Transient neural network dynamics in cognitive ageing.

It is important to maintain cognitive function in old age, yet the neural substrates that support successful cognitive ageing remain unclear. One factor that might be crucial, but has been overlooked due to limitations of previous data and methods, is the ability of brain networks to flexibly reorganize and coordinate over a millisecond time-scale. Magnetoencephalography (MEG) provides such temporal resolution, and can be combined with Hidden Markov Models (HMMs) to characterise transient neural states. We applied HMMs to resting-state MEG data from a large cohort (N=595) of population-based adults (aged 18-88), who also completed a range of cognitive tasks. Using multivariate analysis of neural and cognitive profiles, we found that decreased occurrence of "lower-order" brain networks, coupled with increased occurrence of "higher-order" networks, was associated with both increasing age and decreased fluid intelligence. These results favour theories of age-related reductions in neural efficiency over current theories of age-related functional compensation, and suggest that this shift might reflect a stable property of the ageing brain.

Self-reported sleep relates to hippocampal atrophy across the adult lifespan: results from the Lifebrain consortium.

OBJECTIVES: Poor sleep is associated with multiple age-related neurodegenerative and neuropsychiatric conditions. The hippocampus plays a special role in sleep and sleep-dependent cognition, and accelerated hippocampal atrophy is typically seen with higher age. Hence, it is critical to establish how the relationship between sleep and hippocampal volume loss unfolds across the adult lifespan. METHODS: Self-reported sleep measures and MRI-derived hippocampal volumes were obtained from 3105 cognitively normal participants (18-90 years) from major European brain studies in the Lifebrain consortium. Hippocampal volume change was estimated from 5116 MRIs from 1299 participants for whom longitudinal MRIs were available, followed up to 11 years with a mean interval of 3.3 years. Cross-sectional analyses were repeated in a sample of 21,390 participants from the UK Biobank. RESULTS: No cross-sectional sleep-hippocampal volume relationships were found. However, worse sleep quality, efficiency, problems, and daytime tiredness were related to greater hippocampal volume loss over time, with high scorers showing 0.22% greater annual loss than low scorers. The relationship between sleep and hippocampal atrophy did not vary across age. Simulations showed that the observed longitudinal effects were too small to be detected as age-interactions in the cross-sectional analyses. CONCLUSIONS: Worse self-reported sleep is associated with higher rates of hippocampal volume decline across the adult lifespan. This suggests that sleep is relevant to understand individual differences in hippocampal atrophy, but limited effect sizes call for cautious interpretation.

Age-related decline in positive emotional reactivity and emotion regulation in a population-derived cohort.

Human older age ushers in functional decline across the majority of cognitive domains. A notable exception seems to be affective processing, with older people reporting higher levels of emotional well-being. Here we evaluated age-related changes in emotional reactivity and regulation in a representative subsample (N = 104; age range: 23-88 years) of the population-derived Cambridge Centre for Ageing and Neuroscience cohort. Performance on a film-based emotion reactivity and regulation task in the magnetic resonance imaging scanner showed an age-related decline in positive reactivity, alongside a similar decline in the capacity to down-regulate negative affect. Decreased positivity with age was associated with reduced activation in the middle frontal gyrus. These findings, from the largest neuroimaging investigation to-date, provide no support for age-related increases in positive emotional reactivity.

Strong and specific associations between cardiovascular risk factors and white matter micro- and macrostructure in healthy aging.

Cardiovascular health declines with age, increasing the risk of hypertension and elevated heart rate in middle and old age. Here, we used multivariate techniques to investigate the associations between cardiovascular health (diastolic blood pressure, systolic blood pressure, and heart rate) and white matter macrostructure (lesion volume and number) and microstructure (as measured by diffusion-weighted imaging) in the cross-sectional, population-based Cam-CAN cohort (N = 667, aged 18-88). We found that cardiovascular health and age made approximately similar contributions to white matter health and explained up to 56% of variance therein. Lower diastolic blood pressure, higher systolic blood pressure, and higher heart rate were each strongly, and independently, associated with white matter abnormalities on all indices. Body mass and exercise were associated with white matter health, both directly and indirectly via cardiovascular health. These results highlight the importance of cardiovascular risk factors for white matter health across the adult lifespan and suggest that systolic blood pressure, diastolic blood pressure, and heart rate affect white matter health via separate mechanisms.

Abnormal salience signaling in schizophrenia: The role of integrative beta oscillations.

Aberrant salience attribution and cerebral dysconnectivity both have strong evidential support as core dysfunctions in schizophrenia. Aberrant salience arising from an excess of dopamine activity has been implicated in delusions and hallucinations, exaggerating the significance of everyday occurrences and thus leading to perceptual distortions and delusional causal inferences. Meanwhile, abnormalities in key nodes of a salience brain network have been implicated in other characteristic symptoms, including the disorganization and impoverishment of mental activity. A substantial body of literature reports disruption to brain network connectivity in schizophrenia. Electrical oscillations likely play a key role in the coordination of brain activity at spatially remote sites, and evidence implicates beta band oscillations in long-range integrative processes. We used magnetoencephalography and a task designed to disambiguate responses to relevant from irrelevant stimuli to investigate beta oscillations in nodes of a network implicated in salience detection and previously shown to be structurally and functionally abnormal in schizophrenia. Healthy participants, as expected, produced an enhanced beta synchronization to behaviorally relevant, as compared to irrelevant, stimuli, while patients with schizophrenia showed the reverse pattern: a greater beta synchronization in response to irrelevant than to relevant stimuli. These findings not only support both the aberrant salience and disconnectivity hypotheses, but indicate a common mechanism that allows us to integrate them into a single framework for understanding schizophrenia in terms of disrupted recruitment of contextually appropriate brain networks.

Complexity measures in magnetoencephalography: measuring "disorder" in schizophrenia.

This paper details a methodology which, when applied to magnetoencephalography (MEG) data, is capable of measuring the spatio-temporal dynamics of 'disorder' in the human brain. Our method, which is based upon signal entropy, shows that spatially separate brain regions (or networks) generate temporally independent entropy time-courses. These time-courses are modulated by cognitive tasks, with an increase in local neural processing characterised by localised and transient increases in entropy in the neural signal. We explore the relationship between entropy and the more established time-frequency decomposition methods, which elucidate the temporal evolution of neural oscillations. We observe a direct but complex relationship between entropy and oscillatory amplitude, which suggests that these metrics are complementary. Finally, we provide a demonstration of the clinical utility of our method, using it to shed light on aberrant neurophysiological processing in schizophrenia. We demonstrate significantly increased task induced entropy change in patients (compared to controls) in multiple brain regions, including a cingulo-insula network, bilateral insula cortices and a right fronto-parietal network. These findings demonstrate potential clinical utility for our method and support a recent hypothesis that schizophrenia can be characterised by abnormalities in the salience network (a well characterised distributed network comprising bilateral insula and cingulate cortices).

Glutamatergic correlates of gamma-band oscillatory activity during cognition: a concurrent ER-MRS and EEG study.

Frequency specific synchronisation of neuronal firing within the gamma-band (30-70 Hz) appears to be a fundamental correlate of both basic sensory and higher cognitive processing. In-vitro studies suggest that the neurochemical basis of gamma-band oscillatory activity is based on interactions between excitatory (i.e. glutamate) and inhibitory (i.e. GABA) neurotransmitter concentrations. However, the nature of the relationship between excitatory neurotransmitter concentration and changes in gamma band activity in humans remains undetermined. Here, we examine the links between dynamic glutamate concentration and the formation of functional gamma-band oscillatory networks. Using concurrently acquired event-related magnetic resonance spectroscopy and electroencephalography, during a repetition-priming paradigm, we demonstrate an interaction between stimulus type (object vs. abstract pictures) and repetition in evoked gamma-band oscillatory activity, and find that glutamate levels within the lateral occipital cortex, differ in response to these distinct stimulus categories. Importantly, we show that dynamic glutamate levels are related to the amplitude of stimulus evoked gamma-band (but not to beta, alpha or theta or ERP) activity. These results highlight the specific connection between excitatory neurotransmitter concentration and amplitude of oscillatory response, providing a novel insight into the relationship between the neurochemical and neurophysiological processes underlying cognition.

Methodology for improved detection of low concentration metabolites in MRS: optimised combination of signals from multi-element coil arrays.

State of the art magnetic resonance imaging (MRI) scanners are generally equipped with multi-element receive coils; 16 or 32 channel coils are common. Their development has been predominant for parallel imaging to enable faster scanning. Less consideration has been given to localized magnetic resonance spectroscopy (MRS). Multinuclear studies, for example (31)P or (13)C MRS, are often conducted with a single element coil located over the region of interest. (1)H MRS studies have generally employed the same multi-element coils used for MRI, but little consideration has been given as to how the spectroscopic data from the different channels are combined. In many cases it is simply co-added with detrimental effect on the signal to noise ratio. In this study, we derive the optimum method for combining multi-coil data, namely weighting with the ratio of signal to the square of the noise. We show that provided that the noise is uncorrelated, this is the theoretical optimal combination. The method is demonstrated for in vivo proton MRS data acquired using a 32 channel receive coil at 7T in four different brain areas; left motor and right motor, occipital cortex and medial frontal cortex.

Investigating the electrophysiological basis of resting state networks using magnetoencephalography.

In recent years the study of resting state brain networks (RSNs) has become an important area of neuroimaging. The majority of studies have used functional magnetic resonance imaging (fMRI) to measure temporal correlation between blood-oxygenation-level-dependent (BOLD) signals from different brain areas. However, BOLD is an indirect measure related to hemodynamics, and the electrophysiological basis of connectivity between spatially separate network nodes cannot be comprehensively assessed using this technique. In this paper we describe a means to characterize resting state brain networks independently using magnetoencephalography (MEG), a neuroimaging modality that bypasses the hemodynamic response and measures the magnetic fields associated with electrophysiological brain activity. The MEG data are analyzed using a unique combination of beamformer spatial filtering and independent component analysis (ICA) and require no prior assumptions about the spatial locations or patterns of the networks. This method results in RSNs with significant similarity in their spatial structure compared with RSNs derived independently using fMRI. This outcome confirms the neural basis of hemodynamic networks and demonstrates the potential of MEG as a tool for understanding the mechanisms that underlie RSNs and the nature of connectivity that binds network nodes.