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

Central Precocious Puberty in a Child With Metachromatic Leukodystrophy

Metachromatic leucodystrophy (MLD) is a rare inherited lysosomal disorder caused by reduced activity of the enzyme arylsulfatase A with accumulation of sulfatides in the nervous system. We report a female child affected by MLD who developed central precocious puberty (CPP). This association has not been described so far. The proposita, after normal growth and psychomotor development, at age of 30 months presented with a rapidly progressive gait disturbance with frequent falls and with loss of acquired language skills. Magnetic resonance imaging showed leukoencephalopathy. Biochemical blood essays showed a 91% reduction in the arylsulfatase A activity and genetic analysis revealed compound heterozygous mutations of the Arylsulfatase A gene, enabling diagnosis of MLD. Subsequently, the patient had further rapid deterioration of motor and cognitive functions and developed drug-resistant epilepsy. At 4 years and 7 months of age bilateral thelarche occurred. Magnetic resonance imaging showed a small pituitary gland, extensive signal changes of the brain white matter, increased choline, decreased N-acetyl-aspartate and presence of lactate on 1HMR spectroscopy. Pelvic ultrasound demonstrated a slightly augmented uterine longitudinal diameter (42 mm). The gonadotropin-releasing hormone stimulation test revealed a pubertal LH peak of 12.9 UI/l. A diagnosis of CPP was made and treatment with gonadotropin-releasing hormone agonists was initiated, with good response. In conclusion, a CPP may occur in MLD as in other metabolic diseases with white matter involvement. We hypothesize that brain accumulation of sulfatides could have interfered with the complex network regulating with the hypothalamic-pituitary axis and thus triggering CPP in our patient

Efficacy of MRI data harmonization in the age of machine learning. A multicenter study across 36 datasets

Pooling publicly-available MRI data from multiple sites allows to assemble extensive groups of subjects, increase statistical power, and promote data reuse with machine learning techniques. The harmonization of multicenter data is necessary to reduce the confounding effect associated with non-biological sources of variability in the data. However, when applied to the entire dataset before machine learning, the harmonization leads to data leakage, because information outside the training set may affect model building, and potentially falsely overestimate performance. We propose a 1) measurement of the efficacy of data harmonization; 2) harmonizer transformer, i.e., an implementation of the ComBat harmonization allowing its encapsulation among the preprocessing steps of a machine learning pipeline, avoiding data leakage. We tested these tools using brain T1-weighted MRI data from 1740 healthy subjects acquired at 36 sites. After harmonization, the site effect was removed or reduced, and we measured the data leakage effect in predicting individual age from MRI data, highlighting that introducing the harmonizer transformer into a machine learning pipeline allows for avoiding data leakage