David Sharp

Age-associated disease and disability are placing a growing burden on society. However, ageing does not affect people uniformly. Hence, markers of the underlying biological ageing process are needed to help identify people at increased risk of age-associated physical and cognitive impairments and ultimately, death. Here, we present such a biomarker, 'brain-predicted age', derived using structural neuroimaging. Brain-predicted age was calculated using machine-learning analysis, trained on neuroimaging data from a large healthy reference sample (N = 2001), then tested in the Lothian Birth Cohort 1936 (N = 669), to determine relationships with age-associated functional measures and mortality. Having a brain-predicted age indicative of an older-appearing brain was associated with: weaker grip strength, poorer lung function, slower walking speed, lower fluid intelligence, higher allostatic load and increased mortality risk. Furthermore, while combining brain-predicted age with grey matter and cerebrospinal fluid volumes (themselves strong predictors) not did improve mortality risk prediction, the combination of brain-predicted age and DNA-methylation-predicted age did. This indicates that neuroimaging and epigenetics measures of ageing can provide complementary data regarding health outcomes. Our study introduces a clinically-relevant neuroimaging ageing biomarker and demonstrates that combining distinct measurements of biological ageing further helps to determine risk of age-related deterioration and death.

—Non-invasive brain stimulation, such as transcranial alternating current stimulation (tACS) provides a powerful tool to directly modulate brain oscillations that mediate complex cognitive processes. While the body of evidence about the effect of tACS on behavioral and cognitive performance is constantly growing, those studies fail to address the importance of subject-specific stimulation protocols. With this study here, we set the foundation to combine tACS with a recently presented framework that utilizes real-time fRMI and Bayesian optimization in order to identify the most optimal tACS protocol for a given individual. While Bayesian optimization is particularly relevant to such a scenario, its success depends on two fundamental choices: the choice of covariance kernel for the Gaussian process prior as well as the choice of acquisition function that guides the search. Using empirical (functional neuroimaging) as well as simulation data, we identified the squared exponential kernel and the upper confidence bound acquisition function to work best for our problem. These results will be used to inform our upcoming real-time experiments.

Some neural circuits operate with simple dynamics characterized by one or a few well-defined spatiotemporal scales (e.g. central pattern generators). In contrast, cortical neuronal networks often exhibit richer activity patterns in which all spatiotemporal scales are represented. Such " scale-free " cortical dynamics manifest as cascades of activity with cascade sizes that are distributed according to a power-law. Theory and in vitro experiments suggest that information transmission among cortical circuits is optimized by scale-free dynamics. In vivo tests of this hypothesis have been limited by experimental techniques with insufficient spatial coverage and resolution, i.e., restricted access to a wide range of scales. We overcame these limitations by using genetically encoded voltage imaging to track neural activity in layer 2/3 pyramidal cells across the cortex in mice. As mice recovered from anesthesia, we observed three changes: (a) cortical information capacity increased, (b) information transmission among cortical regions increased and (c) neural activity became scale-free. Our results demonstrate that both information capacity and information transmission are maximized in the awake state in cortical regions with scale-free network dynamics.

A prominent theory proposes that the right inferior frontal
cortex of the human brain houses a dedicated region for
motor response inhibition. However, there is growing
evidence to support the view that this inhibitory control
hypothesis is incorrect. Here, we discuss evidence in
favour of our alternative hypothesis, which states that
response inhibition is one example of a broader class of
control processes that are supported by the same set of
frontoparietal networks. These domain-general networks
exert control by modulating local lateral inhibition pro-
cesses, which occur ubiquitously throughout the cortex.
We propose that to fully understand the neural basis of
behavioural control requires a more holistic approach that
considers how common network mechanisms support
diverse cognitive processes.

Traumatic brain injury affects brain connectivity by producing traumatic axonal injury. This disrupts the function of large-scale
networks that support cognition. The best way to describe this relationship is unclear, but one elegant approach is to view
networks as graphs. Brain regions become nodes in the graph, and white matter tracts the connections. The overall effect of an
injury can then be estimated by calculating graph metrics of network structure and function. Here we test which graph metrics
best predict the presence of traumatic axonal injury, as well as which are most highly associated with cognitive impairment. A
comprehensive range of graph metrics was calculated from structural connectivity measures for 52 patients with traumatic brain injury, 21 of whom had microbleed evidence of traumatic axonal injury, and 25 age-matched controls. White matter connections between 165 grey matter brain regions were defined using tractography, and structural connectivity matrices calculated from skeletonized diffusion tensor imaging data. This technique estimates injury at the centre of tract, but is insensitive to damage at tract edges. Graph metrics were calculated from the resulting connectivity matrices and machine-learning techniques used to select the metrics that best predicted the presence of traumatic brain injury. In addition, we used regularization and variable selection via the elastic net to predict patient behaviour on tests of information processing speed, executive function and associative
memory. Support vector machines trained with graph metrics of white matter connectivity matrices from the microbleed group
were able to identify patients with a history of traumatic brain injury with 93.4% accuracy, a result robust to different ways of
sampling the data. Graph metrics were significantly associated with cognitive performance: information processing speed
(R2 = 0.64), executive function (R2 = 0.56) and associative memory (R2 = 0.25). These results were then replicated in a separate
group of patients without microbleeds. The most influential graph metrics were betweenness centrality and eigenvector centrality,
which provide measures of the extent to which a given brain region connects other regions in the network. Reductions in
betweenness centrality and eigenvector centrality were particularly evident within hub regions including the cingulate cortex and caudate. Our results demonstrate that betweenness centrality and eigenvector centrality are reduced within network hubs, due to the impact of traumatic axonal injury on network connections. The dominance of betweenness centrality and eigenvector centrality suggests that cognitive impairment after traumatic brain injury results from the disconnection of network hubs by traumatic axonal injury.

Diffuse axonal injury after traumatic brain injury (TBI) produces neurological impairment by disconnecting brain networks. This structural damage can be mapped using diffusion MRI, and its functional effects can be investigated in large-scale intrinsic connectivity networks (ICNs). Here, we review evidence that TBI substantially disrupts ICN function, and that this disruption predicts cognitive impairment. We focus on two ICNs--the salience network and the default mode network. The activity of these ICNs is normally tightly coupled, which is important for attentional control. Damage to the structural connectivity of these networks produces predictable abnormalities of network function and cognitive control. For example, the brain normally shows a 'small-world architecture' that is optimized for information processing, but TBI shifts network function away from this organization. The effects of TBI on network function are likely to be complex, and we discuss how advanced approaches to modelling brain dynamics can provide insights into the network dysfunction. We highlight how structural network damage caused by axonal injury might interact with neuroinflammation and neurodegeneration in the pathogenesis of Alzheimer disease and chronic traumatic encephalopathy, which are late complications of TBI. Finally, we discuss how network-level diagnostics could inform diagnosis, prognosis and treatment development following TBI.

Objective: We test the hypothesis that brain networks associated with cognitive function shift away from a “small-world” organization following traumatic brain injury (TBI).

Methods: We investigated 20 TBI patients and 21 age-matched controls. Resting-state functional MRI was used to study functional connectivity. Graph theoretical analysis was then applied to partial correlation matrices derived from these data. The presence of white matter damage was quantified using diffusion tensor imaging.

Results: Patients showed characteristic cognitive impairments as well as evidence of damage to white matter tracts. Compared to controls, the graph analysis showed reduced overall connectivity, longer average path lengths, and reduced network efficiency. A particular impact of TBI is seen on a major network hub, the posterior cingulate cortex. Taken together, these results confirm that a network critical to cognitive function shows a shift away from small-world characteristics.

Conclusions: We provide evidence that key brain networks involved in supporting cognitive function become less small-world in their organization after TBI. This is likely to be the result of diffuse white matter damage, and may be an important factor in producing cognitive impairment after TBI.

Purpose of review
Traumatic brain injury (TBI) often results in long-term cognitive impairments. This is often due to the
disruption of brain networks that support cognition. Major advances have recently been made in our
understanding of these networks. Here we review work that investigates the effect of TBI on brain networks,
and discuss the potential importance of these findings for rehabilitation.
Recent findings
Large-scale brain networks, which we refer to as intrinsic connectivity networks (ICNs), have been
identified. Traumatic axonal injury disrupts their white-matter connections, and altered brain activity within
the networks is frequently observed after TBI. These changes relate to the pattern of cognitive impairment,
and are useful for predicting clinical outcome. The effect of drugs such as methylphenidate, which can be
used to augment rehabilitation, are beginning to be studied in the context of their effect on network
function after TBI.
Summary
The assessment of brain network function after TBI provides insights into the pathophysiology of cognitive
dysfunction and the mechanisms involved in recovery. These advances should provide the basis for a more
detailed understanding of rehabilitation, and ultimately guide the development of targeted individualized
therapy after TBI.

Modern neuroimaging techniques have advanced our understanding of the distributed
anatomy of speech production, beyond that inferred from clinico-pathological correlations. However,
much remains unknown about functional interactions between anatomically distinct components of
this speech production network. One reason for this is the need to separate spatially overlapping
neural signals supporting diverse cortical functions. We took three separate human functional magnetic
resonance imaging (fMRI) datasets (two speech production, one “rest”). In each we decomposed
the neural activity within the left posterior perisylvian speech region into discrete
components. This decomposition robustly identified two overlapping spatio-temporal components,
one centered on the left posterior superior temporal gyrus (pSTG), the other on the adjacent ventral
anterior parietal lobe (vAPL). The pSTG was functionally connected with bilateral superior temporal
and inferior frontal regions, whereas the vAPL was connected with other parietal regions, lateral
and medial. Surprisingly, the components displayed spatial anti-correlation, in which the negative
functional connectivity of each component overlapped with the other component’s positive functional
connectivity, suggesting that these two systems operate separately and possibly in competition. The
speech tasks reliably modulated activity in both pSTG and vAPL suggesting they are involved in
speech production, but their activity patterns dissociate in response to different speech demands.
These components were also identified in subjects at “rest” and not engaged in overt speech production.
These findings indicate that the neural architecture underlying speech production involves parallel
distinct components that converge within posterior peri-sylvian cortex, explaining, in part, why
this region is so important for speech production.

Abstract Stopping an action in response to an unexpected event requires both that the event is attended to, and that the action is inhibited. Previous neuroimaging investigations of stopping have failed to adequately separate these cognitive elements. Here we used a version of the widely used Stop Signal Task that controls for the attentional capture of stop signals.

Abstract Whole-brain structural connectivity matrices extracted from Diffusion Weighted Images (DWI) provide a systematic way of representing anatomical brain networks. They are equivalent to weighted graphs that encode both the topology of the network as well as the strength of connection between each pair of region of interest (ROIs). Here, we exploit their hierarchical organization to infer probability of connection between pairs of ROIs.

Abstract Studies that examine the relationship of functional and structural connectivity are tremendously important in interpreting neurophysiological data. Although, the relationship between functional and structural connectivity has been explored with a number of statistical tools, there is no explicit attempt to quantitatively measure how well functional data can be predicted from structural data. Here, we predict functional connectivity from structural connectivity, explicitly, by utilizing a predictive model based on PCA and CCA.

Abstract Adaptive behaviour, cognition and emotion are the result of a bewildering variety of brain spatio-temporal activity patterns. An important problem in neuroscience is to understand the mechanism by which the human brain's 100 billion neurons and 100 trillion synapses manage to produce this large repertoire of cortical configurations in a flexible manner.

Abstract We propose statistical inference based on the Least Absolute Shrinkage and Selective Operator (Lasso) regression as a framework to investigate the relationship between structural brain connectivity data (DTI) and functional connectivity data (fMRI). Regions of interest (ROIs) are obtained from an accurate atlas-based segmentation. We use direct structural connections to model indirect (higher-order) structural connectivity.

We present a novel probabilistic framework to learn across several subjects a mapping from brain anatomical connectivity to functional connectivity, ie the covariance structure of brain activity. This prediction problem must be formulated as a structured-output learning task, as the predicted parameters are strongly correlated. We introduce a model selection framework based on cross-validation with a parametrization-independent loss function suitable to the manifold of covariance matrices.

Positron emission tomography was used to investigate two competing hypotheses about the role of the left inferior frontal gyrus (IFG) in word generation. One proposes a domain-specific organization, with neural activation dependent on the type of information being processed, ie, surface sound structure or semantic. The other proposes a process-specific organization, with activation dependent on processing demands, such as the amount of selection needed to decide between competing lexical alternatives.