Assessing the neural correlates of motor and cognitive processes under naturalistic experimentation is challenging due to the movement constraints of traditional brain imaging technologies. The recent advent of portable technologies that are less sensitive to motion artifacts such as Functional Near Infrared Spectroscopy (fNIRS) have been made possible the study of brain function in freely-moving participants. In this paper, we describe a series of proof-of-concept experiments examining the potential of fNIRS in assessing the neural correlates of cognitive and motor processes in unconstrained environments. We show illustrative applications for practicing a sport (i.e. table tennis), playing a musical instrument (i.e. piano and violin) alone or in duo and performing daily activities for many hours (i.e. continuous monitoring). Our results expand upon previous research on the feasibility and robustness of fNIRS to monitor brain haemodynamic changes in different real life settings. We believe that these preliminary results showing the flexibility and robustness of fNIRS measurements may contribute by inspiring future work in the field of applied neuroscience.

The framework of graph theory provides useful tools for investigating the neural substrates of neuropsychiatric disorders. Graph description measures may be useful as predictor variables in classification procedures. Here, we consider several centrality measures as predictor features in a classification algorithm to identify nodes of resting-state networks containing predictive information that can discriminate between typical developing children and patients with attention-deficit/hyperactivity disorder (ADHD). The prediction was based on a support vector machines classifier. The analyses were performed in a multisite and publicly available resting-state fMRI dataset of healthy children and ADHD patients: the ADHD-200 database. Network centrality measures contained little predictive information for the discrimination between ADHD patients and healthy subjects. However, the classification between inattentive and combined ADHD subtypes was more promising, achieving accuracies higher than 65% (balance between sensitivity and specificity) in some sites. Finally, brain regions were ranked according to the amount of discriminant information and the most relevant were mapped. As hypothesized, we found that brain regions in motor, frontoparietal, and default mode networks contained the most predictive information. We concluded that the functional connectivity estimations are strongly dependent on the sample characteristics. Thus different acquisition protocols and clinical heterogeneity decrease the predictive values of the graph descriptors.

Studies based on functional magnetic resonance imaging (fMRI) during the resting state have shown decreased functional connectivity between the dorsal anterior cingulate cortex (dACC) and regions of the Default Mode Network (DMN) in adult patients with Attention-Deficit/Hyperactivity Disorder (ADHD) relative to subjects with typical development (TD). Most studies used Pearson correlation coefficients among the BOLD signals from different brain regions to quantify functional connectivity. Since the Pearson correlation analysis only provides a limited description of functional connectivity, we investigated functional connectivity between the dACC and the posterior cingulate cortex (PCC) in three groups (adult patients with ADHD, n = 21; TD age-matched subjects, n = 21; young TD subjects, n = 21) using a more comprehensive analytical approach – unsupervised machine learning using a one-class support vector machine (OC-SVM) that quantifies an abnormality index for each individual. The median abnormality index for patients with ADHD was greater than for TD age-matched subjects (p = 0.014); the ADHD and young TD indices did not differ significantly (p = 0.480); the median abnormality index of young TD was greater than that of TD age-matched subjects (p = 0.016). Low frequencies below 0.05 Hz and around 0.20 Hz were the most relevant for discriminating between ADHD patients and TD age-matched controls and between the older and younger TD subjects. In addition, we validated our approach using the fMRI data of children publicly released by the ADHD-200 Competition, obtaining similar results. Our findings suggest that the abnormal coherence patterns observed in patients with ADHD in this study resemble the patterns observed in young typically developing subjects, which reinforces the hypothesis that ADHD is associated with brain maturation deficits.

Attention Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder, being one of the most prevalent psychiatric disorders in childhood. The neural substrates associated with this condition, both from structural and functional perspectives, are not yet well established. Recent studies have highlighted the relevance of neuroimaging not only to provide a more solid understanding about the disorder but also for possible clinical support. The ADHD-200 Consortium organized the ADHD-200 global competition making publicly available, hundreds of structural magnetic resonance imaging (MRI) and functional MRI (fMRI) datasets of both ADHD patients and typically developing (TD) controls for research use. In the current study, we evaluate the predictive power of a set of three different feature extraction methods and 10 different pattern recognition methods. The features tested were regional homogeneity (ReHo), amplitude of low frequency fluctuations (ALFF), and independent components analysis maps (resting state networks; RSN). Our findings suggest that the combination ALFF+ReHo maps contain relevant information to discriminate ADHD patients from TD controls, but with limited accuracy. All classifiers provided almost the same performance in this case. In addition, the combination ALFF+ReHo+RSN was relevant in combined vs. inattentive ADHD classification, achieving a score accuracy of 67%. In this latter case, the performances of the classifiers were not equivalent and L2-regularized logistic regression (both in primal and dual space) provided the most accurate predictions. The analysis of brain regions containing most discriminative information suggested that in both classifications (ADHD vs. TD controls and combined vs. inattentive), the relevant information is not confined only to a small set of regions but it is spatially distributed across the whole brain.