Building functional network neuroscience for reliable individual differences

Abstract A rapidly emerging application of network neuroscience in neuroimaging studies has provided useful tools to understand individual differences in complex brain function, i.e., the functional network neuroscience (FNN). However, the variability of methodologies applied across FNN studies - with respect to node definition, edge construction, and graph measurements- makes it difficult to directly compare findings and also challenging for end users to select the optimal strategies for mapping individual differences in brain networks. Here, we aim to provide a benchmark for best FNN practices by systematically comparing the measurement reliability of individual differences in FNN under different analytical strategies using the test-retest design of the Human Connectome Project. The results uncovered four essential principles to guide reliable FNN: 1) use a whole brain parcellation to define network nodes, including subcortical and cerebellar regions, 2) construct functional connectome using spontaneous brain activity in multiple slow bands, 3) optimize topological economy of networks at individual level, 4) characterise information flow with specific metrics of integration and segregation. We built an interactive online resource for reliable FNN (http://ibraindata.com/research/reliableFNN).

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Brain connectivity at rest predicts individual differences in normative activity during movie watching

10.1101/2021.01.06.425410 ◽

2021 ◽

Author(s):

David C Gruskin ◽

Gaurav H Patel

Keyword(s):

Spatial Distribution ◽

Individual Differences ◽

Resting State ◽

Functional Organization ◽

Brain Connectivity ◽

Brain Activity ◽

Sensory Information ◽

Resting State Functional Connectivity ◽

Similar Region ◽

Human Connectome Project

When multiple individuals are exposed to the same sensory event, some are bound to have less typical experiences than others. These atypical experiences are underpinned by atypical stimulus-evoked brain activity, the extent of which is often indexed by intersubject correlation (ISC). Previous research has attributed individual differences in ISC to variation in trait-like behavioral phenotypes. Here, we extend this line of work by showing that an individual's degree and spatial distribution of ISC are closely related to their brain's intrinsic functional architecture. Using resting state and movie watching fMRI data from 176 Human Connectome Project participants, we reveal that resting state functional connectivity (RSFC) profiles can be used to predict cortex-wide ISC with considerable accuracy. Similar region-level analyses demonstrate that the amount of ISC a brain region exhibits during movie watching is associated with its connectivity to others at rest, and that the nature of these connectivity-activity relationships varies as a function of the region's role in sensory information processing. Finally, we show that an individual's unique spatial distribution of ISC, independent of its magnitude, is also related to their RSFC profile. These findings suggest that the brain's ability to process complex sensory information is tightly linked to its baseline functional organization and motivate a more comprehensive understanding of individual responses to naturalistic stimuli.

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Context Matters: Situational Stress Impedes Functional Reorganization of Intrinsic Brain Connectivity during Problem-Solving

Cerebral Cortex ◽

10.1093/cercor/bhaa349 ◽

2020 ◽

Author(s):

Mengting Liu ◽

Robert A Backer ◽

Rachel C Amey ◽

Eric E Splan ◽

Adam Magerman ◽

...

Keyword(s):

Individual Differences ◽

Resting State ◽

Brain Connectivity ◽

Brain Activity ◽

Functional Network ◽

Functional Reorganization ◽

Brain States ◽

And Performance ◽

Brain Functional Connectivity ◽

The Relationship

Abstract Extensive research has established a relationship between individual differences in brain activity in a resting state and individual differences in behavior. Conversely, when individuals are engaged in various tasks, certain task-evoked reorganization occurs in brain functional connectivity, which can consequently influence individuals’ performance as well. Here, we show that resting state and task-dependent state brain patterns interact as a function of contexts engendering stress. Findings revealed that when the resting state connectome was examined during performance, the relationship between connectome strength and performance only remained for participants under stress (who also performed worse than all other groups on the math task), suggesting that stress preserved brain patterns indicative of underperformance whereas non-stressed individuals spontaneously transitioned out of these patterns. Results imply that stress may impede the reorganization of a functional network in task-evoked brain states. This hypothesis was subsequently verified using graph theory measurements on a functional network, independent of behavior. For participants under stress, the functional network showed less topological alterations compared to non-stressed individuals during the transition from resting state to task-evoked state. Implications are discussed for network dynamics as a function of context.

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The Noise-Resilient Brain: Resting-State Oscillatory Activity Predicts Words-In-Noise Recognition

10.1101/705053 ◽

2019 ◽

Author(s):

Thomas Houweling ◽

Robert Becker ◽

Alexis Hervais-Adelman

Keyword(s):

Individual Differences ◽

Phonological Processing ◽

Resting State ◽

Recognition Performance ◽

Brain Activity ◽

Superior Temporal Gyrus ◽

Oscillatory Activity ◽

List Type ◽

Gamma Frequency ◽

Human Connectome Project

AbstractThe role of neuronal oscillations in the processing of speech has recently come to prominence. Since resting-state (RS) brain activity has been shown to predict both task-related brain activation and behavioural performance, we set out to establish whether inter-individual differences in spectrally-resolved RS-MEG power are associated with variations in words-in-noise recognition in a sample of 88 participants made available by the Human Connectome Project. Positive associations with resilience to noise were observed with power in the range 21 and 29Hz in a number of areas along the left temporal gyrus and temporo-parietal association areas peaking in left posterior superior temporal gyrus (pSTG). Significant associations were also found in the right posterior superior temporal gyrus in the frequency range 30 to 40Hz. We propose that individual differences in words-in-noise performance are related to baseline excitability levels of the neural substrates of phonological processing.HighlightsPower of resting MEG activity predicts Words-In-Noise recognition performanceSignificant associations in higher beta and lower gamma frequency bandStrongest in left-lateralised perisylvian cluster peaking in posterior STGEffects are spectrally and spatially consistent with phoneme-level processing

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Idiosynchrony: From shared responses to individual differences during naturalistic neuroimaging

10.31234/osf.io/yeu89 ◽

2019 ◽

Cited By ~ 1

Author(s):

Emily S. Finn ◽

Enrico Glerean ◽

Arman Y. Khojandi ◽

Dylan Nielson ◽

Peter J. Molfese ◽

...

Keyword(s):

Individual Differences ◽

Brain Activity ◽

Future Research ◽

Imaging Data ◽

Stimulus Choice ◽

Human Connectome Project ◽

Single Subjects ◽

Using Data ◽

The Individual ◽

Empirical Demonstration

Two ongoing movements in human cognitive neuroscience have researchers shifting focus from group-level inferences to characterizing single subjects, and complementing tightly controlled tasks with rich, dynamic paradigms such as movies and stories. Yet relatively little work combines these two, perhaps because traditional analysis approaches for naturalistic imaging data are geared toward detecting shared responses rather than between-subject variability. Here, we review recent work using naturalistic stimuli to study individual differences, and advance a framework for detecting structure in idiosyncratic patterns of brain activity, or “idiosynchrony”. Specifically, we outline the emerging technique of inter-subject representational similarity analysis (IS-RSA), including its theoretical motivation and an empirical demonstration of how it recovers brain-behavior relationships during movie watching using data from the Human Connectome Project. We also consider how stimulus choice may affect the individual signal and discuss areas for future research. We argue that naturalistic neuroimaging paradigms have the potential to reveal meaningful individual differences above and beyond those observed during traditional tasks or at rest.

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The intrinsic spatiotemporal organization of the human brain - A multi-dimensional functional network atlas

10.1101/2021.12.09.472035 ◽

2021 ◽

Author(s):

Jian Li ◽

Yijun Liu ◽

Jessica L. Wisnowski ◽

Richard M. Leahy

Keyword(s):

Individual Differences ◽

Human Brain ◽

Large Scale ◽

Temporal Dynamics ◽

Brain Networks ◽

Cognitive Networks ◽

Functional Network ◽

Healthy Population ◽

Default Mode ◽

Human Connectome Project

The human brain is a complex, integrative and segregative network that exhibits dynamic fluctuations in activity across space and time. A canonical set of large-scale networks has been historically identified from resting-state fMRI (rs-fMRI), including the default mode, visual, somatomotor, salience, attention, and executive control. However, the methods used in identification of these networks have relied on assumptions that may inadvertently constrain their properties and consequently our understanding of the human connectome. Here we define a brain "network" as a functional component that jointly describes its spatial distribution and temporal dynamics, where neither domain suffers from unrealistic constraints. Using our recently developed BrainSync algorithm and the Nadam-Accelerated SCAlable and Robust (NASCAR) tensor decomposition, we identified twenty-three brain networks using rs-fMRI data from a large group of healthy subjects acquired by the Human Connectome Project. These networks are spatially overlapped, temporally correlated, and highly reproducible across two independent groups and sessions. We show that these networks can be clustered into six distinct functional categories and naturally form a representative functional network atlas for a healthy population. Using this atlas, we demonstrate that individuals with attention-deficit/hyperactivity disorder display disproportionate brain activity increases, relative to neurotypical subjects, in visual, auditory, and somatomotor networks concurrent with decreases in the default mode and higher-order cognitive networks. Thus, this work not only yields a highly reproducible set of spatiotemporally overlapped functional brain networks, but also provides convergent evidence that individual differences in these networks can be used to explain individual differences in neurocognitive functioning.

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Subtypes of brain activation are heritable and genetically linked with behavior in the Human Connectome Project sample

10.31234/osf.io/excdq ◽

2020 ◽

Author(s):

Yassine Benhajali ◽

AmanPreet Badhwar ◽

Sebastian Urchs ◽

Clara Moreau ◽

Francois Chouinard-Decorte ◽

...

Keyword(s):

Brain Activity ◽

Heritability Estimate ◽

Brain Activation ◽

Task Demands ◽

Imaging Genetics ◽

Intermediate Phenotype ◽

Phenotypic Correlation ◽

Behavioral Measures ◽

Individual Level ◽

Human Connectome Project

Many imaging and genetics studies have aimed to clarify whether the brain acts as an intermediate phenotype mediating the influence of genes in human behaviour. Brain activations in response to task demands are heterogeneous at the individual level, but also follow common patterns at the group level. Some studies have addressed this tension between heterogeneity and homogeneity by identifying groups of individuals that share the same brain activations patterns, called brain activation subtypes. In this work, we aimed to assess the viability of brain subtypes as endophenotypes intermediate between genes and behavior. We extracted brain activation subtypes separately for seven fMRI tasks, in 842 participants from the Human Connectome Project (HCP). We estimated the heritability of these subtypes and their genetic correlation with behavioral measures obtained inside and outside the scanner. Across all tasks, subtypes ranged from a predominantly ‘deactivating’ pattern towards a more ‘activating’ pattern of brain activity, with a heritability estimate ranging from 0 to 0.62. We observed high genetic and phenotypic correlation between behavioral measures and brain activation subtypes only for language and working memory tasks. Our results showed a significant genetic grounding of brain activation subtypes and they appear as a simple yet effective technique to tackle heterogeneity into imaging genetics studies.

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Accurate predictions of individual differences in task-evoked brain activity from resting-state fMRI using a sparse ensemble learner

10.1101/2021.08.19.456783 ◽

2021 ◽

Author(s):

Ying-Qiu Zheng ◽

Seyedeh-Rezvan Farahibozorg ◽

Weikang Gong ◽

Hossein Rafipoor ◽

Saad Jbabdi ◽

...

Keyword(s):

Individual Differences ◽

Resting State ◽

Predictive Accuracy ◽

Brain Activity ◽

Resting State Fmri ◽

Human Connectome Project ◽

Wide Range ◽

Dual Regression ◽

Project Data ◽

Test Retest Reliability

Modelling and predicting individual differences in task-evoked FMRI activity can have a wide range of applications from basic to clinical neuroscience. It has been shown that models based on resting-state activity can have high predictive accuracy. Here we propose several improvements to such models. Using a sparse ensemble leaner, we show that (i) features extracted using Stochastic Probabilistic Functional Modes (sPROFUMO) outperform the previously proposed dual-regression approach, (ii) that the shape and overall intensity of individualised task activations can be modelled separately and explicitly, (iii) training the model on predicting residual differences in brain activity further boosts individualised predictions. These results hold for both surface-based analyses of the Human Connectome Project data as well as volumetric analyses of UK-biobank data. Overall, our model achieves state of the art prediction accuracy on par with the test-retest reliability of tfMRI scans, suggesting that it has potential to supplement traditional task localisers.

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Neuroimaging of Individual Differences: A Latent Variable Modeling Perspective

10.31234/osf.io/jryfm ◽

2018 ◽

Author(s):

Shelly Renee Cooper ◽

Joshua James Jackson ◽

Deanna Barch ◽

Todd Samuel Braver

Keyword(s):

Individual Differences ◽

Individual Difference ◽

Latent Variable ◽

Structural Equation ◽

Behavioral Measure ◽

Equation Modeling ◽

Latent Variable Modeling ◽

Psychometric Theory ◽

Human Connectome Project ◽

Neuroimaging Data

Neuroimaging data is being increasingly utilized to address questions of individual difference. When examined with task-related fMRI (t-fMRI), individual differences are typically investigated via correlations between the BOLD activation signal at every voxel and a particular behavioral measure. This can be problematic because: 1) correlational designs require evaluation of t-fMRI psychometric properties, yet these are not well understood; and 2) bivariate correlations are severely limited in modeling the complexities of brain-behavior relationships. Analytic tools from psychometric theory such as latent variable modeling (e.g., structural equation modeling) can help simultaneously address both concerns. This review explores the advantages gained from integrating psychometric theory and methods with cognitive neuroscience for the assessment and interpretation of individual differences. The first section provides background on classic and modern psychometric theories and analytics. The second section details current approaches to t-fMRI individual difference analyses and their psychometric limitations. The last section uses data from the Human Connectome Project to provide illustrative examples of how t-fMRI individual differences research can benefit by utilizing latent variable models.

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Individual Uniqueness in the Neonatal Functional Connectome

Cerebral Cortex ◽

10.1093/cercor/bhab041 ◽

2021 ◽

Author(s):

Qiushi Wang ◽

Yuehua Xu ◽

Tengda Zhao ◽

Zhilei Xu ◽

Yong He ◽

...

Keyword(s):

Individual Differences ◽

Individual Identification ◽

Imaging Data ◽

Functional Connectome ◽

Middle Range ◽

Human Connectome Project ◽

The Individual ◽

And Behavior ◽

Identification Analysis ◽

The Brain

Abstract The functional connectome is highly distinctive in adults and adolescents, underlying individual differences in cognition and behavior. However, it remains unknown whether the individual uniqueness of the functional connectome is present in neonates, who are far from mature. Here, we utilized the multiband resting-state functional magnetic resonance imaging data of 40 healthy neonates from the Developing Human Connectome Project and a split-half analysis approach to characterize the uniqueness of the functional connectome in the neonatal brain. Through functional connectome-based individual identification analysis, we found that all the neonates were correctly identified, with the most discriminative regions predominantly confined to the higher-order cortices (e.g., prefrontal and parietal regions). The connectivities with the highest contributions to individual uniqueness were primarily located between different functional systems, and the short- (0–30 mm) and middle-range (30–60 mm) connectivities were more distinctive than the long-range (>60 mm) connectivities. Interestingly, we found that functional data with a scanning length longer than 3.5 min were able to capture the individual uniqueness in the functional connectome. Our results highlight that individual uniqueness is present in the functional connectome of neonates and provide insights into the brain mechanisms underlying individual differences in cognition and behavior later in life.

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Functional and diffusion MRI reveal the neurophysiological basis of neonates’ noxious-stimulus evoked brain activity

Nature Communications ◽

10.1038/s41467-021-22960-0 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Luke Baxter ◽

Fiona Moultrie ◽

Sean Fitzgibbon ◽

Marianne Aspbury ◽

Roshni Mansfield ◽

...

Keyword(s):

White Matter ◽

Prediction Model ◽

Resting State ◽

Early Life ◽

Diffusion Mri ◽

Brain Activity ◽

Noxious Stimulus ◽

Noxious Stimulation ◽

Human Connectome Project ◽

And Diffusion

AbstractUnderstanding the neurophysiology underlying neonatal responses to noxious stimulation is central to improving early life pain management. In this neonatal multimodal MRI study, we use resting-state and diffusion MRI to investigate inter-individual variability in noxious-stimulus evoked brain activity. We observe that cerebral haemodynamic responses to experimental noxious stimulation can be predicted from separately acquired resting-state brain activity (n = 18). Applying this prediction model to independent Developing Human Connectome Project data (n = 215), we identify negative associations between predicted noxious-stimulus evoked responses and white matter mean diffusivity. These associations are subsequently confirmed in the original noxious stimulation paradigm dataset, validating the prediction model. Here, we observe that noxious-stimulus evoked brain activity in healthy neonates is coupled to resting-state activity and white matter microstructure, that neural features can be used to predict responses to noxious stimulation, and that the dHCP dataset could be utilised for future exploratory research of early life pain system neurophysiology.

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