scholarly journals Genetic and environmental influence on the human functional connectome

2018 ◽  
Author(s):  
Andrew E. Reineberg ◽  
Alexander S. Hatoum ◽  
John K. Hewitt ◽  
Marie T. Banich ◽  
Naomi P. Friedman
Keyword(s):  
Cognitive Abilities ◽  
Environmental Influence ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Genetic Influence ◽  
Functional Connections ◽  
Functional Connectome ◽  
Intermediate Phenotypes ◽  
Human Connectome Project ◽  
Shared Environmental Influence

AbstractDetailed mapping of genetic and environmental influences on the functional connectome is a crucial step toward developing intermediate phenotypes between genes and clinical diagnoses or cognitive abilities. We analyze resting-state data from two, adult twin samples - 390 twins from the Colorado Longitudinal Twin Sample and 422 twins from the Human Connectome Project - to examine genetic and environmental influence on all pairwise functional connections between 264 brain regions (~35,000 functional connections). Non-shared environmental influence was high, genetic influence was moderate, and shared environmental influence was weak-to-moderate across the connectome. The brain’s genetic organization is diverse and not as one would expect based solely on structure evident in non-genetically informative data or lower-resolution data. As follow-up, we make novel classifications of functional connections and examine highly-localized connections with particularly strong genetic influence. This high-resolution genetic taxonomy of brain connectivity will be useful in understanding genetic influences on brain disorders.

scholarly journals Genetic and Environmental Influence on the Human Functional Connectome

2019 ◽  
Vol 30 (4) ◽  
pp. 2099-2113
Author(s):  
Andrew E Reineberg ◽  
Alexander S Hatoum ◽  
John K Hewitt ◽  
Marie T Banich ◽  
Naomi P Friedman
Keyword(s):  
Resting State ◽  
Environmental Influence ◽  
Brain Regions ◽  
Genetic Influence ◽  
Genetic Influences ◽  
Imaging Data ◽  
Functional Connections ◽  
Functional Connectome ◽  
Intermediate Phenotypes ◽  
Shared Environmental Influence

Abstract Detailed mapping of genetic and environmental influences on the functional connectome is a crucial step toward developing intermediate phenotypes between genes and clinical diagnoses or cognitive abilities. We analyzed resting-state functional magnetic resonance imaging data from two adult twin samples (Nos = 446 and 371) to quantify genetic and environmental influence on all pairwise functional connections between 264 brain regions (~35 000 functional connections). Nonshared environmental influence was high across the whole connectome. Approximately 14–22% of connections had nominally significant genetic influence in each sample, 4.6% were significant in both samples, and 1–2% had heritability estimates greater than 30%. Evidence of shared environmental influence was weak. Genetic influences on connections were distinct from genetic influences on a global summary measure of the connectome, network-based estimates of connectivity, and movement during the resting-state scan, as revealed by a novel connectome-wide bivariate genetic modeling procedure. The brain’s genetic organization is diverse and not as one would expect based solely on structure evident in nongenetically informative data or lower resolution data. As follow-up, we make novel classifications of functional connections and examine highly localized connections with particularly strong genetic influence. This high-resolution genetic taxonomy of brain connectivity will be useful in understanding genetic influences on brain disorders.

scholarly journals Shared functional connections within and between cortical networks predict individual cognitive abilities in males and females

2021 ◽  
Author(s):  
Elvisha Dhamala ◽  
Keith W. Jamison ◽  
Abhishek Jaywant ◽  
Amy Kuceyeski
Keyword(s):  
Cognitive Abilities ◽  
Cognitive Tests ◽  
Healthy Individuals ◽  
Default Mode ◽  
Functional Connections ◽  
Functional Connectome ◽  
Human Connectome Project ◽  
Neurological Illnesses ◽  
Males And Females ◽  
Fluid Abilities

AbstractA thorough understanding of sex-independent and sex-specific neurobiological features that underlie cognitive abilities in healthy individuals is essential for the study of neurological illnesses in which males and females differentially experience and exhibit cognitive impairment. Here, we evaluate sex-independent and sex-specific relationships between functional connectivity and individual cognitive abilities in 392 healthy young adults (196 males) from the Human Connectome Project. First, we establish that sex-independent models comparably predict crystallised abilities in males and females, but more accurately predict fluid abilities in males. Second, we demonstrate sex-specific models comparably predict crystallised abilities within and between sexes, and generally fail to predict fluid abilities in either sex. Third, we reveal that largely overlapping connections between visual, dorsal attention, ventral attention, and default mode networks are associated with better performance on crystallised and fluid cognitive tests in males and females, while connections within visual, somatomotor, and default mode networks are associated with poorer performance. Together, our findings suggest that shared neurobiological features of the functional connectome underlie crystallised and fluid abilities across the sexes.

scholarly journals Spatial Patterns for Discriminative Estimation

2019 ◽  
Author(s):  
Alberto Llera ◽  
Roselyne Chauvin ◽  
Peter Mulders ◽  
Jilly Naaijen ◽  
Maarten Mennes ◽  
...  
Keyword(s):  
Spatial Patterns ◽  
Brain Connectivity ◽  
Covariance Structure ◽  
Brain Regions ◽  
Linear Filters ◽  
Cognitive Changes ◽  
Experimental Conditions ◽  
Cognitive States ◽  
Human Connectome Project ◽  
Perfect Discrimination

AbstractFunctional connectivity between brain regions is modulated by cognitive states or experimental conditions. A multivariate methodology that can capture fMRI connectivity maps in light of different experimental conditions would be of primary importance to learn about the specific roles of the different brain areas involved in the observed connectivity variations. Here we detail, adapt, optimize and evaluate a supervised dimensionality reduction model to fMRI timeseries. We demonstrate the strength of such an approach for fMRI data using data from the Human Connectome Project to show that the model provides close to perfect discrimination between different fMRI tasks at low dimensionality. The straightforward interpretability and relevance of the model results is demonstrated by the obtained linear filters relating to anatomical areas well known to be involved on each considered task, and its robustness by testing discriminatory generalization and spatial reproducibility with respect to the number of subjects and fMRI time-points acquired. We additionally suggest how such approach can provide a complementary view to traditional task fMRI analyses by looking at changes in the covariance structure as a substitute to changes in the mean signal. We conclude that the presented methodology provides a robust tool to investigate brain connectivity alterations across induced cognitive changes and has the potential to be used in pathological or pharmacological cohort studies. A publicly available toolbox is provided to facilitate the end use and further development of this methodology to extract Spatial Patterns for Discriminative Estimation (SP♠DE).

scholarly journals Sex Classification by Resting State Brain Connectivity

2019 ◽  
Vol 30 (2) ◽  
pp. 824-835 ◽  
Author(s):  
Susanne Weis ◽  
Kaustubh R Patil ◽  
Felix Hoffstaedter ◽  
Alessandra Nostro ◽  
B T Thomas Yeo ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Brain Organization ◽  
Functional Brain ◽  
Clear Cut ◽  
Human Connectome Project ◽  
Machine Learning Approach ◽  
Imaging Research ◽  
Functional Brain Organization

Abstract A large amount of brain imaging research has focused on group studies delineating differences between males and females with respect to both cognitive performance as well as structural and functional brain organization. To supplement existing findings, the present study employed a machine learning approach to assess how accurately participants’ sex can be classified based on spatially specific resting state (RS) brain connectivity, using 2 samples from the Human Connectome Project (n1 = 434, n2 = 310) and 1 fully independent sample from the 1000BRAINS study (n = 941). The classifier, which was trained on 1 sample and tested on the other 2, was able to reliably classify sex, both within sample and across independent samples, differing both with respect to imaging parameters and sample characteristics. Brain regions displaying highest sex classification accuracies were mainly located along the cingulate cortex, medial and lateral frontal cortex, temporoparietal regions, insula, and precuneus. These areas were stable across samples and match well with previously described sex differences in functional brain organization. While our data show a clear link between sex and regionally specific brain connectivity, they do not support a clear-cut dimorphism in functional brain organization that is driven by sex alone.

scholarly journals Unique mapping of structural and functional connectivity on cognition

2018 ◽  
Author(s):  
J. Zimmermann ◽  
J.G. Griffiths ◽  
A.R. McIntosh
Keyword(s):  
Functional Connectivity ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Unique Mapping ◽  
Human Connectome Project ◽  
Behavioural Patterns ◽  
Wide Range ◽  
Psychological Tasks ◽  
New Evidence

AbstractThe unique mapping of structural and functional brain connectivity (SC, FC) on cognition is currently not well understood. It is not clear whether cognition is mapped via a global connectome pattern or instead is underpinned by several sets of distributed connectivity patterns. Moreover, we also do not know whether the pattern of SC and of FC that underlie cognition are overlapping or distinct. Here, we study the relationship between SC and FC and an array of psychological tasks in 609 subjects from the Human Connectome Project (HCP). We identified several sets of connections that each uniquely map onto different aspects of cognitive function. We found a small number of distributed SC and a larger set of cortico-cortical and cortico-subcortical FC that express this association. Importantly, SC and FC each show unique and distinct patterns of variance across subjects and differential relationships to cognition. The results suggest that a complete understanding of connectome underpinnings of cognition calls for a combination of the two modalities.Significance StatementStructural connectivity (SC), the physical white-matter inter-regional pathways in the brain, and functional connectivity (FC), the temporal co-activations between activity of brain regions, have each been studied extensively. Little is known, however, about the distribution of variance in connections as they relate to cognition. Here, in a large sample of subjects (N = 609), we showed that two sets of brain-behavioural patterns capture the correlations between SC, and FC with a wide range of cognitive tasks, respectively. These brain-behavioural patterns reveal distinct sets of connections within the SC and the FC network and provide new evidence that SC and FC each provide unique information for cognition.

scholarly journals Robust prediction of individual personality from brain functional connectome

2020 ◽  
Vol 15 (3) ◽  
pp. 359-369 ◽  
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.

scholarly journals Auricular transcutaneous vagus nerve stimulation acutely modulates brain connectivity in mice

2021 ◽  
Author(s):  
Cecilia Brambilla Pisoni ◽  
Emma Munoz Moreno ◽  
Ianire Gallego Amaro ◽  
Rafael Maldonado ◽  
Antoni Ivorra ◽  
...  
Keyword(s):  
Vagus Nerve ◽  
Nerve Stimulation ◽  
Vagus Nerve Stimulation ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Familiarization Phase ◽  
Functional Connections ◽  
Cognitive Improvement ◽  
Transcutaneous Vagus Nerve Stimulation ◽  
Wide Range

Background: Brain electrical stimulation techniques take advantage of the intrinsic plasticity of the nervous system, opening a wide range of therapeutic applications. Vagus nerve stimulation (VNS) is an approved adjuvant for drug-resistant epilepsy and depression. Its non-invasive form, auricular transcutaneous VNS (atVNS), is under investigation for applications, including cognitive improvement. Objective: We aimed to study the effects of atVNS on brain connectivity, under conditions that improved memory persistence in CD-1 male mice. Methods: Acute atVNS in the cymba conchae of the left ear was performed using a standard stimulation protocol under light isoflurane anesthesia, immediately or 3 h after the training/familiarization phase of the novel object-recognition memory test (NORT). Another cohort of mice was used for bilateral c-Fos analysis after atVNS administration. Spearman correlation of c-Fos density between each pair of the thirty brain regions analyzed allowed obtaining the network of significant functional connections in stimulated and non-stimulated control brains. Results: NORT performance was enhanced when atVNS was delivered just after, but not 3 h after, the familiarization phase of the task. No alterations in c-Fos density were associated to electrostimulation, but a significant effect of atVNS was observed on c-Fos- based functional connectivity. atVNS induced a clear reorganization of the network, increasing the inter-hemisphere connections and the connectivity of locus coeruleus. Conclusion: Our results provide new insights in the effects of atVNS on memory performance and brain connectivity extending our knowledge of the biological mechanisms of bioelectronics in medicine.

scholarly journals Integrating multimodal connectivity improves prediction of individual cognitive abilities

2020 ◽  
Author(s):  
Elvisha Dhamala ◽  
Keith W. Jamison ◽  
Abhishek Jaywant ◽  
Sarah Dennis ◽  
Amy Kuceyeski
Keyword(s):  
Cognitive Abilities ◽  
Structural Information ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Secondary Analysis ◽  
Learning Approaches ◽  
Activation Patterns ◽  
Verbal Language ◽  
Human Connectome Project ◽  
Deterministic Tractography

SummaryHow white matter pathway integrity and neural co-activation patterns in the brain relate to complex cognitive functions remains a mystery in neuroscience. Here, we integrate neuroimaging, connectomics, and machine learning approaches to explore how multimodal brain connectivity relates to cognition. Specifically, we evaluate whether integrating functional and structural connectivity improves prediction of individual crystallised and fluid abilities in 415 unrelated healthy young adults from the Human Connectome Project. Our primary results are two-fold. First, we demonstrate that integrating functional and structural information – at both a model input or output level – significantly outperforms functional or structural connectivity alone to predict individual verbal/language skills and fluid reasoning/executive function. Second, we show that distinct pairwise functional and structural connections are important for these predictions. In a secondary analysis, we find that structural connectivity derived from deterministic tractography is significantly better than structural connectivity derived from probabilistic tractography to predict individual cognitive abilities.

scholarly journals Regional structural-functional connectome coupling is heritable and associated with age, sex and cognition in adults

2020 ◽  
Author(s):  
Zijin Gu ◽  
Keith Wakefield Jamison ◽  
Mert Rory Sabuncu ◽  
Amy Kuceyeski
Keyword(s):  
Young Adults ◽  
Coupling Strength ◽  
Cognitive Abilities ◽  
Large Scale ◽  
Functional Connectome ◽  
Lingual Gyrus ◽  
Human Connectome Project ◽  
Subcortical Regions ◽  
The Right ◽  
The Relationship

ABSTRACTLarge scale white matter brain connections quantified via the structural connectome (SC) act as the backbone for the flow of functional activation, which can be represented via the functional connectome (FC). Many studies have used statistical analysis or computational modeling techniques to relate SC and FC at a global, whole-brain level. However, relatively few studies have investigated the relationship between individual cortical and subcortical regions’ structural and functional connectivity profiles, here called SC-FC coupling, or how this SC-FC coupling may be heritable or related to age, sex and cognitive abilities. Here, we quantify regional SC-FC coupling in a large group of healthy young adults (22 to 37 years) using diffusion-weighted MRI and resting-state functional MRI data from the Human Connectome Project. We find that while regional SC-FC coupling strengths vary widely across cortical, subcortical and cerebellar regions, they were strongest in highly myelinated visual and somatomotor areas. Additionally, SC-FC coupling displayed a broadly negative association with age and, depending on the region, varied across sexes and with cognitive scores. Specifically, males had higher coupling strength in right supramarginal gyrus and left cerebellar regions while females had higher coupling strength in right visual, right limbic and right cerebellar regions. Furthermore, increased SC-FC coupling in the right lingual gyrus was associated with worse cognitive scores. Finally, we found SC-FC coupling to be highly heritable, particularly in the visual, dorsal attention, and fronto-parietal networks, and, interestingly, more heritable than FC or SC alone. Taken together, these results suggest regional structure-function coupling in young adults decreases with age, varies across sexes in a non-systematic way, is somewhat associated with cognition and is highly heritable.

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