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Brain connectivity analyses
Until recently, most functional magnetic resonance imaging (MRI) studies of brain function were performed during the administration of a task; these studies measured the resulting regional changes in neuronal activity induced by experimental manipulations. However, recent progress has been performed in studying spontaneous brain activity using the so-called resting state functional MRI technique. Numerous studies in healthy volunteers ADDIN EN.CITE ADDIN EN.CITE.DATA 1-7 have demonstrated that resting state functional MRI is able to identify coherent activity patterns in functional brain networks which closely agree with those identified during cognitive tasks or sensory stimulation. This technique may show promise for the study of higher order associative network functionality and its potential abnormalities in pathology ADDIN EN.CITE ADDIN EN.CITE.DATA 8-11 or in altered states of consciousness, ADDIN EN.CITE ADDIN EN.CITE.DATA 12-13 when the subjects are unable to perform a task or to communicate.
There are two main ways to analyze restingstate functional connectivity MRI (rs-fMRI): (1) hypothesis-driven seed-voxel ADDIN EN.CITE Fox200549054905490517Fox, M. D.Snyder, A. Z.Vincent, J. L.Corbetta, M.Van Essen, D. C.Raichle, M. E.Department of Radiology, Washington University, St. Louis, MO 63110, USA. foxm@npg.wustl.eduThe human brain is intrinsically organized into dynamic, anticorrelated functional networksProc Natl Acad Sci U S AProceedings of the National Academy of Sciences of the United States of AmericaProc Natl Acad Sci U S A9673-810227Attention/*physiologyBrain/metabolism/*physiology*Brain MappingElectroencephalographyHumansMagnetic Resonance Imaging*Models, NeurologicalOxygen/*bloodTask Performance and Analysis2005Jul 50027-8424 (Print)15976020http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=15976020 eng4 and (2) data-driven - mainly independent component analysis (ICA) approaches ADDIN EN.CITE McKeown199850985098509817McKeown, M. J.Makeig, S.Brown, G. G.Jung, T. P.Kindermann, S. S.Bell, A. J.Sejnowski, T. J.Howard Hughes Medical Institute, Salk Institute for Biological Studies, La Jolla, California 92186-5800, USA. martin@salk.eduAnalysis of fMRI data by blind separation into independent spatial componentsHum Brain MappHuman brain mappingHum Brain Mapp160-8863*AlgorithmsArtifactsBrain Mapping/*methodsComputer SimulationHead Movements/physiologyHumansLinear ModelsMagnetic Resonance Imaging/*methodsReference ValuesReproducibility of ResultsSignal Processing, Computer-AssistedTime Factors19981065-9471 (Print)9673671http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9673671 eng14 each offering their own advantages and limitations. The seed-voxel approach consists of extracting the blood oxygen level dependent (BOLD) time course from a region of interest and determining the temporal correlation between this signal (seed) and the signal from all other brain voxels. ADDIN EN.CITE Fox200540714071407117Fox, M. D.Snyder, A. Z.Vincent, J. L.Corbetta, M.Van Essen, D. C.Raichle, M. E.Department of Radiology, Washington University, St. Louis, MO 63110, USA. foxm@npg.wustl.eduThe human brain is intrinsically organized into dynamic, anticorrelated functional networksProc Natl Acad Sci U S AProc Natl Acad Sci U S A9673-810227Attention/*physiologyBrain/metabolism/*physiology*Brain MappingComparative StudyElectroencephalographyHumansMagnetic Resonance Imaging*Models, NeurologicalOxygen/*bloodResearch Support, N.I.H., ExtramuralResearch Support, U.S. Gov't, P.H.S.Task Performance and Analysis2005Jul 515976020http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=159760204 To reduce spurious variance unlikely to reflect neuronal activity, the BOLD signal is preprocessed by temporal band-pass filtering, spatial smoothing, and by regressing out of head motion curves, whole brain signal and ventricular and white matter signals. ADDIN EN.CITE Fox200540714071407117Fox, M. D.Snyder, A. Z.Vincent, J. L.Corbetta, M.Van Essen, D. C.Raichle, M. E.Department of Radiology, Washington University, St. Louis, MO 63110, USA. foxm@npg.wustl.eduThe human brain is intrinsically organized into dynamic, anticorrelated functional networksProc Natl Acad Sci U S AProc Natl Acad Sci U S A9673-810227Attention/*physiologyBrain/metabolism/*physiology*Brain MappingComparative StudyElectroencephalographyHumansMagnetic Resonance Imaging*Models, NeurologicalOxygen/*bloodResearch Support, N.I.H., ExtramuralResearch Support, U.S. Gov't, P.H.S.Task Performance and Analysis2005Jul 515976020http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=159760204 This method, which is quite straightforward and gives very intuitive results has been widely adopted and seems to give very consistent results. ADDIN EN.CITE Fox200748974897489717Fox, M. D.Raichle, M. E.Mallinckrodt Institute of Radiology, Washington University School of Medicine, 4525 Scott Avenue, St. Louis, Missouri 63110, USA. foxm@npg.wustl.edu.Spontaneous fluctuations in brain activity observed with functional magnetic resonance imagingNat Rev NeurosciNature reviewsNat Rev Neurosci700-11892007Sep1471-003X (Print)17704812http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17704812 eng3 On the other hand, it has raised some controversial issues mostly related to arbitrary choices that have to be performed in the preprocessing procedures, ADDIN EN.CITE ADDIN EN.CITE.DATA 15-17 a potentially suboptimal correction for physiological noise, ADDIN EN.CITE Soddu200956455645564517Soddu, A.Boly, M.Nir, Y.Noirhomme, Q.Vanhaudenhuyse, A.Demertzi, A.Arzi, A.Ovadia, S.Stanziano, M.Papa, M.Laureys, S.Malach, R.Coma Science Group, Cyclotron Research Centre, University of Liege, Belgium. Andrea.Soddu@ulg.ac.beReaching across the abyss: recent advances in functional magnetic resonance imaging and their potential relevance to disorders of consciousnessProg Brain ResProgress in brain researchProg Brain Res261-7417720091875-7855 (Electronic)19818907http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=19818907 eng18 and some potential for user-dependent bias in the choice of the seed-voxels of interest used to compute correlation patterns.
In contrast to seed-voxel approaches, ICA-based analyses ADDIN EN.CITE McKeown199850985098509817McKeown, M. J.Makeig, S.Brown, G. G.Jung, T. P.Kindermann, S. S.Bell, A. J.Sejnowski, T. J.Howard Hughes Medical Institute, Salk Institute for Biological Studies, La Jolla, California 92186-5800, USA. martin@salk.eduAnalysis of fMRI data by blind separation into independent spatial componentsHum Brain MappHuman brain mappingHum Brain Mapp160-8863*AlgorithmsArtifactsBrain Mapping/*methodsComputer SimulationHead Movements/physiologyHumansLinear ModelsMagnetic Resonance Imaging/*methodsReference ValuesReproducibility of ResultsSignal Processing, Computer-AssistedTime Factors19981065-9471 (Print)9673671http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9673671 eng14 do not require a priori definition of regions of interest. ICA analyzes the entire BOLD dataset and decomposes it into components that are maximally statistically independent. A number of studies have shown that ICA is a powerful tool which can simultaneously extract a variety of different coherent neuronal networks ADDIN EN.CITE ADDIN EN.CITE.DATA 7,19-22 and separate them from other signal modulations such as those induced by head motion or physiological confounds (e.g., cardiac pulsation, respiratory cycle and slow changes in the depth and rate of breathing. ADDIN EN.CITE ADDIN EN.CITE.DATA 23-25 On the other hand, the interpretation of independent component analysis is sometimes less straightforward (it provides some network-level connectivity quantification, rather than a direct measure of correlation between brain regions) and is less efficient than seed-voxel approaches in detecting some patterns of interest such as between-network anticorrelations. A popular approach is now to combine these connectivity measures in the study of resting state BOLD functional MRI fluctuations. ADDIN EN.CITE Seeley200752715271527117Seeley, W. W.Menon, V.Schatzberg, A. F.Keller, J.Glover, G. H.Kenna, H.Reiss, A. L.Greicius, M. D.Department of Neurology, School of Medicine, University of California, San Francisco, San Francisco, California 94143, USa.Dissociable intrinsic connectivity networks for salience processing and executive controlJ NeurosciJ Neurosci2349-56279Adaptation, PhysiologicalAdultAgedFemaleGyrus Cinguli/*physiologyHumansMagnetic Resonance ImagingMaleMemory/physiologyMiddle AgedNerve Net/physiologyPrefrontal Cortex/physiologyReference ValuesThinking/*physiology2007Feb 281529-2401 (Electronic)17329432http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17329432 eng16 Similar results using both approaches provide an additional guarantee that results are not due to the particular analysis method used. Figure 1 in the main manuscript and Supplementary Digital Content 7 explain general principles of seed-voxel based and ICA-based analyses as used in the present study.
Note that rs-fMRI studies assess functional connectivity, i.e., correlation patterns, and not effective connectivity, i.e., causal interactions between distant brain areas. Assessing causal interactions between areas would however require a faster temporal resolution than that of functional MRI. At the same time, functional MRI studies offer the advantage of providing measurements of the whole-brain in the same acquisition, with a spatial resolution allowing precise anatomical localization of the different patterns of correlation. Inferences about effective connectivity from functional neuroimaging studies using positron emission tomography or functional MRI require prior knowledge about anatomical connections between areas. ADDIN EN.CITE Friston199789789789717Friston,K.J.Buechel,C.Fink,G.R.Morris,J.Rolls,E.Dolan,R.J.Psychophysiological and modulatory interactions in neuroimagingNeuroimageNeuroimage218-22961997//26 This anatomical connectivity has increasingly been shown to underlie functional connectivity in resting state functional MRI network patterns. ADDIN EN.CITE ADDIN EN.CITE.DATA 6,27-28 However, current functional MRI studies of anesthesia-induced changes in brain connectivity will ideally be complemented in the future by effective connectivity studies using higher temporal resolution measurement techniques such as high-density electroencephalography. ADDIN EN.CITE Massimini200956635663566317Massimini, M.Boly, M.Casali, A.Rosanova, M.Tononi, G.Department of Clinical Sciences, University of Milan, Milan, Italy. marcello.massimini@unimi.itA perturbational approach for evaluating the brain's capacity for consciousnessProg Brain ResProgress in brain researchProg Brain Res201-1417720091875-7855 (Electronic)
1875-7855 (Linking)19818903http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=19818903 eng29
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