Individual variability in the human connectome maintains selective cross-modal consistency and shares microstructural signatures

Individuals are different in their behavioural responses and cognitive abilities. Neural underpinnings of individual differences are largely unknown. Here, by using multimodal imaging data including diffusion MRI, functional MRI and MEG, we show the consistency of interindividual variation of connectivity across modalities. We demonstrated that regional differences in individual variability of structural and functional connectomes is characterized by higher variability in association cortices and lower variability in sensory and visual cortices. This pattern is consistent across all modalities at varying degrees as shown by significant alignment between functional and structural connectome variabilities at several clusters of brain regions. Variability in connectivity is associated with cortical myelin content and microstructural properties of connections. Our findings contribute to understanding of individual differences in functional and structural organization of brain and facilitate fingerprinting applications.

Download Full-text

Unifying the Notions of Modularity and Core–Periphery Structure in Functional Brain Networks during Youth

Cerebral Cortex ◽

10.1093/cercor/bhz150 ◽

2019 ◽

Vol 30 (3) ◽

pp. 1087-1102

Author(s):

Shi Gu ◽

Cedric Huchuan Xia ◽

Rastko Ciric ◽

Tyler M Moore ◽

Ruben C Gur ◽

...

Keyword(s):

Individual Differences ◽

Brain Function ◽

Brain Networks ◽

Brain Regions ◽

Imaging Data ◽

Modular Architecture ◽

Functional Brain ◽

Rich Club ◽

Functional Brain Networks ◽

Integrative Processing

AbstractAt rest, human brain functional networks display striking modular architecture in which coherent clusters of brain regions are activated. The modular account of brain function is pervasive, reliable, and reproducible. Yet, a complementary perspective posits a core–periphery or rich-club account of brain function, where hubs are densely interconnected with one another, allowing for integrative processing. Unifying these two perspectives has remained difficult due to the fact that the methodological tools to identify modules are entirely distinct from the methodological tools to identify core–periphery structure. Here, we leverage a recently-developed model-based approach—the weighted stochastic block model—that simultaneously uncovers modular and core–periphery structure, and we apply it to functional magnetic resonance imaging data acquired at rest in 872 youth of the Philadelphia Neurodevelopmental Cohort. We demonstrate that functional brain networks display rich mesoscale organization beyond that sought by modularity maximization techniques. Moreover, we show that this mesoscale organization changes appreciably over the course of neurodevelopment, and that individual differences in this organization predict individual differences in cognition more accurately than module organization alone. Broadly, our study provides a unified assessment of modular and core–periphery structure in functional brain networks, offering novel insights into their development and implications for behavior.

Download Full-text

Robust prediction of individual personality from brain functional connectome

Social Cognitive and Affective Neuroscience ◽

10.1093/scan/nsaa044 ◽

2020 ◽

Vol 15 (3) ◽

pp. 359-369 ◽

Cited By ~ 1

Author(s):

Huanhuan Cai ◽

Jiajia Zhu ◽

Yongqiang Yu

Keyword(s):

Personality Traits ◽

Large Scale ◽

Five Factor Model ◽

Brain Regions ◽

Individual Variability ◽

Personality Factors ◽

Imaging Features ◽

Imaging Data ◽

Functional Connectome ◽

Human Connectome Project

Abstract Neuroimaging studies have linked inter-individual variability in the brain to individualized personality traits. However, only one or several aspects of personality have been effectively predicted based on brain imaging features. The objective of this study was to construct a reliable prediction model of personality in a large sample by using connectome-based predictive modeling (CPM), a recently developed machine learning approach. High-quality resting-state functional magnetic resonance imaging data of 810 healthy young participants from the Human Connectome Project dataset were used to construct large-scale brain networks. Personality traits of the five-factor model (FFM) were assessed by the NEO Five Factor Inventory. We found that CPM successfully and reliably predicted all the FFM personality factors (agreeableness, openness, conscientiousness and neuroticism) other than extraversion in novel individuals. At the neural level, we found that the personality-associated functional networks mainly included brain regions within default mode, frontoparietal executive control, visual and cerebellar systems. Although different feature selection thresholds and parcellation strategies did not significantly influence the prediction results, some findings lost significance after controlling for confounds including age, gender, intelligence and head motion. Our finding of robust personality prediction from an individual’s unique functional connectome may help advance the translation of ‘brain connectivity fingerprinting’ into real-world personality psychological settings.

Download Full-text

Disrupted neural synchrony mediates the relationship between white matter integrity and cognitive performance in older adults

10.1101/2019.12.29.890228 ◽

2019 ◽

Author(s):

T. Hinault ◽

M. Kraut ◽

A. Bakker ◽

A. Dagher ◽

S.M. Courtney

Keyword(s):

Older Adults ◽

Individual Differences ◽

White Matter ◽

Cognitive Functioning ◽

Cognitive Performance ◽

Cognitive Aging ◽

Brain Regions ◽

Individual Variability ◽

White Matter Integrity ◽

Neural Basis

AbstractOur main goal was to determine the influence of white matter integrity on the dynamic coupling between brain regions and the individual variability of cognitive performance in older adults. EEG was recorded while participants performed a task specifically designed to engage working memory and inhibitory processes, and the associations among functional activity, structural integrity, and cognitive performance were assessed. We found that the association between white matter microstructural integrity and cognitive functioning with aging is mediated by time-varying alpha and gamma phase-locking value (PLV). Specifically, older individuals with better preservation of the inferior fronto-occipital fasciculus showed greater task-related modulations of alpha and gamma long-range PLV between the inferior frontal gyrus and occipital lobe, lower local phase-amplitude coupling in occipital lobes, and better cognitive control performance. Our results help delineate the role of individual variability of white matter microstructure in dynamic synchrony and cognitive performance during normal aging, and show that even small reductions in white matter integrity can lead to altered communications between brain regions, which in turn can result in reduced efficiency of cognitive functioning.Significance statementCognitive aging is associated with large individual differences, as some individuals maintain cognitive performance similar to that of young adults while others are significantly impaired. We hypothesized that individual differences in white matter integrity would influence the functional synchrony between frontal and posterior brain regions, and cognitive performance in older adults. We found that the association between reduced tract integrity and worse cognitive performance in older adults was mediated by task-related modulations of coupling synchrony in the alpha and gamma bands. Results offer a mechanistic explanation for the neural basis of the variability of cognitive performance in older adults who do not have any clinically diagnosable neuropathology, and for the association between structural network integrity and cognition in older adults.

Download Full-text

Seeing Sounds and Hearing Sights: The Influence of Prior Learning on Current Perception

Journal of Cognitive Neuroscience ◽

10.1162/jocn.2008.20075 ◽

2008 ◽

Vol 20 (6) ◽

pp. 1030-1042 ◽

Cited By ~ 9

Author(s):

Jennifer D. Ryan ◽

Sandra N. Moses ◽

Melanie L. Ostreicher ◽

Timothy Bardouille ◽

Anthony T. Herdman ◽

...

Keyword(s):

Superior Temporal Gyrus ◽

Occipital Cortex ◽

Neural Correlates ◽

Brain Regions ◽

Stimulus Onset ◽

Evoked Responses ◽

Prior Learning ◽

Visual Cortices ◽

Neural Underpinnings ◽

Prior Experiences

It is well known that previous perceptual experiences alter subsequent perception, but the details of the neural underpinnings of this general phenomenon are still sketchy. Here, we ask whether previous experiences with an item (such as seeing a person's face) leads to the alteration of the neural correlates related to processing of the item as such, or whether it creates additional associative connections between such substrates and those activated during prior experience. To address this question, we used magnetoencephalography (MEG) to identify neural changes accompanying subjects' viewing of unfamiliar versus famous faces and hearing the names of unfamiliar versus famous names. We were interested in the nature of the involvement of auditory brain regions in the viewing of faces, and in the involvement of visual regions in the hearing of names. Evoked responses from MEG recordings for the names and faces conditions were localized to auditory and visual cortices, respectively. Unsurprisingly, peak activation strength of evoked responses was larger for famous versus nonfamous names within the superior temporal gyrus (STG), and was similar for famous and nonfamous faces in the occipital cortex. More relevant to the issue of experience on perception, peak activation strength in the STG was larger for viewed famous versus nonfamous faces, and peak activation within the occipital cortex was larger for heard famous versus nonfamous names. Critically, these experience-related responses were present within 150–250 msec of stimulus onset. These findings support the hypothesis that prior experiences may influence processing of faces and names such that perception encompasses more than what is imparted on the senses.

Download Full-text

When are inter-individual brain-behavior correlations informative?

10.1101/036772 ◽

2016 ◽

Cited By ~ 7

Author(s):

Maël Lebreton ◽

Stefano Palminteri

Keyword(s):

Individual Differences ◽

Brain Regions ◽

Group Differences ◽

Societal Factors ◽

Individual Traits ◽

Or Groups ◽

Analytical Strategies ◽

Neural Underpinnings ◽

Statistical Relationships ◽

The Way

AbstractCharacterizing inter-individual differences induced by clinical and social factors constitutes one of the most promising applications of neuroimaging. Paving the way for such applications, neuroimaging studies often report between-group differences in “activations” or correlations between such “activations” and individual traits. Here we raise cautionary warnings about some of those inter-individual analytic strategies. These warnings become critical when measures of “activations” are unstandardized coefficients of regressions between BOLD signal and individual behavior.First, using simple algebraic derivations, we show how inter-individual differences results can spuriously arise from neglecting the statistical relationships which link the ranges of individual BOLD activation and of recorded behavior. We also demonstrate how apparently contradictory results and interpretations may simply arise from the interaction of this scaling issue and the pre-processing of the behavioral variables. Second, using computational simulations, we illustrate how this issue percolates the booming field of model-based fMRI. Finally, we outline a set of recommendations, which might prove useful for researcher and reviewers confronted with questions involving inter-individual differences in neuroimaging.Author SummaryCharacterizing inter-individual differences induced by clinical and societal factors constitutes one of the most promising applications of neuroimaging. Paving the way for such applications, an increasing fraction of neuroimaging studies reports between-group differences in “activations” or correlations between “activations” and individual traits. In this manuscript, we focus on the typical analytical strategies employed in studies investigating how differences in behavior between individuals or groups of individuals are translated to differences of activations in specific brain regions. We notably question whether they are suitable to support inferences and claims about the neural underpinnings of differential cognition. Our core results demonstrate that typical inter-individual results can spuriously arise by overlooking plausible statistical relationships that link the ranges of individual BOLD activation with the ranges of produced behavior. We argue that these results challenge current classical interpretations of inter-individual results. Highlighting the methodological and theoretical gaps regarding the analysis and interpretation of inter-individual differences is fundamental to fulfilling the promises of neuroimaging.

Download Full-text

Integrating across neuroimaging modalities boosts prediction accuracy of cognitive ability

PLoS Computational Biology ◽

10.1371/journal.pcbi.1008347 ◽

2021 ◽

Vol 17 (3) ◽

pp. e1008347 ◽

Cited By ~ 1

Author(s):

Javier Rasero ◽

Amy Isabella Sentis ◽

Fang-Cheng Yeh ◽

Timothy Verstynen

Keyword(s):

Individual Differences ◽

Cognitive Function ◽

Cognitive Ability ◽

Cognitive Abilities ◽

Predictive Power ◽

Fluid Intelligence ◽

Prediction Models ◽

Principal Component ◽

Individual Variability ◽

Human Connectome Project

Variation in cognitive ability arises from subtle differences in underlying neural architecture. Understanding and predicting individual variability in cognition from the differences in brain networks requires harnessing the unique variance captured by different neuroimaging modalities. Here we adopted a multi-level machine learning approach that combines diffusion, functional, and structural MRI data from the Human Connectome Project (N = 1050) to provide unitary prediction models of various cognitive abilities: global cognitive function, fluid intelligence, crystallized intelligence, impulsivity, spatial orientation, verbal episodic memory and sustained attention. Out-of-sample predictions of each cognitive score were first generated using a sparsity-constrained principal component regression on individual neuroimaging modalities. These individual predictions were then aggregated and submitted to a LASSO estimator that removed redundant variability across channels. This stacked prediction led to a significant improvement in accuracy, relative to the best single modality predictions (approximately 1% to more than 3% boost in variance explained), across a majority of the cognitive abilities tested. Further analysis found that diffusion and brain surface properties contribute the most to the predictive power. Our findings establish a lower bound to predict individual differences in cognition using multiple neuroimaging measures of brain architecture, both structural and functional, quantify the relative predictive power of the different imaging modalities, and reveal how each modality provides unique and complementary information about individual differences in cognitive function.

Download Full-text

Effects on brain activity after creative mathematical reasoning when considering individual differences in cognitive ability

10.1101/2021.03.30.437492 ◽

2021 ◽

Author(s):

Linnea Karlsson Wirebring ◽

Carola Wiklund-Hornqvist ◽

Sara Stillesjo ◽

Carina Granberg ◽

Johan Lithner ◽

...

Keyword(s):

Individual Differences ◽

Cognitive Abilities ◽

Mathematical Reasoning ◽

Brain Activity ◽

Brain Regions ◽

Learning Opportunities ◽

Memory Processing ◽

Beneficial Effects ◽

Upper Secondary ◽

Creative Mathematical Reasoning

Many learning opportunities of mathematical reasoning in school encourage passive imitative learning procedures (algorithmic reasoning, AR) instead of engaging in more active constructive reasoning processes (e.g., creative mathematical reasoning, CMR). In the present study, we employed a within-subject mathematical intervention in the classroom with pupils in upper secondary schools followed by a test situation during brain imaging with fMRI one week later. We hypothesized that learning mathematical reasoning with the active (CMR) compared to the passive mode (AR) should lead to a CMR-effect, characterized by better performance and higher activity in brain regions related to semantic memory processing one week after learning. Despite controlling for individual differences in cognitive abilities, higher brain activity in key semantic brain regions such as left AG and left IFG was observed on tasks previously learnt with CMR compared to AR. Thus, encouraging pupils to engage in more active constructive processes when learning mathematical reasoning might have beneficial effects on learning and memory.

Download Full-text

Inter-individual Variability of Functional Connectivity in Awake and Anesthetized Rhesus Monkeys

10.1101/531293 ◽

2019 ◽

Cited By ~ 2

Author(s):

Ting Xu ◽

Darrick Sturgeon ◽

Julian S.B. Ramirez ◽

Seán Froudist-Walsh ◽

Daniel S. Margulies ◽

...

Keyword(s):

Individual Differences ◽

Functional Connectivity ◽

Nonhuman Primate ◽

Individual Variation ◽

Large Scale ◽

Functional Organization ◽

Individual Variability ◽

Initial Examination ◽

Long Distance ◽

Lower Variability

ABSTRACTBackgroundNonhuman primate models (NHP) are commonly used to advance our understanding of brain function and organization. However, to date, they have offered few insights into individual differences among NHPs. In large part, this is due to the logistical challenges of NHP research, which limit most studies to five subjects or fewer.MethodsWe leveraged the availability of a large-scale open NHP imaging resource to provide an initial examination of individual differences in the functional organization of the nonhuman primate brain. Specifically, we selected one awake fMRI dataset (Newcastle: n = 10) and two anesthetized fMRI data sets (Oxford: n = 19; UC-Davis: n = 19) to examine individual differences in functional connectivity characteristics across the cortex, as well as potential state dependencies.ResultsWe noted significant individual variations of functional connectivity across the macaque cortex. Similar to the findings in human, during the awake state, the primary sensory and motor cortices showed lower variability than the high-order association regions. This variability pattern was significantly correlated with T1w/T2w map, the degree of long-distance connectivity, but not short-distance connectivity. However, the inter-individual variability under anesthesia exhibited a very distinct pattern, with lower variability in medial frontal cortex, precuneus and somatomotor regions and higher variability in the lateral ventral frontal and insular cortices.ConclusionsThis work has implications for our understanding of the evolutionary origins of individual variation in the human brain, as well as methodological implications that must be considered in any pursuit to study individual variation in NHP models.

Download Full-text

Light Through the Fog: Using Precision fMRI Data to Disentangle the Neural Substrates of Cognitive Control

10.31234/osf.io/9e28n ◽

2020 ◽

Author(s):

Derek Martin Smith ◽

Diana C. Perez ◽

Alexis Porter ◽

Ally Dworetsky ◽

Caterina Gratton

Keyword(s):

Cognitive Control ◽

Data Acquisition ◽

Frontal Cortex ◽

Neural Substrates ◽

Brain Regions ◽

Individual Variability ◽

Individual Heterogeneity ◽

Fmri Data ◽

Brain Organization ◽

Neural Underpinnings

Cognitive control, the ability to engage in goal-related behavior, is linked to frontal, parietal, andcingulate brain regions. However, the underlying function(s) of these regions is still in question,with ongoing discussions about their specificity and/or multifunctionality. These brain regionsare also among the most variable across individuals, which may confound multi-functionalitywith inter-individual heterogeneity. Precision fMRI extended data acquisition from singleindividuals allows for reliable individualized mapping of brain organization. We reviewexamples of recent studies that use precision fMRI to surmount inter-individual variability infunctional neuroanatomy. These studies provide evidence of interleaved specialized andmultifunctional regions in the frontal cortex. We discuss the potential for these techniques toaddress outstanding controversies on the neural underpinnings of cognitive control.

Download Full-text

Grey matter volume in early human visual cortex predicts proneness to the sound-induced flash illusion

Proceedings of The Royal Society B Biological Sciences ◽

10.1098/rspb.2012.2132 ◽

2012 ◽

Vol 279 (1749) ◽

pp. 4955-4961 ◽

Cited By ~ 24

Author(s):

Benjamin de Haas ◽

Ryota Kanai ◽

Lauri Jalkanen ◽

Geraint Rees

Keyword(s):

Visual Cortex ◽

Individual Differences ◽

Visual Perception ◽

Grey Matter ◽

Brain Regions ◽

Grey Matter Volume ◽

Matter Volume ◽

Visual Cortices ◽

Optimal Weighting ◽

Early Human

Visual perception can be modulated by sounds. A drastic example of this is the sound-induced flash illusion: when a single flash is accompanied by two bleeps, it is sometimes perceived in an illusory fashion as two consecutive flashes. However, there are strong individual differences in proneness to this illusion. Some participants experience the illusion on almost every trial, whereas others almost never do. We investigated whether such individual differences in proneness to the sound-induced flash illusion were reflected in structural differences in brain regions whose activity is modulated by the illusion. We found that individual differences in proneness to the illusion were strongly and significantly correlated with local grey matter volume in early retinotopic visual cortex. Participants with smaller early visual cortices were more prone to the illusion. We propose that strength of auditory influences on visual perception is determined by individual differences in recurrent connections, cross-modal attention and/or optimal weighting of sensory channels.

Download Full-text