Benchmarking functional connectivity by the structure and geometry of the human brain

The brain’s structural connectivity supports the propagation of electrical impulses, manifesting as patterns of co-activation, termed functional connectivity. Functional connectivity emerges from the underlying sparse structural connections, particularly through poly-synaptic communication. As a result, functional connections between brain regions without direct structural links are numerous, but their organization is not completely understood. Here we investigate the organization of functional connections without direct structural links. We develop a simple, data-driven method to benchmark functional connections with respect to their underlying structural and geometric embedding. We then use this method to re-weigh and re-express functional connectivity. We find evidence of unexpectedly strong functional connectivity within the canonical intrinsic networks of the brain. We also find unexpectedly strong functional connectivity at the apex of the unimodal-transmodal hierarchy. Our results suggest that both phenomena – functional modules and functional hierarchies – emerge from functional interactions that transcend the underlying structure and geometry. These findings also potentially explain recent reports that structural and functional connectivity gradually diverge in transmodal cortex. Collectively, we show how structural connectivity and geometry can be used as a natural frame of reference with which to study functional connectivity patterns in the brain.

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An architectonic type principle in the development of laminar patterns of cortico-cortical connections

Brain Structure and Function ◽

10.1007/s00429-021-02219-6 ◽

2021 ◽

Author(s):

Sarah F. Beul ◽

Alexandros Goulas ◽

Claus C. Hilgetag

Keyword(s):

Structural Connectivity ◽

Macaque Monkey ◽

Brain Regions ◽

Adult Brain ◽

Mammalian Brain ◽

Cortical Connections ◽

Cortical Projections ◽

Structural Connections ◽

High Degree ◽

The Brain

AbstractStructural connections between cortical areas form an intricate network with a high degree of specificity. Many aspects of this complex network organization in the adult mammalian cortex are captured by an architectonic type principle, which relates structural connections to the architectonic differentiation of brain regions. In particular, the laminar patterns of projection origins are a prominent feature of structural connections that varies in a graded manner with the relative architectonic differentiation of connected areas in the adult brain. Here we show that the architectonic type principle is already apparent for the laminar origins of cortico-cortical projections in the immature cortex of the macaque monkey. We find that prenatal and neonatal laminar patterns correlate with cortical architectonic differentiation, and that the relation of laminar patterns to architectonic differences between connected areas is not substantially altered by the complete loss of visual input. Moreover, we find that the degree of change in laminar patterns that projections undergo during development varies in proportion to the relative architectonic differentiation of the connected areas. Hence, it appears that initial biases in laminar projection patterns become progressively strengthened by later developmental processes. These findings suggest that early neurogenetic processes during the formation of the brain are sufficient to establish the characteristic laminar projection patterns. This conclusion is in line with previously suggested mechanistic explanations underlying the emergence of the architectonic type principle and provides further constraints for exploring the fundamental factors that shape structural connectivity in the mammalian brain.

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Multimodal Classification of Mild Traumatic Brain Injury Reveals Local Coupling Between Structural and Functional Connectomes

10.1101/2021.12.14.21267815 ◽

2021 ◽

Author(s):

Ajay Peddada ◽

Kevin Holly ◽

Tejaswi D Sudhakar ◽

Christina Ledbetter ◽

Christopher E. Talbot ◽

...

Keyword(s):

White Matter ◽

Functional Connectivity ◽

Temporal Lobe ◽

Resting State ◽

Classification Accuracy ◽

Structural Connectivity ◽

Brain Regions ◽

Group Differences ◽

Functional Connections ◽

White Matter Tractography

Background: Following mild traumatic brain injury (mTBI) compromised white matter structural integrity can result in alterations in functional connectivity of large-scale brain networks and may manifest in functional deficit including cognitive dysfunction . Advanced magnetic resonance neuroimaging techniques, specifically diffusion tensor imaging (DTI) and resting state functional magnetic resonance imaging (rs-fMRI), have demonstrated an increased sensitivity for detecting microstructural changes associated with mTBI. Identification of novel imaging biomarkers can facilitate early detection of these changes for effective treatment. In this study, we hypothesize that feature selection combining both structural and functional connectivity increases classification accuracy. Methods: 16 subjects with mTBI and 20 healthy controls underwent both DTI and resting state functional imaging. Structural connectivity matrices were generated from white matter tractography from DTI sequences. Functional connectivity was measured through pairwise correlations of rs-fMRI between brain regions. Features from both DTI and rs-fMRI were selected by identifying five brain regions with the largest group differences and were used to classify the generated functional and structural connectivity matrices, respectively. Classification was performed using linear support vector machines and validated with leave-one-out cross validation. Results: Group comparisons revealed increased functional connectivity in the temporal lobe and cerebellum as well as decreased structural connectivity in the temporal lobe. After training on structural connections only, a maximum classification accuracy of 78% was achieved when structural connections were selected based on their corresponding functional connectivity group differences. After training on functional connections only, a maximum classification accuracy of 69% was achieved when functional connections were selected based on their structural connectivity group differences. After training on both structural and functional connections, a maximum classification accuracy of 69% was achieved when connections were selected based on their structural connectivity. Conclusions: Our multimodal approach to ROI selection achieves at highest, a classification accuracy of 78%. Our results also implicate the temporal lobe in the pathophysiology of mTBI. Our findings suggest that white matter tractography can serve as a robust biomarker for mTBI when used in tandem with resting state functional connectivity.

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The Impact of Undiagnosed Synaesthesia on the Interpretation of Structural and Functional MRI Images Connectivity Maps and Resulting Diagnoses

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY ◽

10.24297/ijct.v15i11.4369 ◽

2016 ◽

Vol 15 (11) ◽

pp. 7227-7234

Author(s):

Nourhan Zayed

Keyword(s):

Magnetic Resonance Imaging ◽

Magnetic Resonance ◽

Diffusion Tensor ◽

Sensory Modality ◽

Structural Connectivity ◽

Brain Regions ◽

Resonance Imaging ◽

Functional Connections ◽

The Impact ◽

The Brain

Synathesia is a condition in which stimulation of a sensory modality triggers another sensation in the alike or an unalike sensory modality. Currently, synaesthesia is deemed a neurological condition that engages unwanted transfer of signals between brain regions from one sense to another â€œcrosstalk activationâ€. The probability that undiagnosed synaesthesia may impact the results of structural magnetic resonance imaging (MRI), Diffusion Tensor imaging (DTI), functional magnetic resonance imaging (fMRI) and resting state connectivity studies is high, given the multiple anatomical and functional connections within the brain. In this paper, the currently available literature to mark which sensations adjured by synaesthesia and how could this impact MRI different modalities. Our study found that synaesthesia can have an opaque impact on fMRI studies of sensory, memory and cognitive functions, and there is testimony to suggest structural connections in the brain are also mutated DTI measurements especially, it shows enhanced structural connectivity for synesthetes between brain regions, higher Fractional anisotropy (FA), as well as increased in the white matter integrity between some regions.. Given the low dispersal of synaesthesia, the likelihood of synaesthesia being a perplexing factor in DTI, fMRI studies of patient groups is small; however, determining the existence of synaesthesia is paramount for investigating individual patients especially Shizoherenia, and autistic patients.

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An architectonic type principle in the development of laminar patterns of cortico-cortical connections

10.1101/866681 ◽

2019 ◽

Author(s):

Sarah F. Beul ◽

Alexandros Goulas ◽

Claus C. Hilgetag

Keyword(s):

Structural Connectivity ◽

Macaque Monkey ◽

Brain Regions ◽

Adult Brain ◽

Mammalian Brain ◽

Cortical Connections ◽

Cortical Projections ◽

Structural Connections ◽

High Degree ◽

The Brain

AbstractStructural connections between cortical areas form an intricate network with a high degree of specificity. Many aspects of this complex network organization in the adult mammalian cortex are captured by an architectonic type principle, which relates structural connections to the architectonic differentiation of brain regions. In particular, the laminar patterns of projection origins are a prominent feature of structural connections that varies in a graded manner with the relative architectonic differentiation of connected areas in the adult brain. Here we show that the architectonic type principle is already apparent for the laminar origins of cortico-cortical projections in the immature cortex of the macaque monkey. We find that prenatal and neonatal laminar patterns correlate with cortical architectonic differentiation, and that the relation of laminar patterns to architectonic differences between connected areas is not substantially altered by the complete loss of visual input. Moreover, we find that the amount of change in laminar patterns that projections undergo during development varies in proportion to the relative architectonic differentiation of the connected areas. Hence, it appears that initial biases in laminar projection patterns become progressively strengthened by later developmental processes. These findings suggest that early neurogenetic processes during the formation of the brain are sufficient to establish the characteristic laminar projection patterns. This conclusion is in line with previously suggested mechanistic explanations underlying the emergence of the architectonic type principle and provides further constraints for exploring the fundamental factors that shape structural connectivity in the mammalian brain.

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Complexity of intrinsic brain dynamics shaped by multiscale structural constraints

10.1101/2020.05.14.097196 ◽

2020 ◽

Author(s):

Mengsen Zhang ◽

Manish Saggar

Keyword(s):

Functional Connectivity ◽

Brain Regions ◽

Synergistic Interaction ◽

Global Network ◽

Brain Dynamics ◽

Underlying Structure ◽

Energy Costs ◽

Structural Constraints ◽

Temporal Diversity ◽

The Brain

AbstractThe brain is a complex, nonlinear system, exhibiting ever-evolving patterns of activities even without external inputs or tasks. Such intrinsic dynamics play a key role in cognitive functions and psychiatric disorders. A challenge is to link the intrinsic dynamics to the underlying structure, given the nonlinearity. Here we use a biophysically constrained, nonlinear-dynamical model to show how the complexity of intrinsic brain dynamics, manifested as its multistability and temporal diversity, can be sculpted by structural properties across scales. At a local level, multistability and temporal diversity can be induced by sufficient recurrent excitatory connectivity and its heterogeneity. At a global level, such functional complexity can also be created by the synergistic interaction between monostable, locally identical regions. Coordination between model brain regions across attractors in the multistable landscape predicts human functional connectivity. Compared to dynamics near a single attractor, cross-attractor coordination better accounts for functional links uncorrelated with structural connectivity. Energy costs of cross-attractor coordination are modulated by both local and global connectivity, and higher in the Default Mode Network. These findings hint that functional connectivity underscores transitions between alternative patterns of activity in the brain—even more than the patterns themselves. This work provides a systematic framework for characterizing intrinsic brain dynamics as a web of cross-attractor transitions and their energy costs. The framework may be used to predict transitions and energy costs associated with experimental or clinical interventions.Significance StatementThe brain is a multifunctional system: different brain regions can coordinate flexibly to perform different tasks. Understanding the regional and global structural constraints on brain function is critical to understand cognition. Here, using a unified biophysical network model, we show how structural constraints across scales jointly shape the brain’s intrinsic functional repertoire – the set of all possible patterns that a brain could generate. The modeled functional repertoire is enriched by both the flexibility of individual brain regions and their synergistic interaction in a global network. By carefully examining the modeled repertoire, we found that human resting-state functional connectivity was better predicted by transitions between brain activity patterns rather than any specific pattern per se.

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Mapping individual differences across brain network structure to function and behavior with connectome embedding

10.1101/2021.01.13.426513 ◽

2021 ◽

Author(s):

Gidon Levakov ◽

Joshua Faskowitz ◽

Galia Avidan ◽

Olaf Sporns

Keyword(s):

Individual Differences ◽

Functional Connectivity ◽

Predictive Accuracy ◽

Structural Connectivity ◽

Brain Regions ◽

Brain Network ◽

Global Network ◽

Age Related ◽

And Behavior ◽

The Brain

AbstractThe connectome, a comprehensive map of the brain’s anatomical connections, is often summarized as a matrix comprising all dyadic connections among pairs of brain regions. This representation cannot capture higher-order relations within the brain graph. Connectome embedding (CE) addresses this limitation by creating compact vectorized representations of brain nodes capturing their context in the global network topology. Here, nodes “context” is defined as random walks on the brain graph and as such, represents a generative model of diffusive communication around nodes. Applied to group-averaged structural connectivity, CE was previously shown to capture relations between inter-hemispheric homologous brain regions and uncover putative missing edges from the network reconstruction. Here we extend this framework to explore individual differences with a novel embedding alignment approach. We test this approach in two lifespan datasets (NKI: n=542; Cam-CAN: n=601) that include diffusion-weighted imaging, resting-state fMRI, demographics and behavioral measures. We demonstrate that modeling functional connectivity with CE substantially improves structural to functional connectivity mapping both at the group and subject level. Furthermore, age-related differences in this structure-function mapping are preserved and enhanced. Importantly, CE captures individual differences by out-of-sample prediction of age and intelligence. The resulting predictive accuracy was higher compared to using structural connectivity and functional connectivity. We attribute these findings to the capacity of the CE to incorporate aspects of both anatomy (the structural graph) and function (diffusive communication). Our novel approach allows mapping individual differences in the connectome through structure to function and behavior.

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Pairwise maximum entropy model explains the role of white matter structure in shaping emergent co-activation states

Communications Biology ◽

10.1038/s42003-021-01700-6 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Arian Ashourvan ◽

Preya Shah ◽

Adam Pines ◽

Shi Gu ◽

Christopher W. Lynn ◽

...

Keyword(s):

White Matter ◽

Maximum Entropy ◽

Large Scale ◽

Structural Connectivity ◽

Quantitative Relationship ◽

Brain Regions ◽

Maximum Entropy Model ◽

Entropy Model ◽

Activation Patterns ◽

Co Activation

AbstractA major challenge in neuroscience is determining a quantitative relationship between the brain’s white matter structural connectivity and emergent activity. We seek to uncover the intrinsic relationship among brain regions fundamental to their functional activity by constructing a pairwise maximum entropy model (MEM) of the inter-ictal activation patterns of five patients with medically refractory epilepsy over an average of ~14 hours of band-passed intracranial EEG (iEEG) recordings per patient. We find that the pairwise MEM accurately predicts iEEG electrodes’ activation patterns’ probability and their pairwise correlations. We demonstrate that the estimated pairwise MEM’s interaction weights predict structural connectivity and its strength over several frequencies significantly beyond what is expected based solely on sampled regions’ distance in most patients. Together, the pairwise MEM offers a framework for explaining iEEG functional connectivity and provides insight into how the brain’s structural connectome gives rise to large-scale activation patterns by promoting co-activation between connected structures.

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Heritability and interindividual variability of regional structure-function coupling

Nature Communications ◽

10.1038/s41467-021-25184-4 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Zijin Gu ◽

Keith Wakefield Jamison ◽

Mert Rory Sabuncu ◽

Amy Kuceyeski

Keyword(s):

Functional Connectivity ◽

Structure Function ◽

Regional Differences ◽

Regional Scale ◽

Brain Regions ◽

Diffusion Weighted Mri ◽

Regional Structure ◽

Human Connectome Project ◽

Structural Connections ◽

The Relationship

AbstractWhite matter structural connections are likely to support flow of functional activation or functional connectivity. While the relationship between structural and functional connectivity profiles, here called SC-FC coupling, has been studied on a whole-brain, global level, few studies have investigated this relationship at a regional scale. Here we quantify regional SC-FC coupling in healthy young adults using diffusion-weighted MRI and resting-state functional MRI data from the Human Connectome Project and study how SC-FC coupling may be heritable and varies between individuals. We show that regional SC-FC coupling strength varies widely across brain regions, but was strongest in highly structurally connected visual and subcortical areas. We also show interindividual regional differences based on age, sex and composite cognitive scores, and that SC-FC coupling was highly heritable within certain networks. These results suggest regional structure-function coupling is an idiosyncratic feature of brain organisation that may be influenced by genetic factors.

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HIV Infection Is Associated with Attenuated Frontostriatal Intrinsic Connectivity: A Preliminary Study

Journal of the International Neuropsychological Society ◽

10.1017/s1355617715000156 ◽

2015 ◽

Vol 21 (3) ◽

pp. 203-213 ◽

Cited By ~ 39

Author(s):

Jonathan C. Ipser ◽

Gregory G. Brown ◽

Amanda Bischoff-Grethe ◽

Colm G. Connolly ◽

Ronald J. Ellis ◽

...

Keyword(s):

Hiv Infection ◽

Brain Regions ◽

Group Differences ◽

Hiv Rna ◽

Functional Connections ◽

Intrinsic Connectivity ◽

Dorsolateral Prefrontal ◽

Independent Replication ◽

Subcortical Regions ◽

The Brain

AbstractHIV-associated cognitive impairments are prevalent, and are consistent with injury to both frontal cortical and subcortical regions of the brain. The current study aimed to assess the association of HIV infection with functional connections within the frontostriatal network, circuitry hypothesized to be highly vulnerable to HIV infection. Fifteen HIV-positive and 15 demographically matched control participants underwent 6 min of resting-state functional magnetic resonance imaging (RS-fMRI). Multivariate group comparisons of age-adjusted estimates of connectivity within the frontostriatal network were derived from BOLD data for dorsolateral prefrontal cortex (DLPFC), dorsal caudate and mediodorsal thalamic regions of interest. Whole-brain comparisons of group differences in frontostriatal connectivity were conducted, as were pairwise tests of connectivity associations with measures of global cognitive functioning and clinical and immunological characteristics (nadir and current CD4 count, duration of HIV infection, plasma HIV RNA). HIV – associated reductions in connectivity were observed between the DLPFC and the dorsal caudate, particularly in younger participants (<50 years, N=9). Seropositive participants also demonstrated reductions in dorsal caudate connectivity to frontal and parietal brain regions previously demonstrated to be functionally connected to the DLPFC. Cognitive impairment, but none of the assessed clinical/immunological variables, was also associated with reduced frontostriatal connectivity. In conclusion, our data indicate that HIV is associated with attenuated intrinsic frontostriatal connectivity. Intrinsic connectivity of this network may therefore serve as a marker of the deleterious effects of HIV infection on the brain, possibly via HIV-associated dopaminergic abnormalities. These findings warrant independent replication in larger studies. (JINS, 2015, 21, 1–11)

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Distinct structure-function relationships across cortical regions and connectivity scales in the rat brain

10.1101/742833 ◽

2019 ◽

Author(s):

Milou Straathof ◽

Michel R.T. Sinke ◽

Theresia J.M. Roelofs ◽

Erwin L.A. Blezer ◽

R. Angela Sarabdjitsingh ◽

...

Keyword(s):

Functional Connectivity ◽

Structure Function ◽

Rat Brain ◽

Resting State ◽

Structural Connectivity ◽

Distinct Structure ◽

Macro Scale ◽

Function Relationship ◽

The Brain ◽

Meso Scale

AbstractAn improved understanding of the structure-function relationship in the brain is necessary to know to what degree structural connectivity underpins abnormal functional connectivity seen in many disorders. We integrated high-field resting-state fMRI-based functional connectivity with high-resolution macro-scale diffusion-based and meso-scale neuronal tracer-based structural connectivity, to obtain an accurate depiction of the structure-function relationship in the rat brain. Our main goal was to identify to what extent structural and functional connectivity strengths are correlated, macro- and meso-scopically, across the cortex. Correlation analyses revealed a positive correspondence between functional connectivity and macro-scale diffusion-based structural connectivity, but no correspondence between functional connectivity and meso-scale neuronal tracer-based structural connectivity. Locally, strong functional connectivity was found in two well-known resting-state networks: the sensorimotor and default mode network. Strong functional connectivity within these networks coincided with strong short-range intrahemispheric structural connectivity, but with weak heterotopic interhemispheric and long-range intrahemispheric structural connectivity. Our study indicates the importance of combining measures of connectivity at distinct hierarchical levels to accurately determine connectivity across networks in the healthy and diseased brain. Distinct structure-function relationships across the brain can explain the organization of networks and may underlie variations in the impact of structural damage on functional networks and behavior.

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