Spatially heterogeneous structure-function coupling in haemodynamic and electromagnetic brain networks

How to measure functional connectivity using resting-state fMRI? A comprehensive empirical exploration of different connectivity metrics

BACKGROUND

Functional connectivity in the context of functional magnetic resonance imaging is typically quantified by Pearson´s or partial correlation between regional time series of the blood oxygenation level dependent signal. However, a recent interdisciplinary methodological work proposes >230 different metrics to measure similarity between different types of time series.

OBJECTIVE

Hence, we systematically evaluated how the results of typical research approaches in functional neuroimaging vary depending on the functional connectivity metric of choice. We further explored which metrics most accurately detect presumed reductions in connectivity related to age and malignant brain tumors, aiming to initiate a debate on the best approaches for assessing brain connectivity in functional neuroimaging research.

METHODS

We addressed both research questions using four independent neuroimaging datasets, comprising multimodal data from a total of 1187 individuals. We analyzed resting-state functional sequences to calculate functional connectivity using 20 representative metrics from four distinct mathematical domains. We further used T1- and T2-weighted images to compute regional brain volumes, diffusion-weighted imaging data to build structural connectomes, and pseudo-continuous arterial spin labeling to measure regional brain perfusion.

RESULTS

First, our findings demonstrate that the results of typical functional neuroimaging approaches differ fundamentally depending on the functional connectivity metric of choice. Second, we show that correlational and distance metrics are most appropriate to cover reductions in connectivity linked to aging. In this context, partial correlation performs worse than other correlational metrics. Third, our findings suggest that the FC metric of choice depends on the utilized scanning parameters, the regions of interest, and the individual investigated. Lastly, beyond the major objective of this study, we provide evidence in favor of brain perfusion measured via pseudo-continuous arterial spin labeling as a robust neural entity mirroring age-related neural and cognitive decline.

CONCLUSION

Our empirical evaluation supports a recent theoretical functional connectivity framework. Future functional imaging studies need to comprehensively define the study-specific theoretical property of interest, the methodological property to assess the theoretical property, and the confounding property that may bias the conclusions.

Global, mesoscale and local investigation into resting state fMRI brain network attributes of sleep quality

Poor sleep quality has been found to be associated with functional abnormalities in a few regions of the human brain. However, the brain is a dynamic network cooperation system, and it is necessary to study the relationship between sleep quality and the whole-brain network activity at multiple levels. A total of 115 college students underwent resting-state fMRI (rs-fMRI) and completed the Pittsburgh Sleep Quality Index (PSQI). Students were divided into good-quality sleepers (N = 65, PSQI<5) and poor-quality sleepers (N = 50, PSQI≥5). The fMRI data were analyzed using graph theory and machine learning methods to compare between-group differences in functional network topology at different levels. Global analysis shows the poor sleep quality group had lower small-worldness and higher characteristic path length. The mesoscale analysis demonstrates the subnetwork functions of the right middle frontal gyrus, bilateral superior parietal gyrus, bilateral caudate nucleus, and right superior temporal gyrus are important indicators that distinguish between the two groups. Local analysis shows the nodal degree of the left inferior occipital gyrus and left postcentral gyrus significantly differed between the two groups. Taken together, these findings deepen the macroscopic understanding of the relationship between sleep quality and resting functional network topology patterns.

Disrupted coordination between primary and high-order cognitive networks in Parkinson's disease based on morphological and functional analysis

Patients with Parkinson's disease (PD) exhibit structural and functional alterations in both primary and high-order cognitive networks, but the interactions within aberrant functional networks and relevant structural foundation remains unexplored. In this study, the functional networks (FN) and the morphometric similarity networks (MSN) were constructed respectively based on the time-series data and gray matter volume from the MRI data of PD patients and controls. The efficiency, average controllability and k-shell values of the FN and MSN were calculated to evaluate their ability of information transmission and identify structural and functional abnormalities in PD. The abnormal regions were categorized into five types: regions with MSN abnormalities, regions with FN abnormalities, regions with both MSN and FN abnormalities, regions with abnormalities only in MSN but not in FN and regions with abnormalities only in FN but not in MSN. Further, the dynamic causal model (DCM) was used to evaluate the causal relationship of information flow between the identified regions. In the network property analysis of the FN, PD patients showed decreased global efficiency and connectivity in the visual network (VIS) and increased global efficiency in higher-order cognitive networks, including the ventral attention network (VAN), default mode network (DMN), and the limbic network (LIM) but no difference in MSN. In the DCM analysis of the regions, PD patients exhibited increased excitatory transition from the visual areas to the superior frontal gyrus, whereas had disturbed information flow from the visual areas to the insula and the orbitofrontal cortex. These findings suggest changes in structural and functional brain of PD patients, and advance our understanding of PD pathogenesis from different neural dimensions.

Comparing structure-function relationships in brain networks using EEG and fNIRS

Identifying relationships between structural and functional networks is crucial for understanding the large-scale organization of the human brain. The potential contribution of emerging techniques like functional near-infrared spectroscopy to investigate the structure-functional relationship has yet to be explored. In our study, using simultaneous Electroencephalography (EEG) and Functional near-infrared spectroscopy (fNIRS) recordings from 18 subjects, we characterize global and local structure-function coupling using source-reconstructed EEG and fNIRS signals in both resting state and motor imagery tasks, as this relationship during task periods remains underexplored. Employing the mathematical framework of graph signal processing, we investigate how this relationship varies across electrical and hemodynamic networks and different brain states. Results show that fNIRS structure-function coupling resembles slower-frequency EEG coupling at rest, with variations across brain states and oscillations. Locally, the relationship is heterogeneous, with greater coupling in the sensory cortex and increased decoupling in the association cortex, following the unimodal to transmodal gradient. Discrepancies between EEG and fNIRS are noted, particularly in the frontoparietal network. Cross-band representations of neural activity revealed lower correspondence between electrical and hemodynamic activity in the transmodal cortex, irrespective of brain state while showing specificity for the somatomotor network during a motor imagery task. Overall, these findings initiate a multimodal comprehension of structure-function relationship and brain organization when using affordable functional brain imaging.

Linking the microarchitecture of neurotransmitter systems to large-scale MEG resting state networks

Neuronal oscillations are ubiquitous in brain activity at all scales and their synchronization dynamics are essential for information processing in neuronal systems. The underlying synaptic mechanisms, while mainly based on GABA- and glutamatergic neurotransmission, are influenced by neuromodulatory systems that have highly variable densities of neurotransmitter receptors and transporters across the cortical mantle. How they constrain the network structures of interacting oscillations has remained a central unaddressed question. We asked here whether the receptor and transporter densities covary with the frequency-specific neuroanatomical patterns of inter-areal phase synchrony (PS) and amplitude correlation (AC) networks in resting-state magnetoencephalography (MEG) data. Network centrality in delta and gamma frequencies covaried positively with GABA-, NMDA-, dopaminergic-, and most serotonergic receptor and transporter densities while covariance was negative in alpha and beta bands. These results show that local receptor microarchitecture shapes macro-scale oscillation networks in spectrally specific patterns.

Multiscale and multimodal signatures of structure-function coupling variability across the human neocortex

The relationship between the brain's structural wiring and its dynamic activity is thought to vary regionally, implying that the mechanisms underlying structure-function coupling may differ depending on a region's position within the brain's hierarchy. To better bridge the gap between structure and function, it is crucial to identify the factors shaping this regionality, not only in terms of how static functional connectivity aligns with structure, but also regarding the time-domain variability of this interplay. Here we map structure - function coupling and its time-domain variability and relate them to the heterogeneity of the cortex. We show that these two properties split the cortical landscape into two districts anchored to the opposite ends of the brain's hierarchy. By looking at statistical relationships with layer-specific gene transcription, T1w/T2 w ratio, and synaptic density, we show that macro-scale structure-function coupling may be rooted in the brain's microstructure and meso‑scale laminar specialization. Finally, we demonstrate that a lower and more variable alignment of function and structure may bestow the emergence of unique functional dynamics.

Linking structural and functional changes during aging using multilayer brain network analysis

Brain structure and function are intimately linked, however this association remains poorly understood and the complexity of this relationship has remained understudied. Healthy aging is characterised by heterogenous levels of structural integrity changes that influence functional network dynamics. Here, we use the multilayer brain network analysis on structural (diffusion weighted imaging) and functional (magnetoencephalography) data from the Cam-CAN database. We found that the level of similarity of connectivity patterns between brain structure and function in the parietal and temporal regions (alpha frequency band) is associated with cognitive performance in healthy older individuals. These results highlight the impact of structural connectivity changes on the reorganisation of functional connectivity associated with the preservation of cognitive function, and provide a mechanistic understanding of the concepts of brain maintenance and compensation with aging. Investigation of the link between structure and function could thus represent a new marker of individual variability, and of pathological changes.

Towards a biologically annotated brain connectome

The brain is a network of interleaved neural circuits. In modern connectomics, brain connectivity is typically encoded as a network of nodes and edges, abstracting away the rich biological detail of local neuronal populations. Yet biological annotations for network nodes - such as gene expression, cytoarchitecture, neurotransmitter receptors or intrinsic dynamics - can be readily measured and overlaid on network models. Here we review how connectomes can be represented and analysed as annotated networks. Annotated connectomes allow us to reconceptualize architectural features of networks and to relate the connection patterns of brain regions to their underlying biology. Emerging work demonstrates that annotated connectomes help to make more veridical models of brain network formation, neural dynamics and disease propagation. Finally, annotations can be used to infer entirely new inter-regional relationships and to construct new types of network that complement existing connectome representations. In summary, biologically annotated connectomes offer a compelling way to study neural wiring in concert with local biological features.