The posterior cingulate cortex is a highly connected and metabolically active brain region. Recent studies suggest it has an important cognitive role, although there is no consensus about what this ...is. The region is typically discussed as having a unitary function because of a common pattern of relative deactivation observed during attentionally demanding tasks. One influential hypothesis is that the posterior cingulate cortex has a central role in supporting internally-directed cognition. It is a key node in the default mode network and shows increased activity when individuals retrieve autobiographical memories or plan for the future, as well as during unconstrained 'rest' when activity in the brain is 'free-wheeling'. However, other evidence suggests that the region is highly heterogeneous and may play a direct role in regulating the focus of attention. In addition, its activity varies with arousal state and its interactions with other brain networks may be important for conscious awareness. Understanding posterior cingulate cortex function is likely to be of clinical importance. It is well protected against ischaemic stroke, and so there is relatively little neuropsychological data about the consequences of focal lesions. However, in other conditions abnormalities in the region are clearly linked to disease. For example, amyloid deposition and reduced metabolism is seen early in Alzheimer's disease. Functional neuroimaging studies show abnormalities in a range of neurological and psychiatric disorders including Alzheimer's disease, schizophrenia, autism, depression and attention deficit hyperactivity disorder, as well as ageing. Our own work has consistently shown abnormal posterior cingulate cortex function following traumatic brain injury, which predicts attentional impairments. Here we review the anatomy and physiology of the region and how it is affected in a range of clinical conditions, before discussing its proposed functions. We synthesize key findings into a novel model of the region's function (the 'Arousal, Balance and Breadth of Attention' model). Dorsal and ventral subcomponents are functionally separated and differences in regional activity are explained by considering: (i) arousal state; (ii) whether attention is focused internally or externally; and (iii) the breadth of attentional focus. The predictions of the model can be tested within the framework of complex dynamic systems theory, and we propose that the dorsal posterior cingulate cortex influences attentional focus by 'tuning' whole-brain metastability and so adjusts how stable brain network activity is over time.
The default mode network (DMN) is a set of widely distributed brain regions in the parietal, temporal and frontal cortex. These regions often show reductions in activity during attention-demanding ...tasks but increase their activity across multiple forms of complex cognition, many of which are linked to memory or abstract thought. Within the cortex, the DMN has been shown to be located in regions furthest away from those contributing to sensory and motor systems. Here, we consider how our knowledge of the topographic characteristics of the DMN can be leveraged to better understand how this network contributes to cognition and behaviour.
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GEOZS, IJS, IMTLJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK, ZAGLJ
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.
Inferior frontal cortex (IFC) modules that inhibit dominant behaviours are a popular feature in theories of cognitive dysfunction. However, the paradigms on which these theories are based fail to ...distinguish between inhibitory and non-inhibitory cognitive demands. Here we use four novel fMRI variants of the classic stop-signal task to test whether the IFC houses unique inhibitory modules. Our results demonstrate that IFC sub-regions are not functionally unique in their sensitivities to inhibitory cognitive demands, but instead form components of spatially distributed networks. These networks are most strongly activated when infrequent stimuli are being processed, regardless of behavioural inhibitory demands, and when novel tasks are being acquired, as opposed to when routine responses must be suppressed. We propose that there are no inhibitory modules within the frontal lobes and that behavioural inhibition is an emergent property of spatially distributed functional networks, each of which supports a broader class of cognitive demands.
There is considerable uncertainty about the function of the posterior cingulate cortex (PCC). The PCC is a major node within the default mode network (DMN) and has high metabolic activity and dense ...structural connectivity to widespread brain regions, which suggests it has a role as a cortical hub. The region appears to be involved in internally directed thought, for example, memory recollection. However, recent nonhuman primate work provides evidence for a more active role in the control of cognition, through signaling an environmental change and the need to alter behavior. For an organism to flexibly react to a changing environment, information processed in functionally distinct brain networks needs to be integrated by such a cortical hub. If the PCC is involved in this process, its brain activity should show a complex and dynamic pattern that partially reflects activity in other brain networks. Using fMRI in humans and a multivariate analysis, we demonstrate that the PCC shows this type of complex functional architecture, where echoes of multiple other brain networks are seen in separable yet overlapping subregions. For example, a predominantly ventral region shows strong functional connectivity to the rest of the DMN, whereas two subregions within the dorsal PCC show high connectivity to frontoparietal networks involved in cognitive control. PCC subregions showed distinct patterns of activity modulation during the performance of an attentionally demanding task, suggesting that parts of the dorsal PCC interact with frontoparietal networks to regulate the balance between internally and externally directed cognition.
Objective
The long‐term effects of traumatic brain injury (TBI) can resemble observed in normal ageing, suggesting that TBI may accelerate the ageing process. We investigate this using a neuroimaging ...model that predicts brain age in healthy individuals and then apply it to TBI patients. We define individuals' differences in chronological and predicted structural "brain age," and test whether TBI produces progressive atrophy and how this relates to cognitive function.
Methods
A predictive model of normal ageing was defined using machine learning in 1,537 healthy individuals, based on magnetic resonance imaging–derived estimates of gray matter (GM) and white matter (WM). This ageing model was then applied to test 99 TBI patients and 113 healthy controls to estimate brain age.
Results
The initial model accurately predicted age in healthy individuals (r = 0.92). TBI brains were estimated to be "older," with a mean predicted age difference (PAD) between chronological and estimated brain age of 4.66 years (±10.8) for GM and 5.97 years (±11.22) for WM. This PAD predicted cognitive impairment and correlated strongly with the time since TBI, indicating that brain tissue loss increases throughout the chronic postinjury phase.
Interpretation
TBI patients' brains were estimated to be older than their chronological age. This discrepancy increases with time since injury, suggesting that TBI accelerates the rate of brain atrophy. This may be an important factor in the increased susceptibility in TBI patients for dementia and other age‐associated conditions, motivating further research into the age‐like effects of brain injury and other neurological diseases. Ann Neurol 2015;77:571–581
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
The posterior cingulate cortex (PCC) is a central part of the default mode network (DMN) and part of the structural core of the brain. Although the PCC often shows consistent deactivation when ...attention is focused on external events, anatomical studies show that the region is not homogeneous, and electrophysiological recordings in nonhuman primates suggest that it is directly involved in some forms of attention. We report a functional magnetic resonance imaging study of an attentionally demanding task (either a zero- or two-back working memory task). Standard subtraction analysis within the PCC shows a relative deactivation as task difficulty increases. In contrast, a dual-regression functional connectivity analysis reveals a clear dissociation between ventral and dorsal parts of the PCC. As task difficulty increases, the ventral PCC shows reduced integration within the DMN and less anticorrelation with the cognitive control network (CCN) activated by the task. The dorsal PCC shows an opposite pattern, with increased DMN integration and more anticorrelation. At rest, the dorsal PCC also shows functional connectivity with both the DMN and attentional networks. As expected, these results provide evidence that the PCC is involved in supporting internally directed thought, as the region is more highly integrated with the DMN at low task demands. In contrast, the task-dependent increases in connectivity between the dorsal PCC and the CCN are consistent with a role for this region in modulating the dynamic interaction between these two networks controlling the efficient allocation of attention.
The classic mapping of distinct aspects of working memory (WM) to mutually exclusive brain areas is at odds with the distributed processing mechanisms proposed by contemporary network science theory. ...Here, we use machine-learning to determine how aspects of WM are dynamically coded in the human brain. Using cross-validation across independent fMRI studies, we demonstrate that stimulus domains (spatial, number and fractal) and WM processes (encode, maintain, probe) are classifiable with high accuracy from the patterns of network activity and connectivity that they evoke. This is the case even when focusing on 'multiple demands' brain regions, which are active across all WM conditions. Contrary to early neuropsychological perspectives, these aspects of WM do not map exclusively to brain areas or processing streams; however, the mappings from that literature form salient features within the corresponding multivariate connectivity patterns. Furthermore, connectivity patterns provide the most precise basis for classification and become fine-tuned as maintenance load increases. These results accord with a network-coding mechanism, where the same brain regions support diverse WM demands by adopting different connectivity states.
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 (R(2) = 0.64), executive function (R(2) = 0.56) and associative memory (R(2) = 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.
At the forefront of neuroimaging is the understanding of the functional architecture of the human brain. In most applications functional networks are assumed to be stationary, resulting in a single ...network estimated for the entire time course. However recent results suggest that the connectivity between brain regions is highly non-stationary even at rest. As a result, there is a need for new brain imaging methodologies that comprehensively account for the dynamic nature of functional networks. In this work we propose the Smooth Incremental Graphical Lasso Estimation (SINGLE) algorithm which estimates dynamic brain networks from fMRI data. We apply the proposed algorithm to functional MRI data from 24 healthy patients performing a Choice Reaction Task to demonstrate the dynamic changes in network structure that accompany a simple but attentionally demanding cognitive task. Using graph theoretic measures we show that the properties of the Right Inferior Frontal Gyrus and the Right Inferior Parietal lobe dynamically change with the task. These regions are frequently reported as playing an important role in cognitive control. Our results suggest that both these regions play a key role in the attention and executive function during cognitively demanding tasks and may be fundamental in regulating the balance between other brain regions.
•We present a methodology to model non-stationary functional brain connectivity•The SINGLE algorithm accurately estimates dynamic brain networks•The capabilities of SINGLE are demonstrated in simulation studies•We describe an application of SINGLE to a task-based fMRI study
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