Corrigendum to "Handedness Side and Magnetization Transfer Ratio in the Primary Sensorimotor Cortex Central Sulcus".

[This corrects the article DOI: 10.1155/2019/5610849.].

Handedness Side and Magnetization Transfer Ratio in the Primary Sensorimotor Cortex Central Sulcus.

Left-handers show lower asymmetry in manual ability when compared to right-handers. Unlike right-handers, left-handers do not show larger deactivation of the ipsilateral primary sensorimotor (SM1) cortex on functional magnetic resonance imaging when moving their dominant than their nondominant hand. However, it should be noted that morphometric MRI studies have reported no differences between right-handers and left-handers in volume, thickness, or surface area of the SM1 cortex. In this regard, magnetization transfer (MT) imaging is a technique with the potential to provide information on microstructural organization and macromolecular content of tissue. In particular, MT ratio index of the brain gray matter is assumed to reflect the variable content of afferent or efferent myelinated fibers, with higher MT ratio values being associated with increased fibers number or degree of myelination. The aim of this study was hence to assess, for the first time, through quantitative MT ratio measurements, potential differences in microstructural organization/characteristics of SM1 cortex between left- and right-handers, which could underlay handedness side. Nine left-handed and 9 right-handed healthy subjects, as determined by the Edinburgh handedness inventory, were examined with T1-weighted and MT-weighted imaging on a 3 T scanner. The hands of subjects were kept still during all acquisitions. Using FreeSurfer suite and the automatic anatomical labeling parcellations defined by the Destrieux atlas, we measured MT ratio, as well as cortical thickness, in three regions of interests corresponding to the precentral gyrus, the central sulcus, and the postcentral gyrus in the bilateral SM1 cortex. No significant difference between left- and right-handers was revealed in the thickness of the three partitions of the SM1 cortex. However, left-handers showed a significantly (p = 0.007) lower MT ratio (31.92% ± 0.96%) in the right SM1 central sulcus (i.e., the hand representation area for left-handers) as compared to right-handers (33.28% ± 0.94%). The results of this preliminary study indicate that quantitative MT imaging, unlike conventional morphometric MRI measurements, can be a useful tool to reveal, in SM1 cortex, potential microstructural correlates of handedness side.

Relevance of brain lesion location for cognition in vascular mild cognitive impairment.

BACKGROUND: Vascular mild cognitive impairment (VMCI) is a potentially transitional state between normal aging and vascular dementia. The presence of macroscopic white matter lesions (WML) of moderate or severe extension on brain MRI is the hallmark of the VMCI. OBJECTIVE: To assess the clinical relevance of the frequency of WML in patients with VMCI independently of total lesion volume (LV). METHODS: In this multicenter study, we included 110 patients with VMCI (age: 74.3 ±â€¯6.6 years; sex: 60 women). Cognitive assessment was performed with the VMCI-Tuscany Neuropsychological Battery, which allowed to identify four VMCI groups: amnestic single (n = 9) and multi-domain (n = 76), non-amnestic single- (n = 10) and multi-domain (n = 15). Distribution and frequency of WML on MRI FLAIR images were evaluated with lesion probability map (LPM). Voxelwise statistics was performed with nonparametric permutation tests, controlling for age, sex, slice thickness, center, magnetic field strength, total LV and head size (p < .01, family-wise error-corrected for multiple comparisons across space). RESULTS: LPM of the WML had a fairly symmetric and widespread distribution across brain. A higher frequency of WML along association tracts of the WM such as inferior longitudinal fascicle, inferior fronto-occipital fascicle and superior longitudinal fascicle, was correlated with worst cognitive scores at the Trail Making Test Part A and Copy of the Rey-Osterrieth Complex Figure. The non-amnestic groups showed a higher frequency of WML in the anterior cingulum and superior longitudinal fascicle close to the frontal gyrus. CONCLUSIONS: Our study showed that in patients with VMCI, independently of total LV, the higher frequency of lesions along association tracts of the WM, which mediate intrahemispheric long-range connectivity, is related with psychomotor speed and constructional praxis. Moreover, a prevalence of lesions in the frontal WM seems to characterize VMCI patients with involvement of non-amnestic domains.

DTI-derived indexes of brain WM correlate with cognitive performance in vascular MCI and small-vessel disease. A TBSS study.

Indexes derived from diffusion tensor imaging (DTI) are sensitive to changes of both T2-hyperintense and normal-appearing brain white matter (WM) in elderly subjects with variable cognitive status. We investigated correlations between global cognitive performance and DTI-derived indexes along the WM tracts in the brain of patients with vascular mild cognitive impairment (MCI) and small vessel disease (SVD). Seventy-six patients with vascular MCI and SVD were assessed through Montreal Cognitive Assessment (MoCA) and Mini Mental State Examination (MMSE) test and underwent DTI examination on a 1.5 T MR scanner. We used Tract Based Spatial Statistics (TBSS) to assess voxel-wise in the entire brain the spatial distribution of the correlation between values of fractional anisotropy, mean, axial/radial diffusivity and global cognitive performance as assessed with MoCA and MMSE tests. All correlations were statistically tested with a significant p-value <0.05 using a family-wise error correction for multiple comparisons. The MoCA score significantly correlated with fractional anisotropy (positive correlation) and mean, axial and radial diffusivity (negative correlations) in WM tracts of cerebral hemispheres and corpus callosum, as well as in the intra-thalamic WM tracts and the superior cerebellar peduncle decussation in the midbrain. No significant correlations were observed for MMSE score. Global cognitive performance, as measured by the MoCA score, in patients with vascular MCI and SVD is associated with microstructural changes in WM tracts underlying intra- and inter-hemispheric cerebral, thalamo-cortical and cerebello-thalamic connections.

Histogram analysis of DTI-derived indices reveals pontocerebellar degeneration and its progression in SCA2.

PURPOSE: To assess the potential of histogram metrics of diffusion-tensor imaging (DTI)-derived indices in revealing neurodegeneration and its progression in spinocerebellar ataxia type 2 (SCA2). MATERIALS AND METHODS: Nine SCA2 patients and 16 age-matched healthy controls, were examined twice (SCA2 patients 3.6±0.7 years and controls 3.3±1.0 years apart) on the same 1.5T scanner by acquiring T1-weighted and diffusion-weighted (b-value = 1000 s/mm2) images. Cerebrum and brainstem-cerebellum regions were segmented using FreeSurfer suite. Histogram analysis of DTI-derived indices, including mean diffusivity (MD), fractional anisotropy (FA), axial (AD) / radial (RD) diffusivity and mode of anisotropy (MO), was performed. RESULTS: At baseline, significant differences between SCA2 patients and controls were confined to brainstem-cerebellum. Median values of MD/AD/RD and FA/MO were significantly (p<0.001) higher and lower, respectively, in SCA2 patients (1.11/1.30/1.03×10(-3) mm2/s and 0.14/0.19) than in controls (0.80/1.00/0.70×10(-3) mm2/s and 0.20/0.41). Also, peak location values of MD/AD/RD and FA were significantly (p<0.001) higher and lower, respectively, in SCA2 patients (0.91/1.11/0.81×10(-3) mm2/s and 0.12) than in controls (0.71/0.91/0.63×10(-3) mm2/s and 0.18). Peak height values of FA and MD/AD/RD/MO were significantly (p<0.001) higher and lower, respectively, in SCA2 patients (0.20 and 0.07/0.06/0.07×10(-3) mm2/s/year /0.07) than in controls (0.15 and 0.14/0.11/0.12/×10(-3) mm2/s/year /0.09). The rate of change of MD median values was significantly (p<0.001) higher (i.e., increased) in SCA2 patients (0.010×10(-3) mm2/s/year) than in controls (-0.003×10(-3) mm2/s/year) in the brainstem-cerebellum, whereas no significant difference was found for other indices and in the cerebrum. CONCLUSION: Histogram analysis of DTI-derived indices is a relatively straightforward approach which reveals microstructural changes associated with pontocerebellar degeneration in SCA2 and the median value of MD is capable to track its progression.

Structural Complexity of the Cerebellum and Cerebral Cortex is Reduced in Spinocerebellar Ataxia Type 2.

BACKGROUND AND PURPOSE: Fractal dimension (FD) is an index of structural complexity of cortical gray matter (GM) and white matter (WM). Application of FD to pontocerebellar degeneration has revealed cerebellar changes. However, so far, possible concurrent cerebral changes and progression of changes in brain complexity have not been investigated. METHODS: We computed FD of cerebellar and cerebral cortex and WM derived from longitudinal brain MRI of patients with spinocerebellar ataxia type 2 (SCA2), which is an inherited cause of pontocerebellar degeneration. Nine SCA2 patients and 16 age-matched healthy controls were examined twice (3.6 ± .7 and 3.3 ± 1.0 years apart, respectively) on the same 1.5T MR scanner with T1-weighted imaging. Cortical GM and WM of the cerebrum and cerebellum were segmented using FreeSurfer and FD of these segmentations were computed. RESULTS: At baseline, FD values of cerebellar GM and WM were significantly (P < .001) lower in SCA2 patients (2.48 ± .04 for GM and 1.74 ± .09 for WM) than in controls (2.56 ± .02 for GM and 2.22 ± .19 for WM). Also, FD values of cerebral GM were significantly (P < .05) lower in SCA2 patients (2.39 ± .03) than in controls (2.43 ± .02). No significant differences were observed for FD of the cerebral WM. The rate of change of FD values was not significantly different between SCA2 patients and controls. CONCLUSIONS: The structural complexity of the cerebellum and cerebral cortex is reduced in SCA2 patients. Fractal analysis seems not to be able to demonstrate progression of changes associated with degeneration in SCA2.

Resting state fMRI regional homogeneity correlates with cognition measures in subcortical vascular cognitive impairment.

BACKGROUND: The hyperintensity of cerebral white matter (WM) in T2-weighted MR images of elderly subjects due to small vessel disease (SVD) is associated with variable clinical features including mild cognitive impairment (MCI), also termed subcortical vascular cognitive impairment (SVCI). The latter is typically characterized by psychomotor slowing, attention deficits, and executive dysfunctions. We hypothesized that functional brain changes might be associated with these distinctive cognitive deficits in patients with SVCI. METHODS: Resting-state fMRI (rsfMRI) signal was assessed in conjunction with performance on the Montreal Cognitive Assessment battery (MoCA) and Stroop test in 67 subjects with MCI and moderate to severe extension of cerebral WM T2 hyperintensities qualifying for SVCI. We performed a whole-brain analysis of regional homogeneity (ReHo) of rsfMRI in conjunction with cognitive test scores. RESULTS: We observed a significant (p<0.05) negative association between ReHo and MoCA scores, with higher ReHo in the left posterior cerebellum (crus I) of patients with greater global cognitive impairment, and a significant positive association between ReHo and Stroop scores, with higher ReHo in the middle cingulate cortex bilaterally of patients with worse executive functions. CONCLUSION: ReHo of rsfMRI is significantly correlated with measurements of the cognitive deficits which are distinctive of SVCI. The increased activity could have a maladaptive or compensatory significance towards specific aspects of cognition.

Diffusion Tensor Imaging to Map Brain Microstructural Changes in CADASIL.

BACKGROUND AND PURPOSE: Diffusion tensor imaging (DTI) is sensitive to brain microstructural changes. The aims of this DTI study were to map voxelwise the spatial distribution of brain microstructural changes in patients with cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) and to investigate any correlation between DTI-derived indices and extension of T2 hyperintensity. METHODS: Eighteen patients with CADASIL and 18 age-, sex-, and education-level-matched healthy controls underwent magnetic resonance imaging at 3 T. Differences in DTI-derived indices (mean diffusivity [MD], fractional anisotropy [FA], axial [AD] and radial [RD] diffusivities, and mode of anisotropy [MO]) of brain white matter (WM) between CADASIL patients and healthy subjects were assessed through tract-based spatial statistics. Then, DTI-derived indices were correlated with the patient's score on the extended Fazekas visual scale of the T2 hyperintensity. RESULTS: When compared to healthy controls, CADASIL patients showed extensive symmetric areas of increased MD/RD and decreased AD/FA/MO that involved almost the entire hemispheric cerebral WM (internal and external capsule, WM of the temporal poles, superior and inferior longitudinal fasciculus, inferior frontal-occipital fasciculus, uncinate fasciculus, cingulum, forceps major and minor, corticospinal tracts, and thalamic radiations), thalami, and corpus callosum. Additional areas of increased RD were observed in pons, midbrain, cerebellar peduncles, and cerebellar WM. Only FA was negatively correlated with extended Fazekas visual score. CONCLUSIONS: Our results indicate that brain damage in CADASIL is associated with extensive microstructural changes implying impairment of intra- and inter-hemispheric cerebral, thalamocortical, and cerebrocerebellar connections. Severity of microstructural changes correlates with extension of T2 hyperintensity.

Development of digital phantoms based on a finite element model to simulate low-attenuation areas in CT imaging for pulmonary emphysema quantification.

PURPOSE: To develop an innovative finite element (FE) model of lung parenchyma which simulates pulmonary emphysema on CT imaging. The model is aimed to generate a set of digital phantoms of low-attenuation areas (LAA) images with different grades of emphysema severity. METHODS: Four individual parameter configurations simulating different grades of emphysema severity were utilized to generate 40 FE models using ten randomizations for each setting. We compared two measures of emphysema severity (relative area (RA) and the exponent D of the cumulative distribution function of LAA clusters size) between the simulated LAA images and those computed directly on the models output (considered as reference). RESULTS: The LAA images obtained from our model output can simulate CT-LAA images in subjects with different grades of emphysema severity. Both RA and D computed on simulated LAA images were underestimated as compared to those calculated on the models output, suggesting that measurements in CT imaging may not be accurate in the assessment of real emphysema extent. CONCLUSIONS: Our model is able to mimic the cluster size distribution of LAA on CT imaging of subjects with pulmonary emphysema. The model could be useful to generate standard test images and to design physical phantoms of LAA images for the assessment of the accuracy of indexes for the radiologic quantitation of emphysema.

Prediction of Impaired Performance in Trail Making Test in MCI Patients With Small Vessel Disease Using DTI Data.

Mild cognitive impairment (MCI) is a common condition in patients with diffuse hyperintensities of cerebral white matter (WM) in T2-weighted magnetic resonance images and cerebral small vessel disease (SVD). In MCI due to SVD, the most prominent feature of cognitive impairment lies in degradation of executive functions, i.e., of processes that supervise the organization and execution of complex behavior. The trail making test is a widely employed test sensitive to cognitive processing speed and executive functioning. MCI due to SVD has been hypothesized to be the effect of WM damage, and diffusion tensor imaging (DTI) is a well-established technique for in vivo characterization of WM. We propose a machine learning scheme tailored to 1) predicting the impairment in executive functions in patients with MCI and SVD, and 2) examining the brain substrates of this impairment. We employed data from 40 MCI patients with SVD and created feature vectors by averaging mean diffusivity (MD) and fractional anisotropy maps within 50 WM regions of interest. We trained support vector machines (SVMs) with polynomial as well as radial basis function kernels using different DTI-derived features while simultaneously optimizing parameters in leave-one-out nested cross validation. The best performance was obtained using MD features only and linear kernel SVMs, which were able to distinguish an impaired performance with high sensitivity (72.7%-89.5%), specificity (71.4%-83.3%), and accuracy (77.5%-80.0%). While brain substrates of executive functions are still debated, feature ranking confirm that MD in several WM regions, not limited to the frontal lobes, are truly predictive of executive functions.

Multimodal MRI classification in vascular mild cognitive impairment.

Vascular mild cognitive impairment (VMCI) is a disorder in which multimodal MRI can add significant value by combining diffusion tensor imaging (DTI) with brain morphometry. In this study we implemented and compared machine learning techniques for multimodal classification between 58 VMCI patients and 29 healthy subjects as well as for discrimination (within the VMCI group) between patients with different cognitive performances. For each subject, a cortical feature vector was constructed based on cortical parcellation and cortical and subcortical volumetric segmentation and a DTI feature vector was formed by combining descriptive statistical metrics related to the distribution of DTI invariants within white matter. We employed both a sequential minimal optimization and a functional tree classifier, using feature selection and 10-fold cross-validation, and compared their performances in monomodal and multimodal classification for both classification problems (healthy subjects vs VMCI and prediction of cognitive performance). While monomodal classification resulted in satisfactory performance in most cases, turning from monomodal to multimodal classification resulted in an improvement of the performance in the discrimination between VMCI patients with low cognitive performance and healthy subjects by up to 10% in sensitivity (leaving specificity unchanged). We therefore are able to confirm the usefulness of machine learning techniques in discriminating diseased states based on neuroimaging data.

Development and analysis of a finite element model to simulate pulmonary emphysema in CT imaging.

In CT imaging, pulmonary emphysema appears as lung regions with Low-Attenuation Areas (LAA). In this study we propose a finite element (FE) model of lung parenchyma, based on a 2-D grid of beam elements, which simulates pulmonary emphysema related to smoking in CT imaging. Simulated LAA images were generated through space sampling of the model output. We employed two measurements of emphysema extent: Relative Area (RA) and the exponent D of the cumulative distribution function of LAA clusters size. The model has been used to compare RA and D computed on the simulated LAA images with those computed on the models output. Different mesh element sizes and various model parameters, simulating different physiological/pathological conditions, have been considered and analyzed. A proper mesh element size has been determined as the best trade-off between reliable results and reasonable computational cost. Both RA and D computed on simulated LAA images were underestimated with respect to those calculated on the models output. Such underestimations were larger for RA (≈ -44 ÷ -26%) as compared to those for D (≈ -16 ÷ -2%). Our FE model could be useful to generate standard test images and to design realistic physical phantoms of LAA images for the assessment of the accuracy of descriptors for quantifying emphysema in CT imaging.