Brain-predicted age difference scores are calculated by subtracting chronological age from 'brain' age, which is estimated using neuroimaging data. Positive scores reflect accelerated ageing and are associated with increased mortality risk and poorer physical function. To date, however, the relationship between brain-predicted age difference scores and specific cognitive functions has not been systematically examined using appropriate statistical methods. First, applying machine learning to 1,359 T1-weighted MRI scans, we predicted the relationship between chronological age and voxel-wise grey matter data. This model was then applied to MRI data from three independent datasets, significantly predicting chronological age in each dataset: Dokuz Eylül University (n=175), the Cognitive Reserve/Reference Ability Neural Network study (n=380), and The Irish Longitudinal Study on Ageing (n=487). Each independent dataset had rich neuropsychological data. Brain-predicted age difference scores were significantly negatively correlated with performance on measures of general cognitive status (two datasets); processing speed, visual attention, and cognitive flexibility (three datasets); visual attention and cognitive flexibility (two datasets); and semantic verbal fluency (two datasets). As such, there is firm evidence of correlations between increased brainpredicted age differences and reduced cognitive function in some domains that are implicated in cognitive ageing.

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In humans, impaired response inhibition is characteristic of a wide range of psychiatric diseases and of normal aging. It is hypothesised that the right inferior frontal cortex plays a key role by inhibiting the motor cortex via the basal ganglia. The electroencephalographyderived β-rhythm (15-29 Hz) is thought to reflect communication within this network, with increased right frontal β-power often observed prior to successful response inhibition. Recent literature suggests that averaging spectral power obscures the transient, burst-like nature of β-activity. There is evidence that the rate of β-bursts following a Stop signal is higher when a motor response is successfully inhibited. However, other characteristics of β-burst events, and their topographical properties, have not yet been examined. Here, we used a large human (male and female) electroencephalography Stop Signal Task dataset (n=218) to examine averaged normalised β-power, β-burst rate and β-burst 'volume' (which we defined as burst duration x frequency span x amplitude). We first sought to optimise the β-burst detection method. In order to find predictors across the whole scalp, and with high temporal precision, we then used machine learning to (1) classify successful vs. failed stopping and to (2) predict individual Stop Signal Reaction Time. β-Burst volume was significantly more predictive of successful and fast stopping than β-burst rate and normalised β-power. The classification model generalised to an external dataset (n=201). We suggest β-burst volume is a sensitive and reliable measure for investigation of human response inhibition. 3 Significance StatementThe electroencephalography-derived β-rhythm (15-29 Hz) is associated with the ability to inhibit ongoing actions. In this study, we sought to identify the specific characteristics of βactivity that contribute to successful and fast inhibition. In order to search for the most relevant features of β-activityacross the whole scalp and with high temporal precisionwe employed machine learning on two large datasets. Spatial and temporal features of β-burst 'volume' (duration x frequency span x amplitude) predicted response inhibition outcomes in our data significantly better than β-burst rate and normalised β-power. These findings suggest that multidimensional measures of β-bursts, such as burst volume, can add to our understanding of human response inhibition.

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Bimanual movement involves a variety of coordinated functions, ranging from elementary patterns that are performed automatically to complex patterns that require practice to be performed skillfully. The neural dynamics accompanying these coordination patterns are complex and rapid. By means of electro- and magneto-encephalographic approaches, it has been possible to examine these dynamics during bimanual coordination with excellent temporal resolution, which complements other neuroimaging modalities with superb spatial resolution. This review focuses on EEG/MEG studies that unravel the processes involved in movement planning and execution, motor learning, and executive functions involved in task switching and dual tasking. Evidence is presented for a spatio-temporal reorganization of the neural networks within and between hemispheres to meet increased task difficulty demands, induced or spontaneous switches in coordination mode, or training-induced neuroplastic modulation in coordination dynamics. Future theoretical developments will benefit from the integration of research techniques unraveling neural activity at different time scales. Ultimately this work will contribute to a better understanding of how the human brain orchestrates complex behavior via the implementation of inter- and intra-hemispheric coordination networks.

The neural network and the task-dependence of (local) activity changes involved in bimanual coordination are well documented. However, much less is known about the functional connectivity within this neural network and its modulation according to manipulations of task complexity. Here, we assessed neural activity via high-density electroencephalography, focussing on changes of activity in the beta frequency band (~15-30Hz) across the motor network in 26 young adult participants (19-29 years old). We investigated how network connectivity was modulated with task difficulty and errors of performance during a bimanual visuomotor movement consisting of dial rotation according to three different ratios of speed: an isofrequency movement (1:1), a non-isofrequency movement with the right hand keeping the fast pace (1:3), and the converse ratio with the left hand keeping the fast pace (3:1). To quantify functional coupling, we determined neural synchronization which might be key for the timing of the activity within brain regions during task execution. Individual source activity with realistic head models was reconstructed at seven regions of interest including frontal and parietal areas, among which we estimated phase-based connectivity. Partial least squares analysis revealed a significant modulation of connectivity with task difficulty, and significant correlations between connectivity and errors in performance, in particular between sensorimotor cortices. Our findings suggest that modulation of long-range synchronization is instrumental for coping with increasing task demands in bimanual coordination.

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