A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series

Patel, Ameera X.; Kundu, Prantik; Rubinov, Mikail; Jones, P Simon; Vértes, Petra E.; Ersche, Karen D.; Suckling, John; Bullmore, Edward T.

doi:10.1016/j.neuroimage.2014.03.012

Cited by 341 publications

(330 citation statements)

References 29 publications

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“…The construction of brain graphs from any modality of neuroimaging data entails multiple methodological choices about preprocessing and analysis that could influence the pattern of results. We have addressed this issue by testing that key results are robust to contemporary standards for correction of head motion (35)(36)(37) and to reasonable variation in other analysis steps, including choice of parcellation, wavelet scale, and connection density.…”

Section: Discussionmentioning

confidence: 99%

“…The preprocessing procedures for the fMRI datasets in native space included slice-timing correction; motion correction to the first volume with rigid-body alignment; obliquity transform to the structural MR image; spatial smoothing within functional mask with a 6-mm at full-width at half-maximum Gaussian kernel; intensity normalization to a whole brain median of 1,000 (35,37); wavelet despike (removing signal transients related to small amplitude (<1 mm) head movements) (37) Graph Theoretical Analysis of Network Connections. The absolute wavelet correlation matrices were used to construct binary undirected graphs.…”

Section: Methodsmentioning

confidence: 99%

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Randomization and resilience of brain functional networks as systems-level endophenotypes of schizophrenia

Lo¹,

Huang³

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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Schizophrenia is increasingly conceived as a disorder of brain network organization or dysconnectivity syndrome. Functional MRI (fMRI) networks in schizophrenia have been characterized by abnormally random topology. We tested the hypothesis that network randomization is an endophenotype of schizophrenia and therefore evident also in nonpsychotic relatives of patients. Head movement-corrected, resting-state fMRI data were acquired from 25 patients with schizophrenia, 25 first-degree relatives of patients, and 29 healthy volunteers. Graphs were used to model functional connectivity as a set of edges between regional nodes. We estimated the topological efficiency, clustering, degree distribution, resilience, and connection distance (in millimeters) of each functional network. The schizophrenic group demonstrated significant randomization of global network metrics (reduced clustering, greater efficiency), a shift in the degree distribution to a more homogeneous form (fewer hubs), a shift in the distance distribution (proportionally more long-distance edges), and greater resilience to targeted attack on network hubs. The networks of the relatives also demonstrated abnormal randomization and resilience compared with healthy volunteers, but they were typically less topologically abnormal than the patients' networks and did not have abnormal connection distances. We conclude that schizophrenia is associated with replicable and convergent evidence for functional network randomization, and a similar topological profile was evident also in nonpsychotic relatives, suggesting that this is a systems-level endophenotype or marker of familial risk. We speculate that the greater resilience of brain networks may confer some fitness advantages on nonpsychotic relatives that could explain persistence of this endophenotype in the population.psychosis | dysconnectivity | graph theory | brain network | hubs S chizophrenia is increasingly conceived as a brain dysconnectivity syndrome or disorder of brain network organization (1-4). Various methods have been used to demonstrate abnormal structural or functional connectivity between brain regions in patients with schizophrenia. Specifically, several recent studies have used graph theory to measure the topological pattern of connections (or edges) between regional nodes in large-scale networks derived from neuroimaging data (5-12).The results to date of graph theoretical studies of schizophrenia are not entirely consistent, but there is some convergence around the concept of topological randomization (9, 13). For example, human brain networks (and many other complex, real-life networks) generally have a small-world topology that can be understood as intermediate between the regular, highly clustered organization of a lattice and the globally efficient organization of a random graph. Three independent functional MRI (fMRI) studies have shown that the functional brain networks of patients with schizophrenia are relatively shifted toward the random end of this small-world spectrum, i.e., the...

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Randomization and resilience of brain functional networks as systems-level endophenotypes of schizophrenia

Lo¹,

Huang³

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

119

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“…Successful normalization was confirmed via visual inspection using the check registration function in SPM8 and in‐house software that creates whole volume slice montages. Artifact correction included wavelet despiking (Patel et al., 2014). Correlation coefficients were calculated between rsfMRI time courses for each pair of 90 Automated Anatomical Labeling Atlas (AAL) (Tzourio‐Mazoyer et al., 2002) regions of interest (ROIs) and then normalized using Fisher's r‐z transformation.…”

Section: Methodsmentioning

confidence: 99%

Disrupted brain network functional dynamics and hyper‐correlation of structural and functional connectome topology in patients with breast cancer prior to treatment

et al. 2017

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IntroductionSeveral previous studies have demonstrated that cancer chemotherapy is associated with brain injury and cognitive dysfunction. However, evidence suggests that cancer pathogenesis alone may play a role, even in non‐CNS cancers.MethodsUsing a multimodal neuroimaging approach, we measured structural and functional connectome topology as well as functional network dynamics in newly diagnosed patients with breast cancer. Our study involved a novel, pretreatment assessment that occurred prior to the initiation of any cancer therapies, including surgery with anesthesia. We enrolled 74 patients with breast cancer age 29–65 and 50 frequency‐matched healthy female controls who underwent anatomic and resting‐state functional MRI as well as cognitive testing.ResultsCompared to controls, patients with breast cancer demonstrated significantly lower functional network dynamics (p = .046) and cognitive functioning (p < .02, corrected). The breast cancer group also showed subtle alterations in structural local clustering and functional local clustering (p < .05, uncorrected) as well as significantly increased correlation between structural global clustering and functional global clustering compared to controls (p = .03). This hyper‐correlation between structural and functional topologies was significantly associated with cognitive dysfunction (p = .005).ConclusionsOur findings could not be accounted for by psychological distress and suggest that non‐CNS cancer may directly and/or indirectly affect the brain via mechanisms such as tumor‐induced neurogenesis, inflammation, and/or vascular changes, for example. Our results also have broader implications concerning the importance of the balance between structural and functional connectome properties as a potential biomarker of general neurologic deficit.

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“…(2013) and Patel et al. (2014). The first four volumes of each resting state data set were removed to eliminate the nonequilibrium effects of magnetization.…”

Section: Methodsmentioning

confidence: 99%

Reduced specialized processing in psychotic disorder: a graph theoretical analysis of cerebral functional connectivity

et al. 2016

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BackgroundPrevious research has shown that the human brain can be represented as a complex functional network that is characterized by specific topological properties, such as clustering coefficient, characteristic path length, and global/local efficiency. Patients with psychotic disorder may have alterations in these properties with respect to controls, indicating altered efficiency of network organization. This study examined graph theoretical changes in relation to differential genetic risk for the disorder and aimed to identify clinical correlates.MethodsAnatomical and resting‐state MRI brain scans were obtained from 73 patients with psychotic disorder, 83 unaffected siblings, and 72 controls. Topological measures (i.e., clustering coefficient, characteristic path length, and small‐worldness) were used as dependent variables in a multilevel random regression analysis to investigate group differences. In addition, associations with (subclinical) psychotic/cognitive symptoms were examined.ResultsPatients had a significantly lower clustering coefficient compared to siblings and controls, with no difference between the latter groups. No group differences were observed for characteristic path length and small‐worldness. None of the topological properties were associated with (sub)clinical psychotic and cognitive symptoms.ConclusionsThe reduced ability for specialized processing (reflected by a lower clustering coefficient) within highly interconnected brain regions observed in the patient group may indicate state‐related network alterations. There was no evidence for an intermediate phenotype and no evidence for psychopathology‐related alterations.

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A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series

Cited by 341 publications

References 29 publications

Randomization and resilience of brain functional networks as systems-level endophenotypes of schizophrenia

Randomization and resilience of brain functional networks as systems-level endophenotypes of schizophrenia

Disrupted brain network functional dynamics and hyper‐correlation of structural and functional connectome topology in patients with breast cancer prior to treatment

Reduced specialized processing in psychotic disorder: a graph theoretical analysis of cerebral functional connectivity

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