Prefrontal control over motor cortex cycles at beta-frequency during movement inhibition

A fully adapted behavior requires maximum efficiency to inhibit processes in the motor domain [ 1 ]. Although a number of cortical and subcortical brain regions have been implicated, converging evidence suggests that activation of right inferior frontal gyrus (r-IFG) and right presupplementary motor area (r-preSMA) is crucial for successful response inhibition [ 2, 3 ]. However, it is still unknown how these prefrontal areas convey the necessary signal to the primary motor cortex (M1), the cortical site where the final motor plan eventually has to be inhibited or executed. On the basis of the widely accepted view that brain oscillations are fundamental for communication between neuronal network elements [ 4–6 ], one would predict that the transmission of these inhibitory signals within the prefrontal-central networks (i.e., r-IFG/M1 and/or r-preSMA/M1) is realized in rapid, periodic bursts coinciding with oscillatory brain activity at a distinct frequency. However, the dynamics of corticocortical effective connectivity has never been directly tested on such timescales. By using double-coil transcranial magnetic stimulation (TMS) and electroencephalography (EEG) [ 7, 8 ], we assessed instantaneous prefrontal-to-motor cortex connectivity in a Go/NoGo paradigm as a function of delay from (Go/NoGo) cue onset. In NoGo trials only, the effects of a conditioning prefrontal TMS pulse on motor cortex excitability cycled at beta frequency, coinciding with a frontocentral beta signature in EEG. This establishes, for the first time, a tight link between effective cortical connectivity and related cortical oscillatory activity, leading to the conclusion that endogenous (top-down) inhibitory motor signals are transmitted in beta bursts in large-scale cortical networks for inhibitory motor control

Tracking dynamic interactions between structural and functional connectivity : a TMS/EEG-dMRI study

Transcranial magnetic stimulation (TMS) in combination with neuroimaging techniques allows to measure the effects of a direct perturbation of the brain. When coupled with high-density electroencephalography (TMS/hd-EEG), TMS pulses revealed electrophysiological signatures of different cortical modules in health and disease. However, the neural underpinnings of these signatures remain unclear. Here, by applying multimodal analyses of cortical response to TMS recordings and diffusion magnetic resonance imaging (dMRI) tractography, we investigated the relationship between functional and structural features of different cortical modules in a cohort of awake healthy volunteers. For each subject, we computed directed functional connectivity interactions between cortical areas from the source-reconstructed TMS/hd-EEG recordings and correlated them with the correspondent structural connectivity matrix extracted from dMRI tractography, in three different frequency bands (alpha, beta, gamma) and two sites of stimulation (left precuneus and left premotor). Each stimulated area appeared to mainly respond to TMS by being functionally elicited in specific frequency bands, that is, beta for precuneus and gamma for premotor. We also observed a temporary decrease in the whole-brain correlation between directed functional connectivity and structural connectivity after TMS in all frequency bands. Notably, when focusing on the stimulated areas only, we found that the structure-function correlation significantly increases over time in the premotor area controlateral to TMS. Our study points out the importance of taking into account the major role played by different cortical oscillations when investigating the mechanisms for integration and segregation of information in the human brain

Neural Underpinnings of Walking Under Cognitive and Sensory Load: A Mobile Brain/Body Imaging Approach

Dual-task walking studies, in which individuals engage in an attentionally-demanding task while walking, have provided indirect evidence via behavioral and biomechanical measures, of the recruitment of higher-level cortical resources during gait. Additionally, recent EEG and imaging (PET, fNIRS) studies have revealed direct neurophysiological evidence of cortical contributions to steady-state walking. However, there remains a lack of knowledge regarding the underlying neural mechanisms involved in the allocation of cortical resources while walking under increased load. This dissertation presents three experiments designed to provide a greater understanding of the cortical dynamics implicated in processing load (top-down or bottom-up) during locomotion. Furthermore, we seek to investigate age-related differences in these neural pathways. These studies were conducted using an innovative EEG-based Mobile Brain/Body Imaging (MoBI) approach, combining high-density EEG, foot force sensors and 3D body motion capture as participants walked on a treadmill. The first study employed a Go/No-Go response inhibition task to evaluate the long-term test-retest reliability of two cognitively-evoked event-related potentials (ERPs), the earlier N2 and the later P3. Acceptable levels of reliability were found, according to the intraclass correlation coefficient (ICC), and these were similar across sitting and walking conditions. Results indicate that electrocortical signals obtained during walking are stable indices of neurophysiological function. The aim of the second study was to characterize age-related changes in gait and in the allocation of cognitive control under single vs. dual-task load. For young adults, we observed significant modulations as a result of increased task load for both gait (longer stride time) and for ERPs (decreased N2 amplitude and P3 latency). In contrast, older adults exhibited costs in the cognitive domain (reduced accuracy performance), engaged in a more stereotyped pattern of walking, and showed a general lack of ERP modulation while walking under increased load, all of which may indicate reduced flexibility in resource allocation across tasks. Finally, the third study assessed the effects of sensory (optic flow and visual perturbations) and cognitive load (Go/No-Go task) manipulations on gait and cortical neuro-oscillatory activity in young adults. While walking under increased load, participants adopted a more conservative pattern of gait by taking shorter and wider strides, with cognitive load in particular associated with reduced motor variability. Using an Independent Component Analysis (ICA) and dipole-fitting approach, neuro-oscillatory activity was then calculated from eight source-localized clusters of Independent Components (ICs). Significant modulations in average spectral power in the theta (3-7Hz), alpha (8-12Hz), beta (13-30Hz), and gamma (31-45Hz) frequency bands were observed over occipital, parietal and frontal clusters of ICs, as a function of optic flow and task load. Overall, our findings demonstrate the reliability and feasibility of the MoBI approach to assess electrocortical activity in dual-task walking situations, and may be especially relevant to older adults who are less able to flexibly adjust to ongoing cognitive and sensory demands while walking

Modafinil modulation of the default mode network

RationaleThe default mode network (DMN) is a functional network which is implicated in a range of cognitive processes. This network is proposed to consist of hubs located in the ventromedial prefrontal cortex (vmPFC), posterior cingulate/retrosplenial cortex (PCC/rSpl), and inferior parietal lobule (IPL), with other midline cortical and temporal lobe nodes connected to these hubs. How this network is modulated by neurochemical systems during functional brain activity is not yet understood.ObjectivesIn the present study, we used the norepinephrine/dopamine transporter inhibitor modafinil to test the hypothesis that this drug modulates the DMN.MethodsEighteen healthy right-handed adults participated in a double-blind, placebo-controlled study of single oral dose modafinil 200 mg. They performed a simple visual sensorimotor task during slow event-related fMRI. Drug effects were interrogated within the DMN defined by task-induced deactivation (TID) on placebo.ResultsThere was a trend toward faster reaction time (RT) on modafinil (Cohen's d = 0.38). Brain regions within the DMN which exhibited significant modafinil-induced augmentation of TID included vmPFC, PCC/rSpl, and left IPL. Across subjects, the modafinil effect on TID in the vmPFC was significantly and specifically associated with drug effects on RT speeding.ConclusionsModafinil augments TID in the DMN to facilitate sensorimotor processing speed, an effect which may be particularly dependent on changes in vmPFC activity. This is consistent with the gain control function of catecholamine systems and may represent an important aspect of the pro-cognitive effects of modafinil

Reconciling the influence of task-set switching and motor inhibition processes on stop signal after-effects.

Executive response functions can be affected by preceding events, even if they are no longer associated with the current task at hand. For example, studies utilizing the stop signal task have reported slower response times to "GO" stimuli when the preceding trial involved the presentation of a "STOP" signal. However, the neural mechanisms that underlie this behavioral after-effect are unclear. To address this, behavioral and electroencephalography (EEG) measures were examined in 18 young adults (18-30 years) on "GO" trials following a previously "Successful Inhibition" trial (pSI), a previously "Failed Inhibition" trial (pFI), and a previous "GO" trial (pGO). Like previous research, slower response times were observed during both pSI and pFI trials (i.e., "GO" trials that were preceded by a successful and unsuccessful inhibition trial, respectively) compared to pGO trials (i.e., "GO" trials that were preceded by another "GO" trial). Interestingly, response time slowing was greater during pSI trials compared to pFI trials, suggesting executive control is influenced by both task set switching and persisting motor inhibition processes. Follow-up behavioral analyses indicated that these effects resulted from between-trial control adjustments rather than repetition priming effects. Analyses of inter-electrode coherence (IEC) and inter-trial coherence (ITC) indicated that both pSI and pFI trials showed greater phase synchrony during the inter-trial interval compared to pGO trials. Unlike the IEC findings, differential ITC was present within the beta and alpha frequency bands in line with the observed behavior (pSI > pFI > pGO), suggestive of more consistent phase synchrony involving motor inhibition processes during the ITI at a regional level. These findings suggest that between-trial control adjustments involved with task-set switching and motor inhibition processes influence subsequent performance, providing new insights into the dynamic nature of executive control

Learning under uncertainty in the young and older human brain: Common and distinct mechanisms of different attentional and intentional systems

The human brain is able to infer the probability of future events by combining information of past observations with current sensory input. Naturally, we are surrounded by more stimuli than we can pay attention to, so selection of relevant input is crucial. The present thesis aimed at identifying common and distinct neural correlates engaged in predictive processing in spatial attention (selection of attended locations) and motor intention (selection of prepared motor responses). Secondly, age-related influences on probabilistic inference in spatial-attention, feature-based attention (selection of attended color) and motor intention, and the impact of task difficulty were considered. Orienting attention during goal-directed behavior can be supported by visual cues, whereas reorienting to unexpected events following misguiding information is linked to behavioral costs and updating of predictions. These processes can be investigated with a cueing paradigm in which differences in reaction time (RT) between valid and invalidly cued trials increase with higher cue validity (%CV) (Posner, 1980). Bayesian models can describe the experience-dependent learning effects of inferring %CV, following novel events (Vossel et al., 2014c; Vossel, Mathys, Stephan & Friston, 2015). The principle aim of the first experiment was to identify and compare the neural correlates involved in inferring probabilities in the spatial attentional and motor intentional domain. Cues indicated either the possible location or prepared the motor response associated with the target. Instead of a fixed probability context, participants were exposed to a volatile environment, in which the validity of the cue information changed unpredictably over time. Combining functional magnetic resonance imaging (fMRI) data with behavioral estimates derived from a Bayesian learning model (Mathys, Daunizeau, Friston & Stephan, 2011) unveiled domain-specific predictability-dependent responses within the right temporoparietal junction (TPJ) for spatial attention and the left angular gyrus (ANG) and anterior cingulate (ACC) in the motor intention task. The blood oxygen level dependent (BOLD) amplitude particularly increased in accord with violations of cue predictability in high cue validity contexts (i.e. when invalid trials were least expected). Valid trials however, induced no (TPJ and ANG) or decreased modulation (ACC). A further aim was to examine possible commonalities in the neural signatures of predictability-dependent processing. Connectivity analysis uncovered common coupling of all three seed regions involved in predictability-dependent processing with the right anterior hippocampus. Since cognitive functions undergo substantial changes in healthy ageing, a second behavioral study was conducted to test whether age differentially influences probabilistic inference in different attentional subsystems, and how task difficulty impacts on learning performance. Thus, following up on the first experiment, similar tasks and the same computational model was used to assess updating behavior in healthy aging. Older and younger adults performed two separate experiments with different difficulty levels. Each experiment included three versions of a cueing task, entailing predictive spatial- (i.e. location), feature- (i.e. color of target) and motor intention cues (i.e. prepare response). Results of the easier version demonstrated a preserved ability of older adults to generate predictions and profit from all cue types. Interestingly, increased task demand uncovered a reduced ability to use motor intention cues to update predictions in older compared to younger adults. In conclusion, the results provide evidence for a segregated functional anatomy of probabilistic inference in spatial attention and motor intention. Nonetheless a common connectivity profile with the hippocampus also points at commonalities. Finally age seems to differentially impact the efficiency of learning behavior in the motor intention system, supporting the notion of independence of the attentional- and intentional subsystems

Scale-invariant rearrangement of resting state networks in the human brain under sustained stimulation

Brain activity at rest is characterized by widely distributed and spatially specific patterns of synchronized low-frequency blood-oxygenation level-dependent (BOLD) fluctuations, which correspond to physiologically relevant brain networks. This network behaviour is known to persist also during task execution, yet the details underlying task-associated modulations of within- and between-network connectivity are largely unknown. In this study we exploited a multi-parametric and multi-scale approach to investigate how low-frequency fluctuations adapt to a sustained n-back working memory task. We found that the transition from the resting state to the task state involves a behaviourally relevant and scale-invariant modulation of synchronization patterns within both task-positive and default mode networks. Specifically, decreases of connectivity within networks are accompanied by increases of connectivity between networks. In spite of large and widespread changes of connectivity strength, the overall topology of brain networks is remarkably preserved. We show that these findings are strongly influenced by connectivity at rest, suggesting that the absolute change of connectivity (i.e., disregarding the baseline) may not be the most suitable metric to study dynamic modulations of functional connectivity. Our results indicate that a task can evoke scale-invariant, distributed changes of BOLD fluctuations, further confirming that low frequency BOLD oscillations show a specialized response and are tightly bound to task-evoked activation

Connecting the Brain to Itself through an Emulation.

Pilot clinical trials of human patients implanted with devices that can chronically record and stimulate ensembles of hundreds to thousands of individual neurons offer the possibility of expanding the substrate of cognition. Parallel trains of firing rate activity can be delivered in real-time to an array of intermediate external modules that in turn can trigger parallel trains of stimulation back into the brain. These modules may be built in software, VLSI firmware, or biological tissue as in vitro culture preparations or in vivo ectopic construct organoids. Arrays of modules can be constructed as early stage whole brain emulators, following canonical intra- and inter-regional circuits. By using machine learning algorithms and classic tasks known to activate quasi-orthogonal functional connectivity patterns, bedside testing can rapidly identify ensemble tuning properties and in turn cycle through a sequence of external module architectures to explore which can causatively alter perception and behavior. Whole brain emulation both (1) serves to augment human neural function, compensating for disease and injury as an auxiliary parallel system, and (2) has its independent operation bootstrapped by a human-in-the-loop to identify optimal micro- and macro-architectures, update synaptic weights, and entrain behaviors. In this manner, closed-loop brain-computer interface pilot clinical trials can advance strong artificial intelligence development and forge new therapies to restore independence in children and adults with neurological conditions