Resting State Dynamic Reconfiguration of Spatial Attention Cortical Networks and Visuospatial Functioning in Non-Verbal Learning Disability (NVLD): A HD-EEG Investigation.

Nonverbal learning disability (NVLD) is a neurodevelopmental disorder characterized by deficits in visuospatial processing but spared verbal competencies. Neurocognitive markers may provide confirmatory evidence for characterizing NVLD as a separate neurodevelopmental disorder. Visuospatial performance and high-density electroencephalography (EEG) were measured in 16 NLVD and in 16 typically developing (TD) children. Cortical source modeling was applied to assess resting-state functional connectivity (rs-FC) in spatial attention networks (dorsal (DAN) and ventral attention networks (VAN)) implicated in visuospatial abilities. A machine-learning approach was applied to investigate whether group membership could be predicted from rs-FC maps and if these connectivity patterns were predictive of visuospatial performance. Graph theoretical measures were applied to nodes inside each network. EEG rs-FC maps in the gamma and beta band differentiated children with and without NVLD, with increased but more diffuse and less efficient functional connections bilaterally in the NVLD group. While rs-FC of the left DAN in the gamma range predicted visuospatial scores for TD children, in the NVLD group rs-FC of the right DAN in the delta range predicted impaired visuospatial performance, confirming that NVLD is a disorder with a predominant dysfunction in right hemisphere connectivity patterns.

Grounding Adaptive Cognitive Control in the Intrinsic, Functional Brain Organization: An HD-EEG Resting State Investigation.

In a recent study, we used the dynamic temporal prediction (DTP) task to demonstrate that the capability to implicitly adapt motor control as a function of task demand is grounded in at least three dissociable neurofunctional mechanisms: expectancy implementation, expectancy violation and response implementation, which are supported by as many distinct cortical networks. In this study, we further investigated if this ability can be predicted by the individual brain's functional organization at rest. To this purpose, we recorded resting-state, high-density electroencephalography (HD-EEG) in healthy volunteers before performing the DTP task. This allowed us to obtain source-reconstructed cortical activity and compute whole-brain resting state functional connectivity at the source level. We then extracted phase locking values from the parceled cortex based on the Destrieux atlas to estimate individual functional connectivity at rest in the three task-related networks. Furthermore, we applied a machine-learning approach (i.e., support vector regression) and were able to predict both behavioral (response speed and accuracy adaptation) and neural (ERP modulation) task-dependent outcome. Finally, by exploiting graph theory nodal measures (i.e., degree, strength, local efficiency and clustering coefficient), we characterized the contribution of each node to the task-related neural and behavioral effects. These results show that the brain's intrinsic functional organization can be potentially used as a predictor of the system capability to adjust motor control in a flexible and implicit way. Additionally, our findings support the theoretical framework in which cognitive control is conceived as an emergent property rooted in bottom-up associative learning processes.

Electroencephalographic functional connectivity in extreme prematurity: a pilot study based on graph theory.

BACKGROUND: Connectivity studies based on functional magnetic resonance imaging (MRI) provided new insights in neonatal brain development but cannot be performed at bedside in the clinical setting. The electroencephalogram (EEG) connectivity has been less studied, particularly using the new approach based on graph theory. This study aimed to explore the functional EEG connectivity using graph theory analysis at an early post-conception age in extremely premature and late-preterm babies free of medical complications and overt brain damage. METHODS: Sixteen neonates (8 extremely low gestational age (ELGA) and 8 late-preterm infants), both groups having performed multichannel EEG recordings at 35 weeks' post-conception, were recruited in a single tertiary-level neonatal intensive care unit and well-baby nursery, respectively. Global (i.e., small-worldness) and local (i.e., clustering and strength) connectivity measures were calculated on a single-subject connectivity matrix of EEG data. RESULTS: Both ELGA and late-preterm infants showed small-worldness organization at 35 weeks' post-conception. The ELGA group had the strength parameter of the theta frequency band lower in the right than in the left hemisphere. This asymmetry did not emerge in the late-preterm group. Moreover, the mean strength parameter was significantly greater in the right hemisphere in the late preterms than in the ELGA group. CONCLUSION: EEG connectivity measures could represent an index of left-to-right maturation and developmental disadvantage in extremely preterm infants.

Can Implicit or Explicit Time Processing Impact Numerical Representation? Evidence From a Dual Task Paradigm.

Whether the human brain processes various types of magnitude, such as numbers and time, through a shared representation or whether there are different representations for each type of magnitude is still debated. Here, we investigated two aspects of number-time interaction: the effects of implicit and explicit processing of time on numbers and the bi-directional interaction between time and number processing. Thirty-two participants were randomly assigned into two experimental groups that performed, respectively, a Single task (number comparison, with implicit time processing) and a Dual task (number comparison as a primary task, with explicit time processing as a secondary task). Results showed that participants, only in the Dual task, were faster and more accurate when processing large numbers paired with long rather than short durations, whereas the opposite pattern was not evident for small numbers. Moreover, participants were more accurate when judging long durations after having processed large rather than small numbers, whereas the opposite pattern emerged for short durations. We propose that number processing influences time processing more than vice versa, suggesting that numbers and time might be at least partially independently represented. This finding can pave the way for investigating the hierarchical representation of space, numbers, and time.

Decoding social intentions in human prehensile actions: Insights from a combined kinematics-fMRI study.

Consistent evidence suggests that the way we reach and grasp an object is modulated not only by object properties (e.g., size, shape, texture, fragility and weight), but also by the types of intention driving the action, among which the intention to interact with another agent (i.e., social intention). Action observation studies ascribe the neural substrate of this 'intentional' component to the putative mirror neuron (pMNS) and the mentalizing (MS) systems. How social intentions are translated into executed actions, however, has yet to be addressed. We conducted a kinematic and a functional Magnetic Resonance Imaging (fMRI) study considering a reach-to-grasp movement performed towards the same object positioned at the same location but with different intentions: passing it to another person (social condition) or putting it on a concave base (individual condition). Kinematics showed that individual and social intentions are characterized by different profiles, with a slower movement at the level of both the reaching (i.e., arm movement) and the grasping (i.e., hand aperture) components. fMRI results showed that: (i) distinct voxel pattern activity for the social and the individual condition are present within the pMNS and the MS during action execution; (ii) decoding accuracies of regions belonging to the pMNS and the MS are correlated, suggesting that these two systems could interact for the generation of appropriate motor commands. Results are discussed in terms of motor simulation and inferential processes as part of a hierarchical generative model for action intention understanding and generation of appropriate motor commands.

Spectral analysis highlight developmental EEG changes in preterm infants without overt brain damage.

Early markers of neurological outcome in the absence of overt brain damage are scarce in extreme prematurity. The aim of this study was to compare spectral EEG values of infants born near term with those of infants born at extremely low gestational age (ELGA) but having attained near term age. We aimed also to evaluate whether spectral EEG features were related with neurological outcome. The ELGA group consisted of 12 neonates born between 23+2 and 27+6 weeks; the control group consisted of nine infants born 34-35+2 weeks, tested within the first week of life. All neonates underwent multichannel EEG recordings at 35 weeks post-conception. None of the subjects had apparent neurological abnormalities or risk factors at the time of recording. EEG data were transformed into the frequency domain and divided into delta (0.5-4Hz), theta (5-7Hz), alpha (8-13Hz), beta (14-20Hz) frequency bands; relative EEG power values were calculated. ELGA group was compared with the control group using a mixed analysis of variance. Outcome was evaluated at one year of age by Griffiths' scales. A principal effect of frequency and an interaction effect of frequency * group was found. The total relative power of the delta band was significantly higher in ELGA than in control group, whereas in the remaining frequency bands total relative power was lower in ELGA than in control group. Higher values of delta and lower values of alpha and beta spectral power correlated with poor outcome. We provide preliminary results suggesting that, as early as 35 weeks post conception, infants born extremely preterm fail to develop the age specific pattern of EEG spectral activity, in the absence of neurological neonatal risk.

Probing the reaching-grasping network in humans through multivoxel pattern decoding.

INTRODUCTION: The quest for a putative human homolog of the reaching-grasping network identified in monkeys has been the focus of many neuropsychological and neuroimaging studies in recent years. These studies have shown that the network underlying reaching-only and reach-to-grasp movements includes the superior parieto-occipital cortex (SPOC), the anterior part of the human intraparietal sulcus (hAIP), the ventral and the dorsal portion of the premotor cortex, and the primary motor cortex (M1). Recent evidence for a wider frontoparietal network coding for different aspects of reaching-only and reach-to-grasp actions calls for a more fine-grained assessment of the reaching-grasping network in humans by exploiting pattern decoding methods (multivoxel pattern analysis--MVPA). METHODS: Here, we used MPVA on functional magnetic resonance imaging (fMRI) data to assess whether regions of the frontoparietal network discriminate between reaching-only and reach-to-grasp actions, natural and constrained grasping, different grasp types, and object sizes. Participants were required to perform either reaching-only movements or two reach-to-grasp types (precision or whole hand grasp) upon spherical objects of different sizes. RESULTS: Multivoxel pattern analysis highlighted that, independently from the object size, all the selected regions of both hemispheres contribute in coding for grasp type, with the exception of SPOC and the right hAIP. Consistent with recent neurophysiological findings on monkeys, there was no evidence for a clear-cut distinction between a dorsomedial and a dorsolateral pathway that would be specialized for reaching-only and reach-to-grasp actions, respectively. Nevertheless, the comparison of decoding accuracy across brain areas highlighted their different contributions to reaching-only and grasping actions. CONCLUSIONS: Altogether, our findings enrich the current knowledge regarding the functional role of key brain areas involved in the cortical control of reaching-only and reach-to-grasp actions in humans, by revealing novel fine-grained distinctions among action types within a wide frontoparietal network.

Deep generative learning of location-invariant visual word recognition.

It is widely believed that orthographic processing implies an approximate, flexible coding of letter position, as shown by relative-position and transposition priming effects in visual word recognition. These findings have inspired alternative proposals about the representation of letter position, ranging from noisy coding across the ordinal positions to relative position coding based on open bigrams. This debate can be cast within the broader problem of learning location-invariant representations of written words, that is, a coding scheme abstracting the identity and position of letters (and combinations of letters) from their eye-centered (i.e., retinal) locations. We asked whether location-invariance would emerge from deep unsupervised learning on letter strings and what type of intermediate coding would emerge in the resulting hierarchical generative model. We trained a deep network with three hidden layers on an artificial dataset of letter strings presented at five possible retinal locations. Though word-level information (i.e., word identity) was never provided to the network during training, linear decoding from the activity of the deepest hidden layer yielded near-perfect accuracy in location-invariant word recognition. Conversely, decoding from lower layers yielded a large number of transposition errors. Analyses of emergent internal representations showed that word selectivity and location invariance increased as a function of layer depth. Word-tuning and location-invariance were found at the level of single neurons, but there was no evidence for bigram coding. Finally, the distributed internal representation of words at the deepest layer showed higher similarity to the representation elicited by the two exterior letters than by other combinations of two contiguous letters, in agreement with the hypothesis that word edges have special status. These results reveal that the efficient coding of written words-which was the model's learning objective-is largely based on letter-level information.

The spatial representation of numerical and non-numerical ordered sequences: insights from a random generation task.

It is widely believed that numbers are spatially represented from left to right on the mental number line. Whether this spatial format of representation is specific to numbers or is shared by non-numerical ordered sequences remains controversial. When healthy participants are asked to randomly generate digits they show a systematic small-number bias that has been interpreted in terms of "pseudoneglect in number space". Here we used a random generation task to compare numerical and non-numerical order. Participants performed the task at three different pacing rates and with three types of stimuli (numbers, letters, and months). In addition to a small-number bias for numbers, we observed a bias towards "early" items for letters and no bias for months. The spatial biases for numbers and letters were rate independent and similar in size, but they did not correlate across participants. Moreover, letter generation was qualified by a systematic forward direction along the sequence, suggesting that the ordinal dimension was more salient for letters than for numbers in a task that did not require its explicit processing. The dissociation between numerical and non-numerical orders is consistent with electrophysiological and neuroimaging studies and suggests that they rely on at least partially different mechanisms.

Discrimination and ordinal judgments of temporal durations at 3 months.

This study presents the first evidence that 3-month-old infants success in a timing matching task and in an ordinal timing task, when numerical information is controlled. Three-month-old infants discriminated brief temporal durations that differed by a 1:3 ratio, relying solely on temporal information. Moreover, at 3 months of age infants were able to discriminate between monotonic and non-monotonic time-based series, when numerical and temporal information were inconsistent. These findings strengthen the hypothesis that a magnitude representational system for temporal quantities is operating very early in the ontogenetic development.

Priming the mental time line.

Growing experimental evidence suggests that temporal events are represented on a mental time line, spatially oriented from left to right. Support for the spatial representation of time comes mostly from studies that have used spatially organized responses. Moreover, many of these studies did not avoid possible confounds attributable to target stimuli that simultaneously convey both spatial and temporal dimensions. Here we show that task-irrelevant, lateralized visuospatial primes affect auditory duration judgments. Responses to short durations were faster when the auditory target was paired with left- than with right-sided primes, whereas responses to long durations were faster when paired with right- than with left-sided primes. Thus, when the representations of physical space and time are concurrently activated, physical space may influence time even when a lateralized, spatially encoded response is not required by the task. The time-space interaction reported here cannot be ascribed to any Spatial-Temporal Association of Response Codes effect. It supports the hypothesis that the representation of time is spatially organized, with short durations represented on the left space and longer ones on the right.

Numerosity estimation in visual stimuli in the absence of luminance-based cues.

BACKGROUND: Numerosity estimation is a basic preverbal ability that humans share with many animal species and that is believed to be foundational of numeracy skills. It is notoriously difficult, however, to establish whether numerosity estimation is based on numerosity itself, or on one or more non-numerical cues like-in visual stimuli-spatial extent and density. Frequently, different non-numerical cues are held constant on different trials. This strategy, however, still allows numerosity estimation to be based on a combination of non-numerical cues rather than on any particular one by itself. METHODOLOGY/PRINCIPAL FINDINGS: Here we introduce a novel method, based on second-order (contrast-based) visual motion, to create stimuli that exclude all first-order (luminance-based) cues to numerosity. We show that numerosities can be estimated almost as well in second-order motion as in first-order motion. CONCLUSIONS/SIGNIFICANCE: The results show that numerosity estimation need not be based on first-order spatial filtering, first-order density perception, or any other processing of luminance-based cues to numerosity. Our method can be used as an effective tool to control non-numerical variables in studies of numerosity estimation.

Distinct representations of numerical and non-numerical order in the human intraparietal sulcus revealed by multivariate pattern recognition.

Neuroimaging studies of numerical cognition have pointed to the horizontal segment of the intraparietal sulcus (hIPS) as the neural correlate of numerical representations in humans. However, the specificity of hIPS for numbers remains controversial. For example, its activation during numerical comparison cannot be distinguished from activation during ordinal judgments on non-numerical sequences such as letters (Fias et al., 2007, J. Neuroscience). Based on the hypothesis that the fine-grained distinction between representations of numerical vs. letter order in hIPS might simply be invisible to conventional fMRI data analysis, we used support vector machines (SVM) to reanalyse the data of Fias et al. (2007). We show that classifiers trained on hIPS voxels can discriminate between number comparison and letter comparison, even though the two tasks produce the same metric of behaviour. Voxels discriminating between the two conditions were consistent across subjects and contribution analysis revealed maps of distinct sets of voxels implicated in the processing of numerical vs. alphabetical order in bilateral hIPS. These results reconcile the neuroimaging data with the neuropsychological evidence suggesting dissociations between numbers and other non-numerical ordered sequences, and demonstrate that multivariate analyses are fundamental to address fine-grained theoretical issues with fMRI studies.