The Connectome Mapper: An Open-Source Processing Pipeline to Map Connectomes with MRI

Daducci, Alessandro; Gerhard, Stephan; Griffa, Alessandra; Lemkaddem, Alia; Cammoun, Leila; Gigandet, Xavier; Meuli, Reto; Hagmann, Patric; Thiran, Jean-Philippe

doi:10.1371/journal.pone.0048121

Cited by 286 publications

(301 citation statements)

References 30 publications

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“…19 First, we used affine registration in the eddy_correct tool implemented in software from the Oxford Centre for Functional MRI of the Brain (FSL, FMRIB Software Library, http://www.fmrib. ox.ac.uk/fsl/) to correct each diffusion-weighted image for distortions caused by head motion and eddy currents.…”

Section: Network Construction and Calculation Of Graph Theory Metricsmentioning

confidence: 99%

Machine Learning of DTI Structural Brain Connectomes for Lateralization of Temporal Lobe Epilepsy

Kamiya

Amemiya

Suzuki

et al. 2016

MRMS

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Background and Purpose: We analyzed the ability of a machine learning approach that uses diffusion tensor imaging (DTI) structural connectomes to determine lateralization of epileptogenicity in temporal lobe epilepsy (TLE).Materials and Methods: We analyzed diffusion tensor and 3-dimensional (3D) T 1 -weighted images of 44 patients with TLE (right, 15, left, 29; mean age, 33.0 « 11.6 years) and 14 age-matched controls. We constructed a whole brain structural connectome for each subject, calculated graph theoretical network measures, and used a support vector machine (SVM) for classification among 3 groups (right TLE versus controls, left TLE versus controls, and right TLE versus left TLE) following a feature reduction process with sparse linear regression.Results: In left TLE, we found a significant decrease in local efficiency and the clustering coefficient in several brain regions, including the left posterior cingulate gyrus, left cuneus, and both hippocampi. In right TLE, the right hippocampus showed reduced nodal degree, clustering coefficient, and local efficiency. With use of the leave-one-out crossvalidation strategy, the SVM classifier achieved accuracy of 75.9 to 89.7% for right TLE versus controls, 74.4 to 86.0% for left TLE versus controls, and 72.7 to 86.4% for left TLE versus right TLE.Conclusion: Machine learning of graph theoretical measures from the DTI structural connectome may give support to lateralization of the TLE focus. The present good discrimination between left and right TLE suggests that, with further refinement, the classifier should improve presurgical diagnostic confidence.

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Section: Network Construction and Calculation Of Graph Theory Metricsmentioning

confidence: 99%

Machine Learning of DTI Structural Brain Connectomes for Lateralization of Temporal Lobe Epilepsy

Kamiya

Amemiya

Suzuki

et al. 2016

MRMS

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“…Structural connectome graphs were constructed from the DT images by using the Connectome Mapping Toolkit (version 1.2.0; Ecole Polytechnique Fédérale de Lausanne and Hospital Center and University of Lausanne, Lausanne, Switzerland), with 32 random seeds per voxel, an angular threshold of 60°, and a step size of 0.5 mm (22). The B 0 images from the DT images were registered to the T1-weighted anatomic images by using the Functional Magnetic Resonance Imaging of the Brain linear image registration tool (FLIRT) with the maximization of mutual information method (23).…”

Section: Structural Connectome Constructionmentioning

confidence: 99%

The Alzheimer Structural Connectome: Changes in Cortical Network Topology with Increased Amyloid Plaque Burden

Prescott¹,

Guidon²,

Doraiswamy³

et al. 2016

Radiology

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For the Alzheimer's Disease Neuroimaging InitiativePurpose:To evaluate differences in the structural connectome among patients with normal cognition (NC), mild cognitive impairment (MCI), and Alzheimer disease (AD) and to determine associations between the structural connectome and cortical amyloid deposition. Materials and Methods:Patients enrolled in a multicenter biomarker study (Alzheimer's Disease Neuroimaging Initiative [ADNI] 2) who had both baseline diffusion-tensor (DT) and florbetapir positron emission tomography (PET) data at the time of data analyses in November 2012 were studied. All institutions received institutional review board approval. There were 102 patients in ADNI 2 who met criteria for analysis. Patients' T1-weighted images were automatically parcellated into cortical regions of interest. Standardized uptake value ratio (SUVr) was calculated from florbetapir PET images for composite cortical regions (frontal, cingulate, parietal, and temporal). Structural connectome graphs were created from DT images, and connectome topology was analyzed in each region by using graph theoretical metrics. Analysis of variance of structural connectome metrics and florbetapir SUVr across diagnostic group was performed. Linear mixed-effects models were fit to analyze the effect of florbetapir SUVr on structural connectome metrics. Results:Diagnostic group (NC, MCI, or AD) was associated with changes in weighted structural connectome metrics, with decreases from the NC group to the MCI group to the AD group shown for (a) strength in the bilateral frontal, right parietal, and bilateral temporal regions (P , .05); (b) weighted local efficiency in the left temporal region (P , .05); and (c) weighted clustering coefficient in the bilateral frontal and left temporal regions (P , .05). Increased cortical florbetapir SUVr was associated with decreases in weighted structural connectome metrics; namely, strength (P = .00001), weighted local efficiency (P = .00001), and weighted clustering coefficient (P = .0006), independent of brain region. For every 0.1-unit increase in florbetapir SUVr, there was a 14% decrease in strength, an 11% decrease in weighted local efficiency, and a 9% decrease in weighted clustering coefficient, regardless of the analyzed cortical region or, in the case of weighted local efficiency and clustering coefficient, diagnostic group. Conclusion:Increased amyloid burden, as measured with florbetapir PET imaging, is related to changes in the topology of the large-scale cortical network architecture of the brain, as measured with graph theoretical metrics of DTI tractography, even in the preclinical stages of AD.q RSNA, 2014

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“…DSI, resting state functional MRI (fMRI) and magnetization-prepared rapid acquisition gradient echo data were processed using the Connectome Mapping Toolkit (http: //www.cmtk.org/) 41 and the Connectivity Decoding Toolkit, 42 as described below.…”

Section: Subject Recruitmentmentioning

confidence: 99%

Glutathione deficit impairs myelin maturation: relevance for white matter integrity in schizophrenia patients

et al. 2014

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Schizophrenia pathophysiology implies both abnormal redox control and dysconnectivity of the prefrontal cortex, partly related to oligodendrocyte and myelin impairments. As oligodendrocytes are highly vulnerable to altered redox state, we investigated the interplay between glutathione and myelin. In control subjects, multimodal brain imaging revealed a positive association between medial prefrontal glutathione levels and both white matter integrity and resting-state functional connectivity along the cingulum bundle. In early psychosis patients, only white matter integrity was correlated with glutathione levels. On the other side, in the prefrontal cortex of peripubertal mice with genetically impaired glutathione synthesis, mature oligodendrocyte numbers, as well as myelin markers, were decreased. At the molecular levels, under glutathione-deficit conditions induced by short hairpin RNA targeting the key glutathione synthesis enzyme, oligodendrocyte progenitors showed a decreased proliferation mediated by an upregulation of Fyn kinase activity, reversed by either the antioxidant N-acetylcysteine or Fyn kinase inhibitors. In addition, oligodendrocyte maturation was impaired. Interestingly, the regulation of Fyn mRNA and protein expression was also impaired in fibroblasts of patients deficient in glutathione synthesis. Thus, glutathione and redox regulation have a critical role in myelination processes and white matter maturation in the prefrontal cortex of rodent and human, a mechanism potentially disrupted in schizophrenia.

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The Connectome Mapper: An Open-Source Processing Pipeline to Map Connectomes with MRI

Cited by 286 publications

References 30 publications

Machine Learning of DTI Structural Brain Connectomes for Lateralization of Temporal Lobe Epilepsy

Machine Learning of DTI Structural Brain Connectomes for Lateralization of Temporal Lobe Epilepsy

The Alzheimer Structural Connectome: Changes in Cortical Network Topology with Increased Amyloid Plaque Burden

Glutathione deficit impairs myelin maturation: relevance for white matter integrity in schizophrenia patients

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