A resting state network in the motor control circuit of the basal ganglia

Background In the absence of overt stimuli, the brain shows correlated fluctuations in functionally related brain regions. Approximately ten largely independent resting state networks (RSNs) showing this behaviour have been documented to date. Recent studies have reported the existence of an RSN in...

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Veröffentlicht in:	BMC neuroscience Jg. 10; H. 1; S. 137
Hauptverfasser:	Robinson, Simon, Basso, Gianpaolo, Soldati, Nicola, Sailer, Uta, Jovicich, Jorge, Bruzzone, Lorenzo, Kryspin-Exner, Ilse, Bauer, Herbert, Moser, Ewald
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 23.11.2009 BioMed Central Ltd BMC
Schlagworte:	Adult Animal Models Basal Ganglia - physiology Biomedical and Life Sciences Biomedicine Brain Mapping Cerebral Cortex - physiology Female Ganglia Humans Image Processing, Computer-Assisted Magnetic Resonance Imaging Male Middle Aged Nerve Net - physiology Neural Pathways - physiology Neurobiology Neurosciences Patient Selection Physiological aspects Research Article Thalamus - physiology Italy Austria Functional Connectivity Supplementary Motor Area Basal Ganglion Independent Component Analysis Functional Connectivity Analysis
ISSN:	1471-2202, 1471-2202
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Abstract	Background In the absence of overt stimuli, the brain shows correlated fluctuations in functionally related brain regions. Approximately ten largely independent resting state networks (RSNs) showing this behaviour have been documented to date. Recent studies have reported the existence of an RSN in the basal ganglia - albeit inconsistently and without the means to interpret its function. Using two large study groups with different resting state conditions and MR protocols, the reproducibility of the network across subjects, behavioural conditions and acquisition parameters is assessed. Independent Component Analysis (ICA), combined with novel analyses of temporal features, is applied to establish the basis of signal fluctuations in the network and its relation to other RSNs. Reference to prior probabilistic diffusion tractography work is used to identify the basal ganglia circuit to which these fluctuations correspond. Results An RSN is identified in the basal ganglia and thalamus, comprising the pallidum, putamen, subthalamic nucleus and substantia nigra, with a projection also to the supplementary motor area. Participating nuclei and thalamo-cortical connection probabilities allow this network to be identified as the motor control circuit of the basal ganglia. The network was reproducibly identified across subjects, behavioural conditions (fixation, eyes closed), field strength and echo-planar imaging parameters. It shows a frequency peak at 0.025 ± 0.007 Hz and is most similar in spectral composition to the Default Mode (DM), a network of regions that is more active at rest than during task processing. Frequency features allow the network to be classified as an RSN rather than a physiological artefact. Fluctuations in this RSN are correlated with those in the task-positive fronto-parietal network and anticorrelated with those in the DM, whose hemodynamic response it anticipates. Conclusion Although the basal ganglia RSN has not been reported in most ICA-based studies using a similar methodology, we demonstrate that it is reproducible across subjects, common resting state conditions and imaging parameters, and show that it corresponds with the motor control circuit. This characterisation of the basal ganglia network opens a potential means to investigate the motor-related neuropathologies in which the basal ganglia are involved.
AbstractList	Background In the absence of overt stimuli, the brain shows correlated fluctuations in functionally related brain regions. Approximately ten largely independent resting state networks (RSNs) showing this behaviour have been documented to date. Recent studies have reported the existence of an RSN in the basal ganglia - albeit inconsistently and without the means to interpret its function. Using two large study groups with different resting state conditions and MR protocols, the reproducibility of the network across subjects, behavioural conditions and acquisition parameters is assessed. Independent Component Analysis (ICA), combined with novel analyses of temporal features, is applied to establish the basis of signal fluctuations in the network and its relation to other RSNs. Reference to prior probabilistic diffusion tractography work is used to identify the basal ganglia circuit to which these fluctuations correspond. Results An RSN is identified in the basal ganglia and thalamus, comprising the pallidum, putamen, subthalamic nucleus and substantia nigra, with a projection also to the supplementary motor area. Participating nuclei and thalamo-cortical connection probabilities allow this network to be identified as the motor control circuit of the basal ganglia. The network was reproducibly identified across subjects, behavioural conditions (fixation, eyes closed), field strength and echo-planar imaging parameters. It shows a frequency peak at 0.025 [+ -] 0.007 Hz and is most similar in spectral composition to the Default Mode (DM), a network of regions that is more active at rest than during task processing. Frequency features allow the network to be classified as an RSN rather than a physiological artefact. Fluctuations in this RSN are correlated with those in the task-positive fronto-parietal network and anticorrelated with those in the DM, whose hemodynamic response it anticipates. Conclusion Although the basal ganglia RSN has not been reported in most ICA-based studies using a similar methodology, we demonstrate that it is reproducible across subjects, common resting state conditions and imaging parameters, and show that it corresponds with the motor control circuit. This characterisation of the basal ganglia network opens a potential means to investigate the motor-related neuropathologies in which the basal ganglia are involved. Background In the absence of overt stimuli, the brain shows correlated fluctuations in functionally related brain regions. Approximately ten largely independent resting state networks (RSNs) showing this behaviour have been documented to date. Recent studies have reported the existence of an RSN in the basal ganglia - albeit inconsistently and without the means to interpret its function. Using two large study groups with different resting state conditions and MR protocols, the reproducibility of the network across subjects, behavioural conditions and acquisition parameters is assessed. Independent Component Analysis (ICA), combined with novel analyses of temporal features, is applied to establish the basis of signal fluctuations in the network and its relation to other RSNs. Reference to prior probabilistic diffusion tractography work is used to identify the basal ganglia circuit to which these fluctuations correspond. Results An RSN is identified in the basal ganglia and thalamus, comprising the pallidum, putamen, subthalamic nucleus and substantia nigra, with a projection also to the supplementary motor area. Participating nuclei and thalamo-cortical connection probabilities allow this network to be identified as the motor control circuit of the basal ganglia. The network was reproducibly identified across subjects, behavioural conditions (fixation, eyes closed), field strength and echo-planar imaging parameters. It shows a frequency peak at 0.025 ± 0.007 Hz and is most similar in spectral composition to the Default Mode (DM), a network of regions that is more active at rest than during task processing. Frequency features allow the network to be classified as an RSN rather than a physiological artefact. Fluctuations in this RSN are correlated with those in the task-positive fronto-parietal network and anticorrelated with those in the DM, whose hemodynamic response it anticipates. Conclusion Although the basal ganglia RSN has not been reported in most ICA-based studies using a similar methodology, we demonstrate that it is reproducible across subjects, common resting state conditions and imaging parameters, and show that it corresponds with the motor control circuit. This characterisation of the basal ganglia network opens a potential means to investigate the motor-related neuropathologies in which the basal ganglia are involved. Abstract Background In the absence of overt stimuli, the brain shows correlated fluctuations in functionally related brain regions. Approximately ten largely independent resting state networks (RSNs) showing this behaviour have been documented to date. Recent studies have reported the existence of an RSN in the basal ganglia - albeit inconsistently and without the means to interpret its function. Using two large study groups with different resting state conditions and MR protocols, the reproducibility of the network across subjects, behavioural conditions and acquisition parameters is assessed. Independent Component Analysis (ICA), combined with novel analyses of temporal features, is applied to establish the basis of signal fluctuations in the network and its relation to other RSNs. Reference to prior probabilistic diffusion tractography work is used to identify the basal ganglia circuit to which these fluctuations correspond. Results An RSN is identified in the basal ganglia and thalamus, comprising the pallidum, putamen, subthalamic nucleus and substantia nigra, with a projection also to the supplementary motor area. Participating nuclei and thalamo-cortical connection probabilities allow this network to be identified as the motor control circuit of the basal ganglia. The network was reproducibly identified across subjects, behavioural conditions (fixation, eyes closed), field strength and echo-planar imaging parameters. It shows a frequency peak at 0.025 ± 0.007 Hz and is most similar in spectral composition to the Default Mode (DM), a network of regions that is more active at rest than during task processing. Frequency features allow the network to be classified as an RSN rather than a physiological artefact. Fluctuations in this RSN are correlated with those in the task-positive fronto-parietal network and anticorrelated with those in the DM, whose hemodynamic response it anticipates. Conclusion Although the basal ganglia RSN has not been reported in most ICA-based studies using a similar methodology, we demonstrate that it is reproducible across subjects, common resting state conditions and imaging parameters, and show that it corresponds with the motor control circuit. This characterisation of the basal ganglia network opens a potential means to investigate the motor-related neuropathologies in which the basal ganglia are involved. In the absence of overt stimuli, the brain shows correlated fluctuations in functionally related brain regions. Approximately ten largely independent resting state networks (RSNs) showing this behaviour have been documented to date. Recent studies have reported the existence of an RSN in the basal ganglia - albeit inconsistently and without the means to interpret its function. Using two large study groups with different resting state conditions and MR protocols, the reproducibility of the network across subjects, behavioural conditions and acquisition parameters is assessed. Independent Component Analysis (ICA), combined with novel analyses of temporal features, is applied to establish the basis of signal fluctuations in the network and its relation to other RSNs. Reference to prior probabilistic diffusion tractography work is used to identify the basal ganglia circuit to which these fluctuations correspond.BACKGROUNDIn the absence of overt stimuli, the brain shows correlated fluctuations in functionally related brain regions. Approximately ten largely independent resting state networks (RSNs) showing this behaviour have been documented to date. Recent studies have reported the existence of an RSN in the basal ganglia - albeit inconsistently and without the means to interpret its function. Using two large study groups with different resting state conditions and MR protocols, the reproducibility of the network across subjects, behavioural conditions and acquisition parameters is assessed. Independent Component Analysis (ICA), combined with novel analyses of temporal features, is applied to establish the basis of signal fluctuations in the network and its relation to other RSNs. Reference to prior probabilistic diffusion tractography work is used to identify the basal ganglia circuit to which these fluctuations correspond.An RSN is identified in the basal ganglia and thalamus, comprising the pallidum, putamen, subthalamic nucleus and substantia nigra, with a projection also to the supplementary motor area. Participating nuclei and thalamo-cortical connection probabilities allow this network to be identified as the motor control circuit of the basal ganglia. The network was reproducibly identified across subjects, behavioural conditions (fixation, eyes closed), field strength and echo-planar imaging parameters. It shows a frequency peak at 0.025 +/- 0.007 Hz and is most similar in spectral composition to the Default Mode (DM), a network of regions that is more active at rest than during task processing. Frequency features allow the network to be classified as an RSN rather than a physiological artefact. Fluctuations in this RSN are correlated with those in the task-positive fronto-parietal network and anticorrelated with those in the DM, whose hemodynamic response it anticipates.RESULTSAn RSN is identified in the basal ganglia and thalamus, comprising the pallidum, putamen, subthalamic nucleus and substantia nigra, with a projection also to the supplementary motor area. Participating nuclei and thalamo-cortical connection probabilities allow this network to be identified as the motor control circuit of the basal ganglia. The network was reproducibly identified across subjects, behavioural conditions (fixation, eyes closed), field strength and echo-planar imaging parameters. It shows a frequency peak at 0.025 +/- 0.007 Hz and is most similar in spectral composition to the Default Mode (DM), a network of regions that is more active at rest than during task processing. Frequency features allow the network to be classified as an RSN rather than a physiological artefact. Fluctuations in this RSN are correlated with those in the task-positive fronto-parietal network and anticorrelated with those in the DM, whose hemodynamic response it anticipates.Although the basal ganglia RSN has not been reported in most ICA-based studies using a similar methodology, we demonstrate that it is reproducible across subjects, common resting state conditions and imaging parameters, and show that it corresponds with the motor control circuit. This characterisation of the basal ganglia network opens a potential means to investigate the motor-related neuropathologies in which the basal ganglia are involved.CONCLUSIONAlthough the basal ganglia RSN has not been reported in most ICA-based studies using a similar methodology, we demonstrate that it is reproducible across subjects, common resting state conditions and imaging parameters, and show that it corresponds with the motor control circuit. This characterisation of the basal ganglia network opens a potential means to investigate the motor-related neuropathologies in which the basal ganglia are involved. In the absence of overt stimuli, the brain shows correlated fluctuations in functionally related brain regions. Approximately ten largely independent resting state networks (RSNs) showing this behaviour have been documented to date. Recent studies have reported the existence of an RSN in the basal ganglia - albeit inconsistently and without the means to interpret its function. Using two large study groups with different resting state conditions and MR protocols, the reproducibility of the network across subjects, behavioural conditions and acquisition parameters is assessed. Independent Component Analysis (ICA), combined with novel analyses of temporal features, is applied to establish the basis of signal fluctuations in the network and its relation to other RSNs. Reference to prior probabilistic diffusion tractography work is used to identify the basal ganglia circuit to which these fluctuations correspond. An RSN is identified in the basal ganglia and thalamus, comprising the pallidum, putamen, subthalamic nucleus and substantia nigra, with a projection also to the supplementary motor area. Participating nuclei and thalamo-cortical connection probabilities allow this network to be identified as the motor control circuit of the basal ganglia. The network was reproducibly identified across subjects, behavioural conditions (fixation, eyes closed), field strength and echo-planar imaging parameters. It shows a frequency peak at 0.025 +/- 0.007 Hz and is most similar in spectral composition to the Default Mode (DM), a network of regions that is more active at rest than during task processing. Frequency features allow the network to be classified as an RSN rather than a physiological artefact. Fluctuations in this RSN are correlated with those in the task-positive fronto-parietal network and anticorrelated with those in the DM, whose hemodynamic response it anticipates. Although the basal ganglia RSN has not been reported in most ICA-based studies using a similar methodology, we demonstrate that it is reproducible across subjects, common resting state conditions and imaging parameters, and show that it corresponds with the motor control circuit. This characterisation of the basal ganglia network opens a potential means to investigate the motor-related neuropathologies in which the basal ganglia are involved.
ArticleNumber	137
Audience	Academic
Author	Basso, Gianpaolo Robinson, Simon Sailer, Uta Soldati, Nicola Kryspin-Exner, Ilse Moser, Ewald Bauer, Herbert Jovicich, Jorge Bruzzone, Lorenzo
AuthorAffiliation	3 Faculty of Psychology, University of Vienna, Liebiggasse 5, 1010 Vienna, Austria 2 Telecommunication Engineering, University of Trento, Trento, Italy 1 Functional Neuroimaging Laboratory, Center for Mind/Brain Sciences, University of Trento, Trento, Italy 5 Center for Biomedical Engineering and Physics, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria 4 MR Center of Excellence, Medical University of Vienna, Lazarettgasse 14, 1090 Vienna, Austria
AuthorAffiliation_xml	– name: 4 MR Center of Excellence, Medical University of Vienna, Lazarettgasse 14, 1090 Vienna, Austria – name: 3 Faculty of Psychology, University of Vienna, Liebiggasse 5, 1010 Vienna, Austria – name: 2 Telecommunication Engineering, University of Trento, Trento, Italy – name: 5 Center for Biomedical Engineering and Physics, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria – name: 1 Functional Neuroimaging Laboratory, Center for Mind/Brain Sciences, University of Trento, Trento, Italy
Author_xml	– sequence: 1 givenname: Simon surname: Robinson fullname: Robinson, Simon email: simon.robinson@meduniwien.ac.at organization: Functional Neuroimaging Laboratory, Center for Mind/Brain Sciences, University of Trento, MR Center of Excellence, Medical University of Vienna – sequence: 2 givenname: Gianpaolo surname: Basso fullname: Basso, Gianpaolo organization: Functional Neuroimaging Laboratory, Center for Mind/Brain Sciences, University of Trento – sequence: 3 givenname: Nicola surname: Soldati fullname: Soldati, Nicola organization: Telecommunication Engineering, University of Trento – sequence: 4 givenname: Uta surname: Sailer fullname: Sailer, Uta organization: Faculty of Psychology, University of Vienna – sequence: 5 givenname: Jorge surname: Jovicich fullname: Jovicich, Jorge organization: Functional Neuroimaging Laboratory, Center for Mind/Brain Sciences, University of Trento – sequence: 6 givenname: Lorenzo surname: Bruzzone fullname: Bruzzone, Lorenzo organization: Telecommunication Engineering, University of Trento – sequence: 7 givenname: Ilse surname: Kryspin-Exner fullname: Kryspin-Exner, Ilse organization: Faculty of Psychology, University of Vienna – sequence: 8 givenname: Herbert surname: Bauer fullname: Bauer, Herbert organization: Faculty of Psychology, University of Vienna – sequence: 9 givenname: Ewald surname: Moser fullname: Moser, Ewald organization: MR Center of Excellence, Medical University of Vienna, Center for Biomedical Engineering and Physics, Medical University of Vienna
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/19930640$$D View this record in MEDLINE/PubMed
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Keywords	Functional Connectivity Supplementary Motor Area Basal Ganglion Independent Component Analysis Functional Connectivity Analysis
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Snippet	Background In the absence of overt stimuli, the brain shows correlated fluctuations in functionally related brain regions. Approximately ten largely... In the absence of overt stimuli, the brain shows correlated fluctuations in functionally related brain regions. Approximately ten largely independent resting... Background In the absence of overt stimuli, the brain shows correlated fluctuations in functionally related brain regions. Approximately ten largely... Abstract Background In the absence of overt stimuli, the brain shows correlated fluctuations in functionally related brain regions. Approximately ten largely...
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SubjectTerms	Adult Animal Models Basal Ganglia - physiology Biomedical and Life Sciences Biomedicine Brain Mapping Cerebral Cortex - physiology Female Ganglia Humans Image Processing, Computer-Assisted Magnetic Resonance Imaging Male Middle Aged Nerve Net - physiology Neural Pathways - physiology Neurobiology Neurosciences Patient Selection Physiological aspects Research Article Thalamus - physiology
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Title	A resting state network in the motor control circuit of the basal ganglia
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