We present a practical “how-to” guide to help determine whether single-subject fMRI independent components (ICs) characterise structured noise or not. Manual identification of signal and noise after ICA decomposition is required for efficient data denoising: to train supervised algorithms, to check the results of unsupervised ones or to manually clean the data. In this paper we describe the main spatial and temporal features of ICs and provide general guidelines on how to evaluate these. Examples of signal and noise components are provided from a wide range of datasets (3T data, including examples from the UK Biobank and the Human Connectome Project, and 7T data), together with practical guidelines for their identification. Finally, we discuss how the data quality, data type and preprocessing can influence the characteristics of the ICs and present examples of particularly challenging datasets.

BackgroundMyotonic dystrophy type 1 (DM1) represents a multisystemic disorder in which diffuse brain white and gray matter alterations related to clinical and genetic features have been described. We aimed to evaluate in the brain of adult patients with DM1 (i) white and gray matter differences, including cortical-subcortical gray matter volume and cortical thickness and (ii) their correlation with clinical disability, global neuropsychological performance and triplet expansion.MethodsWe included 24 adult genetically-confirmed DM1 patients (14 males; age: 38.5 ± 11.8 years) and 25 age- and sex-matched healthy controls (14 males; age: 38.5 ± 11.3 years) who underwent an identical brain MR protocol including high-resolution 3D T1-weighted, axial T2 FLAIR and DTI sequences. All patients underwent an extensive clinical and neuropsychological evaluation. Voxel-wise analyses of white matter, performed by using Tract Based Spatial Statistics, and of gray matter, with Voxel-based Morphometry and Cortical Thickness, were carried out in order to test for differences between patients with DM1 and healthy controls (p < 0.05, corrected). The correlation between MRI measures and clinical-genetic features was also assessed.ResultsPatients with DM1 showed widespread abnormalities of all DTI parameters in the white matter, which were associated with reduced gray matter volume in all brain lobes and thinning in parieto-temporo-occipital cortices, albeit with less extensive cortical alterations when congenital cases were removed from the analyses. White matter alterations correlated with clinical disability, global cognitive performance and triplet expansions.ConclusionIn patients with DM1, the combined smaller overall gray matter volume and white matter alterations seem to be the main morpho-structural substrates of CNS involvement in this condition. The correlation of white matter differences with both clinical and genetic findings lends support to this notion.

BackgroundAdvanced brain MR techniques are useful tools for differentiating Progressive Supranuclear Palsy from Parkinson's disease, although time-consuming and unlikely to be used all together in routine clinical work. We aimed to compare the diagnostic accuracy of quantitative morphometric, volumetric and DTI metrics for differentiating Progressive Supranuclear Palsy-Richardson's Syndrome from Parkinson's disease.Methods23 Progressive Supranuclear Palsy-Richardson's Syndrome and 42 Parkinson's disease patients underwent a standardized 1.5T brain MR protocol comprising high-resolution T1W1 and DTI sequences. Brainstem and cerebellar peduncles morphometry, automated volumetric analysis of brain deep gray matter and DTI metric analyses of specific brain structures were carried out. We determined diagnostic accuracy, sensitivity and specificity of MR-markers with respect to the clinical diagnosis by using univariate receiver operating characteristics curve analyses. Age-adjusted multivariate receiver operating characteristics analyses were then conducted including only MR-markers with a sensitivity and specificity exceeding 80%.ResultsMorphometric markers (midbrain area, pons to midbrain area ratio and MR Parkinsonism Index), DTI parameters (infratentorial structures) and volumetric analysis (thalamus, putamen and pallidus nuclei) presented moderate to high diagnostic accuracy in discriminating Progressive Supranuclear Palsy-Richardson's Syndrome from Parkinson's disease, with midbrain area showing the highest diagnostic accuracy (99%) (mean ± standard deviation: 75.87 ± 16.95 mm2vs 132.45 ± 20.94 mm2, respectively; p < 0.001).ConclusionAlthough several quantitative brain MR markers provided high diagnostic accuracy in differentiating Progressive Supranuclear Palsy-Richardson's Syndrome from Parkinson's disease, the morphometric assessment of midbrain area is the best single diagnostic marker and should be routinely included in the neuroradiological work-up of parkinsonian patients.