a_00321] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 05/03/2023] [Indexed: 10/03/2023] Open
Abstract
Edge time series decompose functional connectivity into its framewise contributions. Previous studies have focused on characterizing the properties of high-amplitude frames (time points when the global co-fluctuation amplitude takes on its largest value), including their cluster structure. Less is known about middle- and low-amplitude co-fluctuations (peaks in co-fluctuation time series but of lower amplitude). Here, we directly address those questions, using data from two dense-sampling studies: the MyConnectome project and Midnight Scan Club. We develop a hierarchical clustering algorithm to group peak co-fluctuations of all magnitudes into nested and multiscale clusters based on their pairwise concordance. At a coarse scale, we find evidence of three large clusters that, collectively, engage virtually all canonical brain systems. At finer scales, however, each cluster is dissolved, giving way to increasingly refined patterns of co-fluctuations involving specific sets of brain systems. We also find an increase in global co-fluctuation magnitude with hierarchical scale. Finally, we comment on the amount of data needed to estimate co-fluctuation pattern clusters and implications for brain-behavior studies. Collectively, the findings reported here fill several gaps in current knowledge concerning the heterogeneity and richness of co-fluctuation patterns as estimated with edge time series while providing some practical guidance for future studies.
Collapse
Affiliation(s)
- Richard F. Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- Program in Neuroscience, Indiana University, Bloomington, IN, USA
- Cognitive Science Program, Indiana University, Bloomington, IN, USA
- Network Science Institute, Indiana University, Bloomington, IN, USA
| | - Sarah A. Cutts
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- Program in Neuroscience, Indiana University, Bloomington, IN, USA
| | - Jacob Tanner
- Cognitive Science Program, Indiana University, Bloomington, IN, USA
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA
| | - Sarah A. Greenwell
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Thomas Varley
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- Program in Neuroscience, Indiana University, Bloomington, IN, USA
- Cognitive Science Program, Indiana University, Bloomington, IN, USA
- Network Science Institute, Indiana University, Bloomington, IN, USA
| |
Collapse
|
2
|
Huo BB, Zheng MX, Hua XY, Wu JJ, Xing XX, Ma J, Fang M, Xu JG. Effect of aging on the cerebral metabolic mechanism of electroacupuncture treatment in rats with traumatic brain injury. Front Neurosci 2023; 17:1081515. [PMID: 37113153 PMCID: PMC10128857 DOI: 10.3389/fnins.2023.1081515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 03/20/2023] [Indexed: 04/29/2023] Open
Abstract
Objective Aging has great influence on the clinical treatment effect of cerebrovascular diseases, and evidence suggests that the effect may be associated with age-related brain plasticity. Electroacupuncture is an effective alternative treatment for traumatic brain injury (TBI). In the present study, we aimed to explore the effect of aging on the cerebral metabolic mechanism of electroacupuncture to provide new evidence for developing age-specific rehabilitation strategies. Methods Both aged (18 months) and young (8 weeks) rats with TBI were analyzed. Thirty-two aged rats were randomly divided into four groups: aged model, aged electroacupuncture, aged sham electroacupuncture, and aged control group. Similarly, 32 young rats were also divided into four groups: young model, young electroacupuncture, young sham electroacupuncture, and young control group. Electroacupuncture was applied to "Bai hui" (GV20) and "Qu chi" (LI11) for 8 weeks. CatWalk gait analysis was then performed at 3 days pre- and post-TBI, and at 1, 2, 4, and 8 weeks after intervention to observe motor function recovery. Positron emission computed tomography (PET/CT) was performed at 3 days pre- and post-TBI, and at 2, 4, and 8 weeks after intervention to detect cerebral metabolism. Results Gait analysis showed that electroacupuncture improved the forepaw mean intensity in aged rats after 8 weeks of intervention, but after 4 weeks of intervention in young rats. PET/CT revealed increased metabolism in the left (the injured ipsilateral hemisphere) sensorimotor brain areas of aged rats during the electroacupuncture intervention, and increased metabolism in the right (contralateral to injury hemisphere) sensorimotor brain areas of young rats. Results This study demonstrated that aged rats required a longer electroacupuncture intervention duration to improve motor function than that of young rats. The influence of aging on the cerebral metabolism of electroacupuncture treatment was mainly focused on a particular hemisphere.
Collapse
Affiliation(s)
- Bei-Bei Huo
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Mou-Xiong Zheng
- Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, China
- Department of Traumatology and Orthopedics, Yueyang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xu-Yun Hua
- Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, China
- Department of Traumatology and Orthopedics, Yueyang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jia-Jia Wu
- Department of Rehabilitation Medicine, Yueyang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xiang-Xin Xing
- Department of Rehabilitation Medicine, Yueyang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jie Ma
- Department of Rehabilitation Medicine, Yueyang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Min Fang
- Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jian-Guang Xu
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, China
- *Correspondence: Jian-Guang Xu,
| |
Collapse
|
3
|
Chen Y, Li S, Liang X, Zhang J. Differential Alterations to the Metabolic Connectivity of the Cortical and Subcortical Regions in Rat Brain During Ketamine-Induced Unconsciousness. Anesth Analg 2022; 135:1106-1114. [PMID: 35007212 DOI: 10.1213/ane.0000000000005869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
BACKGROUND Ketamine anesthesia increased glucose metabolism in most brain regions compared to another intravenous anesthetic propofol. However, whether the changes in cerebral metabolic networks induced by ketamine share the same mechanism with propofol remains to be explored. The purpose of the present study was to identify specific features of metabolic network in rat brains during ketamine-induced subanesthesia state and anesthesia state compared to awake state. METHODS We acquired fluorodeoxyglucose positron emission tomography (FDG-PET) images in 20 healthy adult Sprague-Dawley rats that were intravenously administrated saline and ketamine to achieve different conscious states: awake (normal saline), subanesthesia (30 mg kg -1 h -1 ), and anesthesia (160 mg kg -1 h -1 ). Based on the FDG-PET data, the alterations in cerebral glucose metabolism and metabolic topography were investigated by graph-theory analysis. RESULTS The baseline metabolism in rat brains was found significantly increased during ketamine-induced subanesthesia and anesthesia. The graph-theory analysis manifested a reduction in metabolism connectivity and network global/local efficiency across cortical regions and an increase across subcortical regions during ketamine-induced anesthesia (nonparametric permutation test: global efficiency between awake and anesthesia, cortex: P = .016, subcortex: P = .015; global efficiency between subanesthesia and anesthesia, subcortex: P = .012). CONCLUSIONS Ketamine broadly increased brain metabolism alongside decreased metabolic connectivity and network efficiency of cortex network. Modulation of these cortical metabolic networks may be a candidate mechanism underlying general anesthesia-induced loss of consciousness.
Collapse
Affiliation(s)
- Yali Chen
- From the Department of Anesthesiology, Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Siyang Li
- School of Life Science and Technology.,Institute of Space Environment and Materiel Science, Harbin Institute of Technology, Harbin, China
| | - Xia Liang
- School of Life Science and Technology.,Institute of Space Environment and Materiel Science, Harbin Institute of Technology, Harbin, China
| | - Jun Zhang
- From the Department of Anesthesiology, Shanghai Cancer Center, Fudan University, Shanghai, China.,Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| |
Collapse
|
4
|
Zhu Z, Zhang Z, Gao X, Feng L, Chen D, Yang Z, Hu S. Individual Brain Metabolic Connectome Indicator Based on Jensen-Shannon Divergence Similarity Estimation Predicts Seizure Outcomes of Temporal Lobe Epilepsy. Front Cell Dev Biol 2022; 9:803800. [PMID: 35310541 PMCID: PMC8926031 DOI: 10.3389/fcell.2021.803800] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 12/15/2021] [Indexed: 01/01/2023] Open
Abstract
Objective: We aimed to use an individual metabolic connectome method, the Jensen-Shannon Divergence Similarity Estimation (JSSE), to characterize the aberrant connectivity patterns and topological alterations of the individual-level brain metabolic connectome and predict the long-term surgical outcomes in temporal lobe epilepsy (TLE). Methods: A total of 128 patients with TLE (63 females, 65 males; 25.07 ± 12.01 years) who underwent Positron emission tomography (PET) with 18F-fluorodeoxyglucose (FDG) imaging were enrolled. Patients were classified either as experiencing seizure recurrence (SZR) or seizure free (SZF) at least 1 year after surgery. Each individual's metabolic brain network was ascertained using the proposed JSSE method. We compared the similarity and difference in the JSSE network and its topological measurements between the two groups. The two groups were then classified by combining the information from connection and topological metrics, which was conducted by the multiple kernel support vector machine. The validation was performed using the nested leave-one-out cross-validation strategy to confirm the performance of the methods. Results: With a median follow-up of 33 months, 50% of patients achieved SZF. No relevant differences in clinical features were found between the two groups except age at onset. The proposed JSSE method showed marked degree reductions in IFGoperc.R, ROL. R, IPL. R, and SMG. R; and betweenness reductions in ORBsup.R and IOG. R; meanwhile, it found increases in the degree analysis of CAL. L and PCL. L, and in the betweenness analysis of PreCG.R, IOG. R, PoCG.R, PCL. L and PCL.R. Exploring consensus significant metabolic connections, we observed that the most involved metabolic motor networks were the INS-TPOmid.L, MTG. R-SMG. R, and MTG. R-IPL.R pathways between the two groups, and yielded another detailed individual pathological connectivity in the PHG. R-CAU.L, PHG. R-HIP.L, TPOmid.L-LING.R, TPOmid.L-DCG.R, MOG. R-MTG.R, MOG. R-ANG.R, and IPL. R-IFGoperc.L pathways. These aberrant functional network measures exhibited ideal classification performance in predicting SZF individuals from SZR ones at a sensitivity of 75.00%, a specificity of 92.79%, and an accuracy of 83.59%. Conclusion: The JSSE method indicator can identify abnormal brain networks in predicting an individual's long-term surgical outcome of TLE, thus potentially constituting a clinically applicable imaging biomarker. The results highlight the biological meaning of the estimated individual brain metabolic connectome.
Collapse
Affiliation(s)
- Zehua Zhu
- Department of Nuclear Medicine, XiangYa Hospital, Changsha, China
| | - Zhimin Zhang
- Department of Blood Transfusion, XiangYa Hospital, Changsha, China
| | - Xin Gao
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, China
| | - Li Feng
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Dengming Chen
- Department of Nuclear Medicine, XiangYa Hospital, Changsha, China
| | - Zhiquan Yang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
| | - Shuo Hu
- Department of Nuclear Medicine, XiangYa Hospital, Changsha, China
- Key Laboratory of Biological Nanotechnology of National Health Commission, Xiangya Hospital, Central South University, Changsha, China
| |
Collapse
|
5
|
Li S, Zheng J, Li D. Precise segmentation of non-enhanced computed tomography in patients with ischemic stroke based on multi-scale U-Net deep network model. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 208:106278. [PMID: 34274610 DOI: 10.1016/j.cmpb.2021.106278] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Accepted: 07/04/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Acute ischemic stroke requires timely diagnosis and thrombolytic therapy, but it is difficult to locate and quantify the lesion site manually. The purpose of this study was to explore a more rapid and effective method for automatic image segmentation of acute ischemic stroke. METHODS The image features of 30 stroke patients were segmented from non-enhanced computed tomography (CT) images using a multi-scale U-Net deep network model. The Dice loss function training model was used to counter the similar imbalance problem in the data. The difference was compared between manual segmentation and automatic segmentation. RESULTS The Dice similarity coefficient based on multi-scale convolution U-Net network segmentation was 0.86±0.04, higher than the Dice based on classic U-Net (0.81±0.07, P=0.001). The lesion contour of automatic segmentation based on multi-scale U-Net was very close to manual segmentation. The error of lesion area is 1.28±0.59 mm2, and the Pearson correlation coefficient was r=0.986 (P<0.01). The motion time of automatic segmentation is less than 20 ms. CONCLUSIONS Multi-scale U-Net deep network model can effectively segment ischemic stroke lesions in non-enhanced CT and meet real-time clinical requirements.
Collapse
Affiliation(s)
- Shaoquan Li
- Department of Neurosurgery, Cangzhou Central Hospital, Hebei 061000, China.
| | - Jianye Zheng
- Department of Neurosurgery, Cangzhou Central Hospital, Hebei 061000, China
| | - Dongjiao Li
- Department of Neurosurgery, Cangzhou Central Hospital, Hebei 061000, China
| |
Collapse
|
6
|
Huo BB, Zheng MX, Hua XY, Shen J, Wu JJ, Xu JG. Metabolic Brain Network Analysis With 18F-FDG PET in a Rat Model of Neuropathic Pain. Front Neurol 2021; 12:566119. [PMID: 34276529 PMCID: PMC8284720 DOI: 10.3389/fneur.2021.566119] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 05/05/2021] [Indexed: 11/16/2022] Open
Abstract
Neuropathic pain has been found to be related to profound reorganization in the function and structure of the brain. We previously demonstrated changes in local brain activity and functional/metabolic connectivity among selected brain regions by using neuroimaging methods. The present study further investigated large-scale metabolic brain network changes in 32 Sprague–Dawley rats with right brachial plexus avulsion injury (BPAI). Graph theory was applied in the analysis of 2-deoxy-2-[18F] fluoro-D-glucose (18F-FDG) PET images. Inter-subject metabolic networks were constructed by calculating correlation coefficients. Global and nodal network properties were calculated and comparisons between pre- and post-BPAI (7 days) status were conducted. The global network properties (including global efficiency, local efficiency and small-world index) and nodal betweenness centrality did not significantly change for all selected sparsity thresholds following BPAI (p > 0.05). As for nodal network properties, both nodal degree and nodal efficiency measures significantly increased in the left caudate putamen, left medial prefrontal cortex, and right caudate putamen (p < 0.001). The right entorhinal cortex showed a different nodal degree (p < 0.05) but not nodal efficiency. These four regions were selected for seed-based metabolic connectivity analysis. Strengthened connectivity was found among these seeds and distributed brain regions including sensorimotor area, cognitive area, and limbic system, etc. (p < 0.05). Our results indicated that the brain had the resilience to compensate for BPAI-induced neuropathic pain. However, the importance of bilateral caudate putamen, left medial prefrontal cortex, and right entorhinal cortex in the network was strengthened, as well as most of their connections with distributed brain regions.
Collapse
Affiliation(s)
- Bei-Bei Huo
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Mou-Xiong Zheng
- Department of Traumatology and Orthopedics, Yueyang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xu-Yun Hua
- Department of Traumatology and Orthopedics, Yueyang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jun Shen
- Department of Orthopedics, Guanghua Hospital of Integrative Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jia-Jia Wu
- Department of Rehabilitation Medicine, Yueyang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jian-Guang Xu
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| |
Collapse
|
7
|
Early changes in brain network topology and activation of affective pathways predict persistent pain in the rat. Pain 2021; 162:45-55. [PMID: 32773593 DOI: 10.1097/j.pain.0000000000002010] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Adaptations in brain communication are associated with multiple pain disorders and are hypothesized to promote the transition from acute to chronic pain. Despite known increases in brain synaptic activity, it is unknown if and how changes in pathways and networks contribute to persistent pain. A tunable rat model that induces transient or persistent temporomandibular joint pain was used to characterize brain network and subcircuit changes when sensitivity is detected in both transient and persistent pain groups and later when sensitivity is present only for the persistent pain group. Brain activity was measured by F-FDG positron emission tomography imaging and used to construct intersubject correlation networks; network connectivity distributions, diagnostics, and community structure were assessed. Activation of subcircuits was tested by structural equation modeling. Findings reveal differences in the brain networks at day 7 between the persistent and transient pain groups, a time when peripheral sensitivity is detected in both groups, but spontaneous pain occurs only in the persistent pain group. At day 7, increased (P ≤ 0.01) clustering, node strength, network segregation, and activation of prefrontal-limbic pathways are observed only in the group that develops persistent pain. Later, increased clustering and node strength are more pronounced with persistent pain, particularly within the limbic system, and decrease when pain resolves. Pretreatment with intra-articular etanercept to attenuate pain confirms that these adaptations are associated with pain onset. Results suggest that early and sustained brain changes can differentiate persistent and transient pain, implying they could be useful as prognostic biomarkers for persistent pain and in identifying therapeutic targets.
Collapse
|
8
|
Huo BB, Shen J, Hua XY, Zheng MX, Lu YC, Wu JJ, Shan CL, Xu JG. Alteration of metabolic connectivity in a rat model of deafferentation pain: a 18F-FDG PET/CT study. J Neurosurg 2020; 132:1295-1303. [PMID: 30835695 DOI: 10.3171/2018.11.jns181815] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Accepted: 11/21/2018] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Refractory deafferentation pain has been evidenced to be related to central nervous system neuroplasticity. In this study, the authors sought to explore the underlying glucose metabolic changes in the brain after brachial plexus avulsion, particularly metabolic connectivity. METHODS Rats with unilateral deafferentation following brachial plexus avulsion, a pain model of deafferentation pain, were scanned by small-animal 2-deoxy-[18F]fluoro-d-glucose (18F-FDG) PET/CT to explore the changes of metabolic connectivity among different brain regions. Thermal withdrawal latency (TWL) and mechanical withdrawal threshold (MWT) of the intact forepaw were also measured for evaluating pain sensitization. Brain metabolic connectivity and TWL were compared from baseline to 1 week after brachial plexus avulsion. RESULTS Alterations of metabolic connectivity occurred not only within the unilateral hemisphere contralateral to the injured forelimb, but also in the other hemisphere and even in the connections between bilateral hemispheres. Metabolic connectivity significantly decreased between sensorimotor-related areas within the left hemisphere (contralateral to the injured forelimb) (p < 0.05), as well as between areas across bilateral hemispheres (p < 0.05). Connectivity between areas within the right hemisphere (ipsilateral to the injured forelimb) significantly increased (p = 0.034). TWL and MWT of the left (intact) forepaw after surgery were significantly lower than those at baseline (p < 0.001). CONCLUSIONS This study revealed that unilateral brachial plexus avulsion facilitates pain sensitization in the opposite limb. A specific pattern of brain metabolic changes occurred in this procedure. Metabolic connectivity reorganized not only in the sensorimotor area corresponding to the affected forelimb, but also in extensive areas involving the bilateral hemispheres. These findings may broaden our understanding of central nervous system changes, as well as provide new information and a potential intervention target for nosogenesis of deafferentation pain.
Collapse
Affiliation(s)
- Bei-Bei Huo
- 1School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine; and
| | - Jun Shen
- 1School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine; and
| | - Xu-Yun Hua
- 1School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine; and
- 3Trauma and Orthopedics, Yueyang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Mou-Xiong Zheng
- 1School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine; and
- 3Trauma and Orthopedics, Yueyang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Ye-Chen Lu
- 1School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine; and
| | - Jia-Jia Wu
- 1School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine; and
- Departments of2Rehabilitation Medicine and
| | - Chun-Lei Shan
- 1School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine; and
- Departments of2Rehabilitation Medicine and
| | - Jian-Guang Xu
- 1School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine; and
- Departments of2Rehabilitation Medicine and
| |
Collapse
|
9
|
Propofol Anesthesia Alters Spatial and Topologic Organization of Rat Brain Metabolism. Anesthesiology 2020; 131:850-865. [PMID: 31343459 DOI: 10.1097/aln.0000000000002876] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
BACKGROUND Loss of consciousness during anesthesia reduces local and global rate of cerebral glucose metabolism. Despite this, the influence of gradual anesthetic-induced changes on consciousness across the entire brain metabolic network has barely been studied. The purpose of the present study was to identify specific cerebral metabolic patterns characteristic of different consciousness/anesthesia states induced by intravenous anesthetic propofol. METHODS At various times, 20 Sprague-Dawley adult rats were intravenously administered three different dosages of propofol to induce different anesthetic states: mild sedation (20 mg · kg · h), deep sedation (40 mg · kg · h), and deep anesthesia (80 mg · kg · h). Using [F]fluorodeoxyglucose positron emission tomography brain imaging, alterations in the spatial pattern of metabolic distribution and metabolic topography were investigated by applying voxel-based spatial covariance analysis and graph-theory analysis. RESULTS Evident reductions were found in baseline metabolism along with altered metabolic spatial distribution during propofol-induced anesthesia. Moreover, graph-theory analysis revealed a disruption in global and local efficiency of the metabolic brain network characterized by decreases in metabolic connectivity and energy efficiency during propofol-induced deep anesthesia (mild sedation global efficiency/local efficiency = 0.6985/0.7190, deep sedation global efficiency/local efficiency = 0.7444/0.7875, deep anesthesia global efficiency/local efficiency = 0.4498/0.6481; mild sedation vs. deep sedation, global efficiency: P = 0.356, local efficiency: P = 0.079; mild sedation vs. deep anesthesia, global efficiency: P < 0.0001, local efficiency: P < 0.0001; deep sedation vs. deep anesthesia, global efficiency: P < 0.0001, local efficiency: P < 0.0001). A strong spatial correlation was also found between cerebral metabolism and metabolic connectivity strength, which decreased significantly with deepening anesthesia level (correlation coefficients: mild sedation, r = 0.55, deep sedation, r = 0.47; deep anesthesia, r = 0.23; P < 0.0001 between the sedation and deep anesthesia groups). CONCLUSIONS The data revealed anesthesia-related alterations in spatial and topologic organization of metabolic brain network, as well as a close relationship between metabolic connectivity and cerebral metabolism during propofol anesthesia. These findings may provide novel insights into the metabolic mechanism of anesthetic-induced loss of consciousness.
Collapse
|