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.

Implications of structural connections within and between brain regions for their functional counterpart are timely points of discussion. White matter microstructural organization and functional activity can be assessed in unison. At first glance, however, the corresponding findings appear variable, both in the healthy brain and in numerous neuro-pathologies. To identify consistent associations between structural and functional connectivity and possible impacts for the clinic, we reviewed the literature of combined recordings of electro-encephalography (EEG) and diffusion-based magnetic resonance imaging (MRI). It appears that the strength of event-related EEG activity increases with increased integrity of structural connectivity, while latency drops. This agrees with a simple mechanistic perspective: the nature of microstructural white matter influences the transfer of activity. The EEG, however, is often assessed for its spectral content. Spectral power shows associations with structural connectivity that can be negative or positive often dependent on the frequencies under study. Functional connectivity shows even more variations, which are difficult to rank. This might be caused by the diversity of paradigms being investigated, from sleep and resting state to cognitive and motor tasks, from healthy participants to patients. More challenging, though, is the potential dependency of findings on the kind of analysis applied. While this does not diminish the principal capacity of EEG and diffusion-based MRI co-registration, it highlights the urgency to standardize especially EEG analysis.