References of "NeuroImage: Clinical"
     in
Bookmark and Share    
Full Text
Peer Reviewed
See detailBiased binomial assessment of cross-validated estimation of classification accuracies illustrated in diagnosis predictions
Noirhomme, Quentin ULg; Lesenfants, Damien ULg; Gomez, Francisco et al

in NeuroImage: Clinical (2014), 4

Multivariate classification is used in neuroimaging studies to infer brain activation or in medical applications to infer diagnosis. Their results are often assessed through either a binomial or a ... [more ▼]

Multivariate classification is used in neuroimaging studies to infer brain activation or in medical applications to infer diagnosis. Their results are often assessed through either a binomial or a permutation test. Here, we simulated classification results of generated random data to assess the influence of the cross-validation scheme on the significance of results. Distributions built from classification of random data with crossvalidation did not follow the binomial distribution. The binomial test is therefore not adapted. On the contrary, the permutation test was unaffected by the cross-validation scheme. The influence of the crossvalidation was further illustrated on real-data from a brain–computer interface experiment in patients with disorders of consciousness and from an fMRI study on patients with Parkinson disease. Three out of 16 patients with disorders of consciousness had significant accuracy on binomial testing, but only one showed significant accuracy using permutation testing. In the fMRI experiment, the mental imagery of gait could discriminate significantly between idiopathic Parkinson’s disease patients and healthy subjects according to the permutation test but not according to the binomial test. Hence, binomial testing could lead to biased estimation of significance and false positive or negative results. In our view, permutation testing is thus recommended for clinical application of classification with cross-validation. [less ▲]

Detailed reference viewed: 52 (12 ULg)
Full Text
Peer Reviewed
See detailAltered network properties of the fronto-parietal network and the thalamus in impaired consciousness
Crone, Julia Sophia; Soddu, Andrea ULg; Höller, Yvonne et al

in NeuroImage: Clinical (2013)

Recovery of consciousness has been associated with connectivity in the frontal cortex and parietal regions modulated by the thalamus. To examine this model and to relate alterations to deficits in ... [more ▼]

Recovery of consciousness has been associated with connectivity in the frontal cortex and parietal regions modulated by the thalamus. To examine this model and to relate alterations to deficits in cognitive functioning and conscious processing, we investigated topological network properties in patients with chronic disorders of consciousness recovered from coma. Resting state fMRI data of 34 patients with unresponsive wakefulness syndrome and 25 in minimally conscious state were compared to 28 healthy controls.We investigated global and local network characteristics. Additionally, behavioralmeasureswere correlatedwith the localmetrics of 28 regionswithin the fronto-parietal network and the thalamus. In chronic disorders of consciousness, modularity at the global level was reduced suggesting a disturbance in the optimal balance between segregation and integration.Moreover, network properties were altered in several regionswhich are associatedwith conscious processing (particularly, inmedial parietal, and frontal regions, aswell as in the thalamus). Between minimally conscious and unconscious patients the local efficiency of medial parietal regions differed. Alterations in the thalamus were particularly evident in non-conscious patients.Most of the regions affected in patientswith impaired consciousness belong to the so-called ‘rich club’ of highly interconnected central nodes. Disturbances in their topological characteristics have severe impact on information integration and are reflected in deficits in cognitive functioning probably leading to a total breakdown of consciousness. [less ▲]

Detailed reference viewed: 68 (5 ULg)
Full Text
Peer Reviewed
See detailMulticlass classification of FDG PET scans for the distinction between Parkinson's disease and atypical parkinsonian syndromes
Garraux, Gaëtan ULg; Phillips, Christophe ULg; Schrouff, Jessica ULg et al

in NeuroImage: Clinical (2013), 2

Most available pattern recognition methods in neuroimaging address binary classification problems. Here, we used relevance vector machine (RVM) in combination with booststrap resampling (‘bagging’) for ... [more ▼]

Most available pattern recognition methods in neuroimaging address binary classification problems. Here, we used relevance vector machine (RVM) in combination with booststrap resampling (‘bagging’) for non-hierarchical multiclass classification. The method was tested on 120 cerebral 18fluorodeoxyglucose (FDG) positron emission tomography (PET) scans performed in patients who exhibited parkinsonian clinical features for 3.5 years on average but that were outside the prevailing perception for Parkinson's disease (PD). A radiological diagnosis of PD was suggested for 30 patients at the time of PET imaging. However, at follow-up several years after PET imaging, 42 of them finally received a clinical diagnosis of PD. The remaining 78 APS patients were diagnosed with multiple system atrophy (MSA, N = 31), progressive supranuclear palsy (PSP, N = 26) and corticobasal syndrome (CBS, N = 21), respectively. With respect to this standard of truth, classification sensitivity, specificity, positive and negative predictive values for PD were 93% 83% 75% and 96%, respectively using binary RVM (PD vs. APS) and 90%, 87%, 79% and 94%, respectively, using multiclass RVM (PD vs. MSA vs. PSP vs. CBS). Multiclass RVM achieved 45%, 55% and 62% classification accuracy for, MSA, PSP and CBS, respectively. Finally, a majority confidence ratio was computed for each scan on the basis of class pairs that were the most frequently assigned by RVM. Altogether, the results suggest that automatic multiclass RVM classification of FDG PET scans achieves adequate performance for the early differentiation between PD and APS on the basis of cerebral FDG uptake patterns when the clinical diagnosis is felt uncertain. This approach cannot be recommended yet as an aid for distinction between the three APS classes under consideration. [less ▲]

Detailed reference viewed: 91 (16 ULg)