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Healthy aging affects both grey and white matter. However, the trajectories of regional specific degeneration are not fully understood. Here we investigate the effects of aging on cortical thickness and myelin concentration in a large cohort of healthy participants (N = 610) aged between 18 and 89 years’ old who underwent single-site T1-weighted, T2-weighted and MTI sequences in the context of the Cam-CAN project. Participants were subdivided in three age groups representative of young, middle and late adulthood. The large size of the dataset allowed us to minimize the impact of sample variance without relying on multi-site acquisition protocols. We assessed linear changes in cortical thickness and cortical myelin concentration; the latter was assessed using both T1w/T2w ratio and MTR proxies, to evaluate which is the most stable metrics. Our results do not fit with either the anterior-posterior gradient or the last-in/first-out hypothesis. We demonstrate that aging patterns are more complex than just depending on a spatial gradient or the temporally reversed order of regional development. Moreover, we show a dissociation in aging patterns between somatosensory and motor regions both in terms of cortical thickness and myelin concentration. Finally, comparing T1w/T2w and MTR results of cortical myelination, we found the latter being a more stable and reliable proxy.HighlightsCortical thickness and myelo-architecture changes must be jointly considered in investigating brain aging trajectories.We assessed linear changes in cortical thickness and myelination in a large, homogeneous and single site MRI dataset.Motor and sensory regions show a dissociation in their aging trajectories both in terms of cortical thickness and myelin concentration.Sensory processing regions show similar aging trajectories in both cortical thickness and myelin concentration.MTR is a more reliable proxy for myelin concentration compared to T1w/T2w ratio.

Alpha is the predominant rhythm of the human electroencephalogram, but its function, multiple generators and functional coupling patterns are still relatively unknown. In this regard, alpha connectivity patterns can change between different cortical generators depending on the status of the brain. Therefore, in the light of the communication through coherence framework, an alpha functional network depends on the functional coupling patterns in a determined state. This notion has a relevance for brain-state dependent EEG-TMS because, beyond the local state, a network connectivity overview at rest could provide further and more comprehensive information for the definition of ‘instantaneous state’ at the stimulation moment, rather than just the local state around the stimulation site. For this reason, we studied functional coupling at rest in 203 healthy subjects with MEG data. Sensor signals were source localized and connectivity was studied at the Individual Alpha Frequency (IAF) between three different cortical areas (occipital, parietal and prefrontal). Two different and complementary phase-coherence metrices were used. Our results show a consistent connectivity between parietal and prefrontal regions whereas occipito-prefrontal connectivity is less marked and occipito-parietal connectivity is extremely low, despite physical closeness. We consider our results a relevant add-on for informed, individualized real-time brain state dependent stimulation, with possible contributions to novel, personalized non-invasive therapeutic approaches.

Stroke constitutes the main cause of adult disability worldwide. Even after application of standard rehabilitation protocols, the majority of patients still show relevant motor impairment. Outcomes of standard rehabilitation protocols have led to mixed results, suggesting that relevant factors for brain re-organization after stroke have not been considered in explanatory models. Therefore, finding a comprehensive model to optimally define patient-dependent rehabilitation protocols represents a crucial topic in clinical neuroscience. In this context, we first report on the rehabilitation models conceived thus far in the attempt of predicting stroke rehabilitation outcomes. Then, we propose a new framework to interpret results in stroke literature in the light of the latest evidence regarding: (1) the role of the callosum in inter-hemispheric communication, (2) the role of prefrontal cortices in exerting a control function, and (3) diaschisis mechanisms. These new pieces of evidence on the role of callosum can help to understand which compensatory mechanism may take place following a stroke. Moreover, depending on the individual impairment, the prefrontal control network will play different roles according to the need of high-level motor control. We believe that our new model, which includes crucial overlooked factors, will enable clinicians to better define individualized motor rehabilitation protocols.

The task of identifying and characterizing network structures out of experimentally observed time series is tackled by implementing different solutions, ranging from entropy-based techniques to the evaluation of the significance of observed correlation estimators. Among the metrics that belong to the first class, mutual information is of major importance due to the relative simplicity of implementation and its relying on the crucial concept of entropy. With regard to the second class, a method that allows us to assess the connectivity strength of a link in terms of a time scale of its observability via the significance estimate of measured cross correlation was recently shown to provide a reliable tool to study network structures. In this paper, we investigate the relationship between this last metric and mutual information by simultaneously assessing both metrics on large sets of data extracted from three experimental contexts, human brain magnetoencephalography, human brain electroencephalography, and surface wind measurements carried out on a small regional scale, as well as on simulated coupled, auto-regressive processes. We show that the relationship is well described by a power law and provide a theoretical explanation based on a simple noise and signal model. Besides further upholding the reliability of cross-correlation time scale of observability, the results show that the combined use of this metric and mutual information can be used as a valuable tool to identify and characterize connectivity links in a wide range of experimental contexts.

Physical connections between nodes in a complex network are constrained by limiting factors, such as the cost of establishing links and maintaining them, which can hinder network capability in terms of signal propagation speed and processing power. Trade-off mechanisms between cost constraints and performance requirements are reflected in the topology of a network and, ultimately, on the dependence of connectivity on geometric distance. This issue, though rarely addressed, is crucial in neuroscience, where physical links between brain regions are associated with a metabolic cost. In this work we investigate brain connectivity—estimated by means of a recently developed method that evaluates time scales of cross-correlation observability—and its dependence on geometric distance by analyzing resting state magnetoencephalographic recordings collected from a large set of healthy subjects. We identify three regimes of distance each showing a specific behavior of connectivity. This identification makes up a new tool to study the mechanisms underlying network formation and sustainment, with possible applications to the investigation of neuroscientific issues, such as aging and neurodegenerative diseases.

The oscillatory features of non-REM sleep states have been a subject of intense research over many decades. However, a systematic spatial characterization of the spectral features of cortical activity in each sleep state is not available yet. Here, we used magnetoencephalography (MEG) and electroencephalography (EEG) recordings during night sleep. We performed source reconstruction based on the individual subject's anatomical magnetic resonance imaging (MRI) scans and spectral analysis on each non-REM sleep epoch in eight standard frequency bands, spanning the complete spectrum, and computed cortical source reconstructions of the spectral contrasts between each sleep state in comparison to the resting wakefulness. Despite not distinguishing periods of high and low activity within each sleep stage, our results provide new information about relative overall spectral changes in the non-REM sleep stages.

The oscillatory features of non-REM sleep states have been a subject of intense research over many decades. However, a systematic spatial characterization of the spectral features of cortical activity in each sleep state is not available yet. Here, we used magnetoencephalography (MEG) and electroencephalography (EEG) recordings during night sleep. We performed source reconstruction based on the individual subject’s anatomical magnetic resonance imaging (MRI) scans and spectral analysis on each non-REM sleep epoch in eight standard frequency bands, spanning the complete spectrum, and computed cortical source reconstructions of the spectral contrasts between each sleep state in comparison to the resting wakefulness. Despite not distinguishing periods of high and low activity within each sleep stage, our results provide new information about relative overall spectral changes in the non-REM sleep stages.

Successfully interpreting and navigating our natural visual environment requires us to track its dynamics constantly. Additionally, we focus our attention on behaviorally relevant stimuli to enhance their neural processing. Little is known, however, about how sustained attention affects the ongoing tracking of stimuli with rich natural temporal dynamics. Here, we used MRI-informed source reconstructions of magnetoencephalography (MEG) data to map to what extent various cortical areas track concurrent continuous quasi-rhythmic visual stimulation. Further, we tested how top-down visuo-spatial attention influences this tracking process. Our bilaterally presented quasi-rhythmic stimuli covered a dynamic range of 4 – 20Hz, subdivided into three distinct bands. As an experimental control, we also included strictly rhythmic stimulation (10 vs 12 Hz). Using a spectral measure of brain-stimulus coupling, we were able to track the neural processing of left vs. right stimuli independently, even while fluctuating within the same frequency range. The fidelity of neural tracking depended on the stimulation frequencies, decreasing for higher frequency bands. Both attended and non-attended stimuli were tracked beyond early visual cortices, in ventral and dorsal streams depending on the stimulus frequency. In general, tracking improved with the deployment of visuo-spatial attention to the stimulus location. Our results provide new insights into how human visual cortices process concurrent dynamic stimuli and provide a potential mechanism – namely increasing the temporal precision of tracking – for boosting the neural representation of attended input.

In any network, the dependence of connectivity on physical distance between nodes is a direct consequence of trade-off mechanisms between costs of establishing and sustaining links, processing rates, propagation speed of signals between nodes. Despite its universality, there are still few studies addressing this issue. Here we apply a recently–developed method to infer links between nodes, and possibly subnetwork structures, to determine connectivity strength as a function of physical distance between nodes. The model system we investigate is brain activity reconstructed on the cortex out of magnetoencephalography recordings sampled on a set of healthy subjects in resting state. We found that the dependence of the time scale of observability of a link on its geometric length follows a power–law characterized by an exponent whose extent is inversely proportional to connectivity. Our method provides a new tool to highlight and investigate networks in neuroscience.

The parietal operculum (OP) contains haptic memory on the geometry of objects that is readily transferrable to the motor cortex but a causal role of OP in memory-guided grasping is only speculative. We explored this issue by using online high-frequency repetitive transcranial magnetic stimulation (rTMS). The experimental task was performed by blindfolded participants acting on objects of variable size. Trials consisted in three phases: haptic exploration of an object, delay, and reach-grasp movement onto the explored object. Motor performance was evaluated by the kinematics of finger aperture. Online rTMS was applied to the left OP region separately in each of the three phases of the task. The results showed that rTMS altered grip aperture only when applied in the delay phase to the OP. In a second experiment a haptic discriminative (match-to-sample) task was carried out on objects similar to those used in the first experiment. Online rTMS was applied to the left OP. No psychophysical effects were induced by rTMS on the detection of explicit haptic object size. We conclude that neural activity in the OP region is necessary for proficient memory-guided haptic grasping. The function of OP seems to be critical while maintaining the haptic memory trace and less so while encoding it or retrieving it.

In this paper, the topographical relationship between functional connectivity (intended as inter-regional synchronization), spectral and non-linear dynamical properties across cortical areas of the healthy human brain is considered. Based upon functional MRI acquisitions of spontaneous activity during wakeful idleness, node degree maps are determined by thresholding the temporal correlation coefficient among all voxel pairs. In addition, for individual voxel time-series, the relative amplitude of low-frequency fluctuations and the correlation dimension (D2), determined with respect to Fourier amplitude and value distribution matched surrogate data, are measured. Across cortical areas, high node degree is associated with a shift towards lower frequency activity and, compared to surrogate data, clearer saturation to a lower correlation dimension, suggesting presence of non-linear structure. An attempt to recapitulate this relationship in a network of single-transistor oscillators is made, based on a diffusive ring (n = 90) with added long-distance links defining four extended hub regions. Similarly to the brain data, it is found that oscillators in the hub regions generate signals with larger low-frequency cycle amplitude fluctuations and clearer saturation to a lower correlation dimension compared to surrogates. The effect emerges more markedly close to criticality. The homology observed between the two systems despite profound differences in scale, coupling mechanism and dynamics appears noteworthy. These experimental results motivate further investigation into the heterogeneity of cortical non-linear dynamics in relation to connectivity and underline the ability for small networks of single-transistor oscillators to recreate collective phenomena arising in much more complex biological systems, potentially representing a future platform for modelling disease-related changes.

International audience; Neurons firing both during self and other's motor behavior (mirror neurons) have been described in the brain of vertebrates including humans. The activation of somatic motor programs driven by perceived behavior has been taken as evidence for mirror neurons' contribution to cognition. The inverse relation, that is the influence of motor behavior on perception, is needed for demonstrating the long-hypothesized causal role of mirror neurons in action understanding. We provide here conclusive behavioral and neurophysiological evidence for that causal role by means of cross-modal adaptation coupled with a novel transcranial magnetic stimulation (TMS)-adaptation paradigm. Blindfolded repeated motor performance of an object-directed action (push or pull) induced in healthy participants a strong visual after-effect when categorizing others' actions, as a result of motor-to-visual adaptation of visuo-motor neurons. TMS over the ventral premotor cortex, but not over the primary motor cortex, suppressed the after-effect, thus localizing the population of adapted visuo-motor neurons in the premotor cortex. These data are exquisitely consistent in humans with the existence of premotor mirror neurons that have access to the action meaning. We also show that controlled manipulation of the firing properties of this neural population produces strong predictable changes in the way we categorize others' actions.

In human perception, exogenous noise is known to yield a masking effect, i.e. an increase of the perceptual threshold relative to a stimulus acting on the same modality. However, somehow counter-intuitively, the opposite mechanism can occasionally occur: a decrease of the perceptual threshold for a non-vanishing, virtuous amount of noise. This mechanism, called stochastic resonance, is deemed to provide important information about the role of noise in the human brain. In this paper, we investigate stochastic resonance in a detection task in the auditory modality. Normal-hearing participants were asked to judge the presence of acoustic stimuli of different intensity and superimposed to different levels of white noise. The matrix-like outcomes of a behavioural experiment were fitted by a two-dimensional, noise-dependent psychometric function. The fit revealed a statistically significant stochastic resonance in 43% of the experimental runs. We conclude that, in the auditory modality, stochastic resonance is a tiny effect that, under conventional circumstances, is largely overrun by standard masking.

Decision making is a widespread research topic and plays a crucial role in neuroscience as well as in other research and application fields of, for example, biology, medicine and economics. The most basic implementation of decision making, namely binary discrimination, is successfully interpreted by means of signal detection theory (SDT), a statistical model that is deeply linked to physics. An additional, widespread tool to investigate discrimination ability is the psychometric function, which measures the probability of a given response as a function of the magnitude of a physical quantity underlying the stimulus. However, the link between psychometric functions and binary discrimination experiments is often neglected or misinterpreted. Aim of the present paper is to provide a detailed description of an experimental investigation on a prototypical discrimination task and to discuss the results in terms of SDT. To this purpose, we provide an outline of the theory and describe the implementation of two behavioural experiments in the visual modality: upon the assessment of the so-called psychometric function, we show how to tailor a binary discrimination experiment on performance and decisional bias, and to measure these quantities on a statistical base. Attention is devoted to the evaluation of uncertainties, an aspect which is also often overlooked in the scientific literature.

We report on a detailed measurement of the enhanced absorption Hanle effect resonances in 85 Rb. The effect was analysed with an experimental setup allowing for the control of each magnetic field component within 1 mG. The characterization deals with the dependence of resonances, observed under different magnetic field conditions, on the frequency, intensity and polarization of the exciting radiation field. An analytic model that precisely describes the resonance behaviour is discussed.

This study on cold-air pool formation in the wide Cerdanya Valley in the Pyrenees mountain range was conducted using available observational information from September 2010 to August 2014. Cold-air pools occur during almost 60% of the nights, mainly during winter. Cold pools develop even under significant synoptic pressure gradients. Additionally, drainage currents transporting air down-valley occur most of the nights. In particular one representative cold-air pool event has been analysed in detail by a high-resolution mesoscale simulation, combined with an analysis of data from both ground-based stations and satellites. Radiative processes dominate the evolution of cold-air pools, together with turbulence in the lowest layers, while drainage flows down from the high mountains mainly through the tributary valleys and from the valley sidewall slopes play a key role in bringing air to the pool. Cold pool formation begins approximately 1 hr after sunset, and it extends across most of the valley bottom, with a very strong thermal inversion close to the surface that has a depth of up to 100¿m in the lowest parts of the valley. Wind veers down-valley along the main axis 2–3 hr after sunset and the wind direction is approximately maintained until after sunrise.