Brain Connectivity

Cognitive neuroscience explores the mechanisms of cognition by studying its structural and functional brain correlates. Many studies have combined structural and functional neuroimaging techniques to uncover the complex relationship between them. In this study, we report the first systematic review that assesses how information from structural and functional neuroimaging methods can be integrated to investigate the brain substrates of cognition.

Web of Science and Scopus databases were searched for studies of healthy young adult populations that collected cognitive data and structural and functional neuroimaging data.

Five percent of screened studies met all inclusion criteria. Next, 50% of included studies related cognitive performance to brain structure and function without quantitative analysis of the relationship. Finally, 31% of studies formally integrated structural and functional brain data. Overall, many studies consider either structural or functional neural correlates of cognition, and of those that consider both, they have rarely been integrated. We identified four emergent approaches to the characterization of the relationship between brain structure, function, and cognition; comparative, predictive, fusion, and complementary.

We discuss the insights provided in each approach about the relationship between brain structure and function and how it impacts cognitive performance. In addition, we discuss how authors can select approaches to suit their research questions.

The relationship between structural and functional brain networks and their relationship to cognition is a matter of current investigations. This work surveys how researchers have studied the relationship between brain structure and function and its impact on cognitive function in healthy adult populations. We review four emergent approaches of quantitative analysis of this multivariate problem; comparative, predictive, fusion, and complementary. We explain the characteristics of each approach, discuss the insights provided in each approach, and how authors can combine approaches to suit their research questions.

Recent neuroimaging studies on upper-limb amputation have revealed the reorganization of bilateral sensorimotor cortex after sensory deprivation, underpinning the assumption of changes in the interhemispheric connections. In the present study, using functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), we aim to explore the alterations in the interhemispheric functional and structural connectivity after upper-limb amputation.

Twenty-two upper-limb amputees and 15 age- and sex-matched healthy controls were recruited for MRI scanning. The amputees were further divided into subgroups by amputation side and residual limb pain (RLP). DTI metrics of corpus callosum (CC) subregions and resting-state functional connectivity (FC) between the bilateral sensorimotor cortices were measured for each participant. Linear mixed models were carried out to investigate the relationship of interhemispheric connectivity with the amputation, amputation side, and RLP.

Compared with healthy controls, upper-limb amputees showed lower axial diffusivity (AD) in CC subregions II and III. Subgroup analyses showed that the dominant hand amputation induced significant microstructural changes in CC subregion III. In addition, only amputees with RLP showed decreased fractional anisotropy and AD in CC, which was also correlated with the intensity of RLP. No significant changes in interhemispheric FC were found after upper-limb amputation.

The present study demonstrated that the interhemispheric structural connectivity rather than FC degenerated after upper-limb amputation, and the degeneration of interhemispheric structural connectivity was shown to be relevant to the amputation side and the intensity of RLP.

Neuroimaging studies have revealed the functional reorganization of bilateral sensorimotor cortex after amputation, with expanded activation from the intact hemisphere to the deprived hemisphere. Our findings indicated a degeneration of interhemispheric white matter connections in upper-limb amputees, unveiling the underlying structural basis for bilateral functional reorganization after amputation.

In older people with mild cognitive impairment (MCI), the relationship between early changes in functional connectivity and in vivo changes in key neurometabolites is not known. Two established correlates of MCI diagnosis are decreased N-acetylaspartate (NAA) in the hippocampus, indicative of decreased neuronal integrity, and changes in the default mode network (DMN) functional network. If and how these measures interrelate is yet to be established, and such understanding may provide insight into the processes underpinning observed cognitive decline.

To determine the relationship between NAA levels in the left hippocampus and functional connectivity within the DMN in an aging cohort.

In a sample of 51 participants with MCI and 30 controls, hippocampal NAA was determined using magnetic resonance spectroscopy, and DMN connectivity was quantified using resting-state functional MRI. The association between hippocampal NAA and the DMN functional connectivity was tested within the MCI group and separately within the control group.

In the DMN, we showed a significant inverse association between functional connectivity and hippocampal NAA in 20 specific brain connections for patients with MCI. This was despite no evidence of any associations in the healthy control group or group differences in either of these measures alone.

This study suggests that decreased neuronal integrity in the hippocampus is associated with functional change within the DMN for those with MCI, in contrast to healthy older adults. These results highlight the potential of multimodal investigations to better understand the processes associated with cognitive decline.

This study measured activity within the default mode network (DMN) and quantified N-acetylaspartate (NAA), a measure of neuronal integrity, within the hippocampus in participants with mild cognitive impairment (MCI) and healthy controls. In participants with MCI, NAA levels were inversely associated with connectivity between specific regions of the DMN, a relationship not evident in healthy controls. This association was present even in the absence of group differences in DMN connectivity or NAA levels. This research illustrates the possibility of using multiple magnetic resonance modalities for more sensitive measures of early cognitive decline to identify and intervene earlier.

Hidden Markov models (HMMs) are a popular choice to extract and examine recurring patterns of activity or functional connectivity in neuroimaging data, both in terms of spatial patterns and their temporal progression. Although many diverse HMMs have been applied to neuroimaging data, most have defined states based on activity levels (intensity-based [IB] states) rather than patterns of functional connectivity between brain areas (connectivity-based states), which is problematic if we want to understand connectivity dynamics: IB states are unlikely to provide comprehensive information about dynamic connectivity patterns.

We addressed this problem by introducing a new HMM that defines states based on full functional connectivity (FFC) profiles among brain regions. We empirically explored the behavior of this new model in comparison to existing approaches based on IB or summed functional connectivity states using the Human Connectome Project unrelated 100 functional magnetic resonance imaging “resting-state” dataset.

Our FFC model discovered connectivity states with more distinguishable (i.e., unique and separable from each other) patterns than previous approaches, and recovered simulated connectivity-based states more faithfully than the other models tested.

Thus, if our goal is to extract and interpret connectivity states in neuroimaging data, our new model outperforms previous methods, which miss crucial information about the evolution of functional connectivity in the brain.

Hidden Markov models (HMMs) can be used to investigate brain states noninvasively. Previous models “recover” connectivity from intensity-based hidden states, or from connectivity “summed” across nodes. In this study, we introduce a novel connectivity-based HMM and show how it can reveal true connectivity hidden states under minimal assumptions.

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating disease with unknown pathophysiology. Functional magnetic resonance imaging (fMRI) studies in ME/CFS have reported disparate connectivities for the brain salience (SA) network and default mode network (DMN).

In this study, we acquired resting-state and task fMRI with an advanced scanner for improved subject numbers: 24 healthy controls (HC) and 42 ME/CFS patients, 18 meeting the International Consensus Criteria (ICC) and 24 meeting the Fukuda criteria. We evaluated mean functional connectivity between the SA network and DMN hubs and subcortical regions known to be involved in ME/CFS. We tested the hypothesis that ME/CFS connectivity differed from HC and the ICC and Fukuda classes are distinguished by different connectivities with HC for different pairs of SA network, DMN, or subcortical hubs.

During resting-state fMRI, only two connections differed from HC, both for Fukuda ME/CFS and both with an SA network hub. During task fMRI, 10 ME/CFS connections differed from HC, 5 for ICC, and 5 for Fukuda. None was common to both classes. Eight of the 10 different connections involved an SA network hub, six of the 10 were weaker in ME/CFS, and 4 were stronger. SA network connections to the hippocampus and brainstem reticular activation system (RAS) differed from and were stronger than HC.

The SA network mediates the relative activity of the DMN and executive networks and an imbalance will have functional consequences. The RAS and hippocampus modulate cortical activation. Different regulatory connections are consistent with the impaired cognitive performance and sleep–wake cycle of ME/CFS. Different neuropathologies are involved in ICC and Fukuda classes.

Criteria for the diagnosis of the debilitating myalgic encephalitis/chronic fatigue syndrome (ME/CFS) condition have evolved over two decades. Physicians are now instructed that the recent, more stringent (ICC) questionnaire criteria define a disease that is distinct from those remaining subjects defined by the previous Fukuda criteria. This work reports the remarkable finding that functional magnetic resonance imaging connectivity can differentiate between these two classes of ME/CFS. This is the first objective medical evidence that the questionnaire-based diagnosis does indeed differentiate between two different disease states. This facilitates a clearer understanding of ME/CFS and can better direct research and therapy development.