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

Spatiotemporal dynamics of attentional orienting and reorienting revealed by fast optical imaging in occipital and parietal cortices

The mechanisms of visuospatial attention are mediated by two distinct fronto-parietal networks: a bilateral dorsal network (DAN), involved in the voluntary orientation of visuospatial attention, and a ventral network (VAN), lateralized to the right hemisphere, involved in the reorienting of attention to unexpected, but relevant, stimuli. The present study consisted of two aims: 1) characterize the spatio-temporal dynamics of attention and 2) examine the predictive interactions between and within the two attention systems along with visual areas, by using fast optical imaging combined with Granger causality. Data were collected from young healthy participants performing a discrimination task in a Posner-like paradigm. Functional analyses revealed bilateral dorsal parietal (i.e. dorsal regions included in the DAN) and visual recruitment during orienting, highlighting a recursive predictive interplay between specific dorsal parietal regions and visual cortex. Moreover, we found that both attention networks are active during reorienting, together with visual cortex, highlighting a mutual interaction among dorsal and visual areas, which, in turn, predicts subsequent ventral activity. For attentional reorienting our findings indicate that dorsal and visual areas encode disengagement of attention from the attended location and trigger reorientation to the unexpected location. Ventral network activity could instead reflect post-perceptual maintenance of the internal model to generate and keep updated task-related expectations

Analysis of the flows and of the factors that impact the accessibility and identity of public spaces

The territory and the contemporary city require a dynamic approach capable of embracing the life cycles of the different functions and their relationships. The article discusses tools developed in three case studies in which some factors that determine the evolution of tourist and healthcare functions in a historical centre and recreational functions in a protected natural park can be observed and interpreted. The research presented aims to provide the territorial conservation, transformation and enhancement project with tools capable of interacting with new social demands for inclusiveness, identity, accessibility and sharing, favouring new forms of administration as well as participation and collaboration between different knowledge and skill sets

Conversion of the Native N-Terminal Domain of TDP-43 into a Monomeric Alternative Fold with Lower Aggregation Propensity.

TAR DNA-binding protein 43 (TDP-43) forms intraneuronal cytoplasmic inclusions associated with amyotrophic lateral sclerosis and ubiquitin-positive frontotemporal lobar degeneration. Its N-terminal domain (NTD) can dimerise/oligomerise with the head-to-tail arrangement, which is essential for function but also favours liquid-liquid phase separation and inclusion formation of full-length TDP-43. Using various biophysical approaches, we identified an alternative conformational state of NTD in the presence of Sulfobetaine 3-10 (SB3-10), with higher content of α-helical structure and tryptophan solvent exposure. NMR shows a highly mobile structure, with partially folded regions and β-sheet content decrease, with a concomitant increase of α-helical structure. It is monomeric and reverts to native oligomeric NTD upon SB3-10 dilution. The equilibrium GdnHCl-induced denaturation shows a cooperative folding and a somewhat lower conformational stability. When the aggregation processes were compared with and without pre-incubation with SB3-10, but at the identical final SB3-10 concentration, a slower aggregation was found in the former case, despite the reversible attainment of the native conformation in both cases. This was attributed to protein monomerization and oligomeric seeds disruption by the conditions promoting the alternative conformation. Overall, the results show a high plasticity of TDP-43 NTD and identify strategies to monomerise TDP-43 NTD for methodological and biomedical applications

Prediction of the information processing speed performance in multiple sclerosis using a machine learning approach in a large multicenter magnetic resonance imaging data set

Many patients with multiple sclerosis (MS) experience information processing speed (IPS) deficits, and the Symbol Digit Modalities Test (SDMT) has been recommended as a valid screening test. Magnetic resonance imaging (MRI) has markedly improved the understanding of the mechanisms associated with cognitive deficits in MS. However, which structural MRI markers are the most closely related to cognitive performance is still unclear. We used the multicenter 3T-MRI data set of the Italian Neuroimaging Network Initiative to extract multimodal data (i.e., demographic, clinical, neuropsychological, and structural MRIs) of 540 MS patients. We aimed to assess, through machine learning techniques, the contribution of brain MRI structural volumes in the prediction of IPS deficits when combined with demographic and clinical features. We trained and tested the eXtreme Gradient Boosting (XGBoost) model following a rigorous validation scheme to obtain reliable generalization performance. We carried out a classification and a regression task based on SDMT scores feeding each model with different combinations of features. For the classification task, the model trained with thalamus, cortical gray matter, hippocampus, and lesions volumes achieved an area under the receiver operating characteristic curve of 0.74. For the regression task, the model trained with cortical gray matter and thalamus volumes, EDSS, nucleus accumbens, lesions, and putamen volumes, and age reached a mean absolute error of 0.95. In conclusion, our results confirmed that damage to cortical gray matter and relevant deep and archaic gray matter structures, such as the thalamus and hippocampus, is among the most relevant predictors of cognitive performance in MS

Deep Learning in Neuroimaging: Effect of Data Leakage in Cross-validation Using 2D Convolutional Neural Networks

In recent years, 2D convolutional neural networks (CNNs) have been extensively used to diagnose neurological diseases from magnetic resonance imaging (MRI) data due to their potential to discern subtle and intricate patterns. Despite the high performances reported in numerous studies, developing CNN models with good generalization abilities is still a challenging task due to possible data leakage introduced during cross-validation (CV). In this study, we quantitatively assessed the effect of a data leakage caused by 3D MRI data splitting based on a 2D slice-level using three 2D CNN models to classify patients with Alzheimer’s disease (AD) and Parkinson’s disease (PD). Our experiments showed that slice-level CV erroneously boosted the average slice level accuracy on the test set by 30% on Open Access Series of Imaging Studies (OASIS), 29% on Alzheimer’s Disease Neuroimaging Initiative (ADNI), 48% on Parkinson’s Progression Markers Initiative (PPMI) and 55% on a local de-novo PD Versilia dataset. Further tests on a randomly labeled OASIS-derived dataset produced about 96% of (erroneous) accuracy (slice-level split) and 50% accuracy (subject-level split), as expected from a randomized experiment. Overall, the extent of the effect of an erroneous slice-based CV is severe, especially for small datasets

Left egocentric neglect in early subacute right-stroke patients is related to damage of the superior longitudinal fasciculus.

A typical consequence of stroke in the right hemisphere is unilateral spatial neglect. Distinct forms of neglect have been described, such as space-based (egocentric) and object-based (allocentric) neglect. However, the relationship between these two forms of neglect is still far from being understood, as well as their neural substrates. Here, we further explore this issue by using voxel lesion symptoms mapping (VLSM) analyses on a large sample of early subacute right-stroke patients assessed with the Apples Cancellation Test. This is a sensitive test that simultaneously measures both egocentric and allocentric neglect. Behaviourally, we found no correlation between egocentric and allocentric performance, indicating independent mechanisms supporting the two forms of neglect. This was confirmed by the VLSM analysis that pointed out a link between a damage in the superior longitudinal fasciculus and left egocentric neglect. By contrast, no association was found between brain damage and left allocentric neglect. These results indicate a higher probability to observe egocentric neglect as a consequence of white matter damages in the superior longitudinal fasciculus, while allocentric neglect appears more "globally" related to the whole lesion map. Overall, these findings on early subacute right-stroke patients highlight the role played by white matter integrity in sustaining attention-related operations within an egocentric frame of reference

Fractal dimension of the cortical gray matter outweighs other brain MRI features as a predictor of transition to dementia in patients with mild cognitive impairment and leukoaraiosis

BackgroundThe relative contribution of changes in the cerebral white matter (WM) and cortical gray matter (GM) to the transition to dementia in patients with mild cognitive impairment (MCI) is not yet established. In this longitudinal study, we aimed to analyze MRI features that may predict the transition to dementia in patients with MCI and T2 hyperintensities in the cerebral WM, also known as leukoaraiosis.MethodsSixty-four participants with MCI and moderate to severe leukoaraiosis underwent baseline MRI examinations and annual neuropsychological testing over a 2 year period. The diagnosis of dementia was based on established criteria. We evaluated demographic, neuropsychological, and several MRI features at baseline as predictors of the clinical transition. The MRI features included visually assessed MRI features, such as the number of lacunes, microbleeds, and dilated perivascular spaces, and quantitative MRI features, such as volumes of the cortical GM, hippocampus, T2 hyperintensities, and diffusion indices of the cerebral WM. Additionally, we examined advanced quantitative features such as the fractal dimension (FD) of cortical GM and WM, which represents an index of tissue structural complexity derived from 3D-T1 weighted images. To assess the prediction of transition to dementia, we employed an XGBoost-based machine learning system using SHapley Additive exPlanations (SHAP) values to provide explainability to the machine learning model.ResultsAfter 2 years, 18 (28.1%) participants had transitioned from MCI to dementia. The area under the receiving operator characteristic curve was 0.69 (0.53, 0.85) [mean (90% confidence interval)]. The cortical GM-FD emerged as the top-ranking predictive feature of transition. Furthermore, aggregated quantitative neuroimaging features outperformed visually assessed MRI features in predicting conversion to dementia.DiscussionOur findings confirm the complementary roles of cortical GM and WM changes as underlying factors in the development of dementia in subjects with MCI and leukoaraiosis. FD appears to be a biomarker potentially more sensitive than other brain features