Introducing Structural Reliance: A New Method to Assess Structure-Function Coupling in the Brain.

Bipolar disorder (BD) is associated with a high risk of suicidality, and it is challenging to predict suicide attempts in clinical practice to date. Although structural and functional connectivity alterations from neuroimaging studies have been previously reported in BD with suicide attempts, little is known about how abnormal structural and functional connectivity relates to each other. Here, we hypothesize that structure connectivity constrains functional connectivity, and structural-functional coupling is a more sensitive biomarker to detect subtle brain abnormalities than any single modality in BD patients with a current major depressive episode who had attempted suicide. By investigating structural and resting-state fMRI connectivity, as well as their coupling among 191 BD depression patients with or without a history of suicide attempts and 113 healthy controls, we found that suicide attempters in BD depression patients showed significantly decreased central-temporal structural connectivity, increased frontal-temporal functional connectivity, along with decreased structural-functional coupling compared with non-suicide attempters. Crucially, the altered structural connectivity network predicted the abnormal functional connectivity network profile, and the structural-functional coupling was significantly correlated with suicide risk but not with depression or anxiety severity. Our findings suggest that the structural connectome is the key determinant of brain dysfunction, and structural-functional coupling could serve as a valuable trait-like biomarker for BD suicidal predication over and above the intramodality network connectivity. Such a measure can have clinical implications for early identification of suicide attempters with BD depression and inform strategies for prevention.

Brain structure-function coupling provides signatures for task decoding and individual fingerprinting

The nuclei of the brainstem fundamentally modulate neuronal activity throughout the central nervous system. Yet connectome reconstructions typically do not include the brainstem because it is notoriously difficult to image. As a result, the influence of the brainstem on structure-function coupling in the brain is unknown. Here we use high-resolution 7 Tesla magnetic resonance imaging (MRI) to reconstruct structural and functional brain connectomes encompassing cortex and 58 brainstem nuclei spanning the midbrain, pons, and medulla. We identify structural connectional profiles of individual brainstem nuclei to the cortex and find that they align with a spectrum of functions, spanning sensory and motor processing to higher-order cognition. Structural and functional connectivity in brainstem-augmented connectomes are positively correlated, and pairs of regions with direct anatomical projections display greater functional connectivity than pairs without. Structure-function coupling is heterogeneous across brainstem nuclei, with greatest coupling in both modulatory and relay nuclei. Collectively, this work presents an initial step towards understanding how the brainstem shapes structure-function relationships in the brain.

Control energy detects discrepancies in good vs. poor readers' structural-functional coupling during a rhyming task

Adolescent MDD has become a significant public health issue, yet its underlying mechanisms remain unclear. Multimodal brain imaging techniques offer a powerful method for exploring complex mental disorders. However, evidence focusing on the multimodal brain networks and structural-functional coupling in adolescent depression is still limited. Participants with major depressive disorder (MDD) were Han Chinese individuals aged 13 to 18 who had been unmedicated for at least two weeks. We conducted multimodal MRI acquisitions, including structural MRI (sMRI), resting-state functional MRI (rsfMRI), and Diffusion Tensor Imaging (DTI). The cortex was parceled into 360 regions using the HCP-MMP atlas. Functional connectivity and deterministic structural connectivity matrices were constructed, and structural-functional coupling coefficients were calculated. Differences in connectivity and coupling coefficients between the MDD and healthy controls (HCs) groups were identified. A total of 25 adolescents with MDD (mean age: 15.68 years, standard deviation [SD]: 1.18; Female: 21 (84.00%)) and 27hCs (mean age: 14.30 years, standard deviation [SD]: 1.51; Female: 13 (48.15%)) were included in the analysis. There were 9 structural connections and 122 functional connections that differed between the two groups, involving multiple cortical regions. Additionally, we identified structural-functional coupling differences in three brain areas, specifically the posterior cingulate cortex and the ventral visual cortex. Adolescent MDD involves disruptions in brain structural networks, functional networks, and structural-functional coupling. These differing indicators may serve as potential biomarkers for adolescent MDD.

To decipher the organizational styles of neural underpinning in major depressive disorder (MDD), the current article reviewed recent neuroimaging studies (published during 2015–2020) that applied graph theory approach to the diffusion tensor imaging data or functional brain activation data acquired during task-free resting state. The global network organization of resting-state functional connectivity network in MDD were diverse according to the onset age and medication status. Intra-modular functional connections were weaker in MDD compared to healthy controls (HC) for default mode and limbic networks. Weaker local graph metrics of default mode, frontoparietal, and salience network components in MDD compared to HC were also found. On the contrary, brain regions comprising the limbic, sensorimotor, and subcortical networks showed higher local graph metrics in MDD compared to HC. For the brain white matter-based structural connectivity network, the global network organization was comparable to HC in adult MDD but was attenuated in late-life depression. Local graph metrics of limbic, salience, default-mode, subcortical, insular, and frontoparietal network components in structural connectome were affected from the severity of depressive symptoms, burden of perceived stress, and treatment effects. Collectively, the current review illustrated changed global network organization of structural and functional brain connectomes in MDD compared to HC and were varied according to the onset age and medication status. Intra-modular functional connectivity within the default mode and limbic networks were weaker in MDD compared to HC. Local graph metrics of structural connectome for MDD reflected severity of depressive symptom and perceived stress, and were also changed after treatments. Further studies that explore the graph metrics-based neural correlates of clinical features, cognitive styles, treatment response and prognosis in MDD are required.

BackgroundDifficulties with performance of functional activities may result from cognitive and/or physical impairments. To date, there has not been a clear delineation of the physical and cognitive demands of activities of daily living. ObjectivesTo quantify the relative physical and cognitive demands required to complete typical functional activities in older adults. DesignExpert panel survey. SettingWeb-based platform. ParticipantsEleven experts from 8 academic medical centers and 300 community-dwelling elderly adults age 70 and older scheduled for elective noncardiac surgery from 2 academic medical centers. MethodsSum scores of expert ratings were calculated and then validated against objective data collected from a prospective longitudinal study. Main Outcome MeasurementsCorrelation between expert ratings and objective neuropsychologic tests (memory, language, complex attention) and physical measures (gait speed and grip strength) for performance-based tasks. ResultsManaging money, self-administering medications, using the telephone, and preparing meals were rated as requiring significantly more cognitive demand, whereas walking and transferring, moderately strenuous activities, and climbing stairs were assessed as more physically demanding. Largely cognitive activities correlated with objective neuropsychologic performance (r = 0.13-0.23, P < .05) and largely physical activities correlated with physical performance (r = 0.15-0.46, P < .05). ConclusionsQuantifying the degree of cognitive and/or physical demand for completing a specific task adds an additional dimension to standard measures of functional assessment. This additional information may significantly influence decisions about rehabilitation, postacute care needs, treatment plans, and caregiver education.

Resting-state functional MRI (rs-fMRI) is a popular and widely used technique to explore the brain's functional organization and to examine whether it is altered in neurological or mental disorders. The most common approach for its analysis targets the measurement of the synchronized fluctuations between brain regions, characterized as functional connectivity (FC), typically relying on pairwise correlations in activity across different brain regions. While hugely successful in exploring state- and disease-dependent network alterations, these statistical graph theory tools suffer from two key limitations. First, they discard useful information about the rich frequency content of the fMRI signal. The rich spectral information now achievable from advances in fast multiband acquisitions is consequently being underutilized. Second, the analyzed FCs are phenomenological without a direct neurobiological underpinning in the underlying structures and processes in the brain. There does not currently exist a complete generative model framework for whole brain resting fMRI that is informed by its underlying biological basis in the structural connectome. Here we propose that a different approach can solve both challenges at once: the use of an appropriately realistic yet parsimonious biophysics-informed signal generation model followed by graph spectral (i.e., eigen) decomposition. We call this model a spectral graph model (SGM) for fMRI, using which we can not only quantify the structure-function relationship in individual subjects, but also condense the variable and individual-specific repertoire of fMRI signal's spectral and spatial features into a small number of biophysically interpretable parameters. We expect this model-based analysis of rs-fMRI that seamlessly integrates with structure can be used to examine state and trait characteristics of structure-function relationships in a variety of brain disorders.

In the normal aging process, the functional connectome restructures and shows a shift from more segregated to more integrated brain networks, which manifests itself in highly different cognitive performances in older adults. Underpinnings of this reorganization are not fully understood, but may be related to age-related differences in structural connectivity, the underlying scaffold for information exchange between regions. The structure-function relationship might be a promising factor to understand the neurobiological sources of interindividual cognitive variability, but remain unclear in older adults. Here, we used diffusion weighted and resting-state functional magnetic resonance imaging as well as cognitive performance data of 573 older subjects from the 1000BRAINS cohort (55-85 years, 287 males) and performed a partial least square regression on 400 regional functional and structural connectivity (FC and SC, respectively) estimates comprising seven resting-state networks. Our aim was to identify FC and SC patterns that are, together with cognitive performance, characteristic of the older adults aging process. Results revealed three different aging profiles prevalent in older adults. FC was found to behave differently depending on the severity of age-related SC deteriorations. A functionally highly interconnected system is associated with a structural connectome that shows only minor age-related decreases. Because this connectivity profile was associated with the most severe age-related cognitive decline, a more interconnected FC system in older adults points to a process of dedifferentiation. Thus, functional network integration appears to increase primarily when SC begins to decline, but this does not appear to mitigate the decline in cognitive performance.

A comprehensive decoding of cognitive load

Unifying integration and segregation in the brain has been a fundamental puzzle in neuroscience ever since the conception of the “binding problem.” Here, we introduce a framework that places integration and segregation within a continuum based on a fundamental property of the brain–its structural connectivity graph Laplacian harmonics and a new feature we term the gap-spectrum. This framework organizes harmonics into three regimes–integrative, segregative, and degenerate–that together account for various group-level properties. Integrative and segregative harmonics occupy the ends of the continuum, and they share properties such as reproducibility across individuals, stability to perturbation, and involve “bottom-up” sensory networks. Degenerate harmonics are in the middle of the continuum, and they are subject-specific, flexible, and involve “top-down” networks. The proposed framework accommodates inter-subject variation, sensitivity to changes, and structure-function coupling in ways that offer promising avenues for studying cognition and consciousness in the brain.

Brain activity during rest displays complex, rapidly evolving patterns in space and time. Structural connections comprising the human connectome are hypothesized to impose constraints on the dynamics of this activity. Here, we use magnetoencephalography (MEG) to quantify the extent to which fast neural dynamics in the human brain are constrained by structural connections inferred from diffusion MRI tractography. We characterize the spatio-temporal unfolding of whole-brain activity at the millisecond scale from source-reconstructed MEG data, estimating the probability that any two brain regions will significantly deviate from baseline activity in consecutive time epochs. We find that the structural connectome relates to, and likely affects, the rapid spreading of neuronal avalanches, evidenced by a significant association between these transition probabilities and structural connectivity strengths (r = 0.37, p<0.0001). This finding opens new avenues to study the relationship between brain structure and neural dynamics.

Functional connectivity is scaffolded by the structural connections of the brain. Disruptions of either structural or functional connectivity can lead to deficits in cognitive functions and increase the risk for neurodevelopmental disorders such as attention deficit hyperactivity disorder (ADHD). To date, very little research has examined the association between structural and functional connectivity in typical development, while no studies have attempted to understand the development of structure-function coupling in children with ADHD. 175 individuals (84 typically developing children and 91 children with ADHD) participated in a longitudinal neuroimaging study with up to three waves. In total, we collected 278 observations between the ages 9 and 14 (139 each in typically developing controls and ADHD). Regional measures of structure-function coupling were calculated at each timepoint using Spearman’s rank correlation and mixed effect models were used to determine group differences and longitudinal changes in coupling over time. In typically developing children, we observed increases in structure-function coupling strength across multiple higher-order cognitive and sensory regions. Overall, weaker coupling was observed in children with ADHD, mainly in the prefrontal cortex, superior temporal gyrus, and inferior parietal cortex. Further, children with ADHD showed an increased rate of coupling strength predominantly in the inferior frontal gyrus, superior parietal cortex, precuneus, mid-cingulate, and visual cortex, compared to no corresponding change over time in typically developing controls. This study provides evidence of the joint maturation of structural and functional brain connections in typical development across late childhood to mid-adolescence, particularly in regions that support cognitive maturation. Findings also suggest that children with ADHD exhibit different patterns of structure-function coupling, suggesting atypical patterns of coordinated white matter and functional connectivity development predominantly in the regions overlapping with the default mode network, salience network, and dorsal attention network during late childhood to mid-adolescence.

In the central nervous system, gliomas are the most common, but complex primary tumors. Genome-based molecular and clinical studies have revealed different classifications and subtypes of gliomas. Neuroradiological approaches have non-invasively provided a macroscopic view for surgical resection and therapeutic effects. The connectome is a structural map of a physical object, the brain, which raises issues of spatial scale and definition, and it is calculated through diffusion magnetic resonance imaging (MRI) and functional MRI. In this study, we reviewed the basic principles and attributes of the structural and functional connectome, followed by the alternations of connectomes and their influences on glioma. To extend the applications of connectome, we demonstrated that a series of multi-center projects still need to be conducted to systemically investigate the connectome and the structural–functional coupling of glioma. Additionally, the brain–computer interface based on accurate connectome could provide more precise structural and functional data, which are significant for surgery and postoperative recovery. Besides, integrating the data from different sources, including connectome and other omics information, and their processing with artificial intelligence, together with validated biological and clinical findings will be significant for the development of a personalized surgical strategy.

Glomerular structural-functional relationships were investigated in 21 type 2 diabetic patients with proteinuria. Structural parameters were quantified using both light and electron microscopy and standard stereologic techniques. Data were also available on 14 nondiabetic subjects. Mesangial and matrix volume fractions and glomerular basement membrane (GBM) width were increased in type 2 patients when compared with nondiabetic subjects (mean +/- SD: 0.45 +/- 0.13 versus 0.18 +/- 0.03, P: < 0.001; 0.28 +/- 0.09 versus 0.10 +/- 0.02, P: < 0.001; and 665 +/- 138 versus 361 +/- 51 nm, P: < 0. 001, respectively). An increase in mesangial volume fraction was associated with high levels of proteinuria and low creatinine clearance (r = 0.64, P: = 0.002; r = -0.58, P: = 0.006, respectively). GBM width and mesangial foot process width (FPW(mes)) also correlated with proteinuria (r = 0.58, P: = 0.006; r = 0.60, P: = 0.004, respectively). Volume fraction of interstitium correlated with creatinine clearance (r = -0.58, P: = 0.006). Patients had previously been defined by light microscopy as having either diffuse or nodular glomerulosclerosis; those with nodules had larger mesangial and matrix volume fractions and more proteinuria than those classified as diffuse (mean +/- SD: 0.51 +/- 0.12 versus 0.36 +/- 0.08, P: = 0.007; 0.32 +/- 0.08 versus 0.21 +/- 0.05, P: = 0. 003; median, range: 4.3, 1.1 to 9.6 versus 1.1, 0.9 to 12.7 g/24 h, P: = 0.027). Creatinine clearance did not differ significantly between the groups. Type 2 diabetic patients with proteinuria have established glomerulopathy, which is more advanced in those with nodular glomerulosclerosis. Creatinine clearance correlated with both mesangial and interstitial expansion, whereas proteinuria correlated only with glomerular pathology. These results suggest that type 2 patients with advanced nephropathy have structural-functional relationships similar to type 1, consistent with a common pathogenesis, and strongly support an important role of the tubulointerstitium in the role of renal impairment.