A positive-negative mode of population covariation links brain connectivity, demographics and behavior.
Ontology highlight
ABSTRACT:
SUBMITTER: Smith SM
PROVIDER: S-EPMC4625579 | biostudies-literature | 2015 Nov
REPOSITORIES: biostudies-literature
Publications
A positive-negative mode of population covariation links brain connectivity, demographics and behavior.
Smith Stephen M SM Nichols Thomas E TE Vidaurre Diego D Winkler Anderson M AM Behrens Timothy E J TE Glasser Matthew F MF Ugurbil Kamil K Barch Deanna M DM Van Essen David C DC Miller Karla L KL
Nature neuroscience 20150928 11
We investigated the relationship between individual subjects' functional connectomes and 280 behavioral and demographic measures in a single holistic multivariate analysis relating imaging to non-imaging data from 461 subjects in the Human Connectome Project. We identified one strong mode of population co-variation: subjects were predominantly spread along a single 'positive-negative' axis linking lifestyle, demographic and psychometric measures to each other and to a specific pattern of brain c ...[more]
Similar Datasets
Project description:In mental health research, it has proven difficult to find measures of brain function that provide reliable indicators of mental health and well-being, including susceptibility to mental health disorders. Recently, a family of data-driven analyses have provided such reliable measures when applied to large, population-level datasets. In the current pre-registered replication study, we show that the canonical correlation analysis (CCA) methods previously developed using resting-state magnetic resonance imaging functional connectivity and subject measures (SMs) of cognition and behaviour from healthy adults are also effective in measuring well-being (a 'positive-negative axis') in an independent developmental dataset. Our replication was successful in two out of three of our pre-registered criteria, such that a primary CCA mode's weights displayed a significant positive relationship and explained a significant amount of variance in both functional connectivity and SMs. The only criterion that was not successful was that compared to other modes the magnitude of variance explained by the primary CCA mode was smaller than predicted, a result that could indicate a developmental trajectory of a primary mode. This replication establishes a signature neurotypical relationship between connectivity and phenotype, opening new avenues of research in neuroscience with clear clinical applications.
| S-EPMC8847886 | biostudies-literature
Project description:Multiple human behaviors improve early in life, peaking in young adulthood, and declining thereafter. Several properties of brain structure and function progress similarly across the lifespan. Cognitive and neuroscience research has approached aging primarily using associations between a few behaviors, brain functions, and structures. Because of this, the multivariate, global factors relating brain and behavior across the lifespan are not well understood. We investigated the global patterns of associations between 334 behavioral and clinical measures and 376 brain structural connections in 594 individuals across the lifespan. A single-axis associated changes in multiple behavioral domains and brain structural connections (r = 0.5808). Individual variability within the single association axis well predicted the age of the subject (r = 0.6275). Representational similarity analysis evidenced global patterns of interactions across multiple brain network systems and behavioral domains. Results show that global processes of human aging can be well captured by a multivariate data fusion approach.
| S-EPMC8342440 | biostudies-literature
Project description:Schizophrenia is a complex, debilitating mental disorder characterized by wide-ranging symptoms including delusions, hallucinations (so-called positive symptoms), and impaired motor and speech/language production (so-called negative symptoms). Salience-monitoring theorists propose that abnormal functional communication between the salience network (SN) and default mode network (DMN) begets positive and negative symptoms of schizophrenia, yet prior studies have predominately reported links between disrupted SN/DMN functional communication and positive symptoms. It remains unclear whether disrupted SN/DMN functional communication explains (1) solely positive symptoms or (2) both positive and negative symptoms of schizophrenia. To address this question, we incorporate time-lag-shifted functional network connectivity (FNC) analyses that explored coherence of the resting-state functional magnetic resonance imaging signal of 3 networks (anterior DMN, posterior DMN, and SN) with fixed time lags introduced between network time series (1 TR = 2 s; 2 TR = 4 s). Multivariate linear regression analysis revealed that severity of disordered thought and attentional deficits were negatively associated with 2 TR-shifted FNC between anterior DMN and posterior DMN. Meanwhile, severity of flat affect and bizarre behavior were positively associated with 1 TR-shifted FNC between anterior DMN and SN. These results provide support favoring the hypothesis that lagged SN/DMN functional communication is associated with both positive and negative symptoms of schizophrenia.
| S-EPMC6581131 | biostudies-literature
Project description:A growing field of research explores links between behavioral measures and functional connectivity (FC) assessed using resting-state functional magnetic resonance imaging. Recent studies suggest that measurement of these relationships may be corrupted by head motion artifact. Using data from the Human Connectome Project (HCP), we find that a surprising number of behavioral, demographic, and physiological measures (23 of 122), including fluid intelligence, reading ability, weight, and psychiatric diagnostic scales, correlate with head motion. We demonstrate that "trait" (across-subject) and "state" (across-day, within-subject) effects of motion on FC are remarkably similar in HCP data, suggesting that state effects of motion could potentially mimic trait correlates of behavior. Thus, head motion is a likely source of systematic errors (bias) in the measurement of FC:behavior relationships. Next, we show that data cleaning strategies reduce the influence of head motion and substantially alter previously reported FC:behavior relationship. Our results suggest that spurious relationships mediated by head motion may be widespread in studies linking FC to behavior.
| S-EPMC6410500 | biostudies-literature
Project description:BackgroundThe default mode network (DMN) is a set of brain regions that exhibit synchronized low frequency oscillations at resting-state, and is believed to be relevant to attention and self-monitoring. As the anterior cingulate cortex and hippocampus are impaired in drug addiction and meanwhile are parts of the DMN, the present study examined addiction-related alteration of functional connectivity of the DMN.MethodologyResting-state functional magnetic resonance imaging data of chronic heroin users (14 males, age: 30.1±5.3 years, range from 22 to 39 years) and non-addicted controls (13 males, age: 29.8±7.2 years, range from 20 to 39 years) were investigated with independent component analysis to address their functional connectivity of the DMN.Principal findingsCompared with controls, heroin users showed increased functional connectivity in right hippocampus and decreased functional connectivity in right dorsal anterior cingulate cortex and left caudate in the DMN.ConclusionsThese findings suggest drug addicts' abnormal functional organization of the DMN, and are discussed as addiction-related abnormally increased memory processing but diminished cognitive control related to attention and self-monitoring, which may underlie the hypersensitivity toward drug related cues but weakened strength of cognitive control in the state of addiction.
| S-EPMC3027699 | biostudies-literature
Project description:22q11.2 deletion syndrome (22q11DS) is a genetic mutation associated with disorders of cortical connectivity and social dysfunction. However, little is known about the functional connectivity (FC) of the resting brain in 22q11DS and its relationship with social behavior. A seed-based analysis of resting-state functional magnetic resonance imaging data was used to investigate FC associated with the posterior cingulate cortex (PCC), in (26) youth with 22qDS and (51) demographically matched controls. Subsequently, the relationship between PCC connectivity and Social Responsiveness Scale (SRS) scores was examined in 22q11DS participants. Relative to 22q11DS participants, controls showed significantly stronger FC between the PCC and other default mode network (DMN) nodes, including the precuneus, precentral gyrus and left frontal pole. 22q11DS patients did not show age-associated FC changes observed in typically developing controls. Increased connectivity between PCC, medial prefrontal regions and the anterior cingulate cortex, was associated with lower SRS scores (i.e. improved social competence) in 22q11DS. DMN integrity may play a key role in social information processing. We observed disrupted DMN connectivity in 22q11DS, paralleling reports from idiopathic autism and schizophrenia. Increased strength of long-range DMN connectivity was associated with improved social functioning in 22q11DS. These findings support a 'developmental-disconnection' hypothesis of symptom development in this disorder.
| S-EPMC4158365 | biostudies-literature
Project description:Background: Schizophrenia affects around 1% of the global population. Functional connectivity extracted from resting-state functional magnetic resonance imaging (rs-fMRI) has previously been used to study schizophrenia and has great potential to provide novel insights into the disorder. Some studies have shown abnormal functional connectivity in the default mode network (DMN) of individuals with schizophrenia, and more recent studies have shown abnormal dynamic functional connectivity (dFC) in individuals with schizophrenia. However, DMN dFC and the link between abnormal DMN dFC and symptom severity have not been well-characterized. Method: Resting-state fMRI data from subjects with schizophrenia (SZ) and healthy controls (HC) across two datasets were analyzed independently. We captured seven maximally independent subnodes in the DMN by applying group independent component analysis and estimated dFC between subnode time courses using a sliding window approach. A clustering method separated the dFCs into five reoccurring brain states. A feature selection method modeled the difference between SZs and HCs using the state-specific FC features. Finally, we used the transition probability of a hidden Markov model to characterize the link between symptom severity and dFC in SZ subjects. Results: We found decreases in the connectivity of the anterior cingulate cortex (ACC) and increases in the connectivity between the precuneus (PCu) and the posterior cingulate cortex (PCC) (i.e., PCu/PCC) of SZ subjects. In SZ, the transition probability from a state with weaker PCu/PCC and stronger ACC connectivity to a state with stronger PCu/PCC and weaker ACC connectivity increased with symptom severity. Conclusions: To our knowledge, this was the first study to investigate DMN dFC and its link to schizophrenia symptom severity. We identified reproducible neural states in a data-driven manner and demonstrated that the strength of connectivity within those states differed between SZs and HCs. Additionally, we identified a relationship between SZ symptom severity and the dynamics of DMN functional connectivity. We validated our results across two datasets. These results support the potential of dFC for use as a biomarker of schizophrenia and shed new light upon the relationship between schizophrenia and DMN dynamics.
| S-EPMC8013735 | biostudies-literature
Project description:There is significant interest in the development and application of deep neural networks (DNNs) to neuroimaging data. A growing literature suggests that DNNs outperform their classical counterparts in a variety of neuroimaging applications, yet there are few direct comparisons of relative utility. Here, we compared the performance of three DNN architectures and a classical machine learning algorithm (kernel regression) in predicting individual phenotypes from whole-brain resting-state functional connectivity (RSFC) patterns. One of the DNNs was a generic fully-connected feedforward neural network, while the other two DNNs were recently published approaches specifically designed to exploit the structure of connectome data. By using a combined sample of almost 10,000 participants from the Human Connectome Project (HCP) and UK Biobank, we showed that the three DNNs and kernel regression achieved similar performance across a wide range of behavioral and demographic measures. Furthermore, the generic feedforward neural network exhibited similar performance to the two state-of-the-art connectome-specific DNNs. When predicting fluid intelligence in the UK Biobank, performance of all algorithms dramatically improved when sample size increased from 100 to 1000 subjects. Improvement was smaller, but still significant, when sample size increased from 1000 to 5000 subjects. Importantly, kernel regression was competitive across all sample sizes. Overall, our study suggests that kernel regression is as effective as DNNs for RSFC-based behavioral prediction, while incurring significantly lower computational costs. Therefore, kernel regression might serve as a useful baseline algorithm for future studies.
| S-EPMC6984975 | biostudies-literature
Project description:Understanding the modularity of functional magnetic resonance imaging (fMRI)-derived brain networks or "connectomes" can inform the study of brain function organization. However, fMRI connectomes additionally involve negative edges, which may not be optimally accounted for by existing approaches to modularity that variably threshold, binarize, or arbitrarily weight these connections. Consequently, many existing Q maximization-based modularity algorithms yield variable modular structures. Here, we present an alternative complementary approach that exploits how frequent the blood-oxygen-level-dependent (BOLD) signal correlation between two nodes is negative. We validated this novel probability-based modularity approach on two independent publicly-available resting-state connectome data sets (the Human Connectome Project [HCP] and the 1,000 functional connectomes) and demonstrated that negative correlations alone are sufficient in understanding resting-state modularity. In fact, this approach (a) permits a dual formulation, leading to equivalent solutions regardless of whether one considers positive or negative edges; (b) is theoretically linked to the Ising model defined on the connectome, thus yielding modularity result that maximizes data likelihood. Additionally, we were able to detect novel and consistent sex differences in modularity in both data sets. As data sets like HCP become widely available for analysis by the neuroscience community at large, alternative and perhaps more advantageous computational tools to understand the neurobiological information of negative edges in fMRI connectomes are increasingly important.
| S-EPMC6625529 | biostudies-literature
Project description:ObjectiveTo examine the role that intrinsic functional networks, specifically the default mode network, have on metacognitive accuracy for individuals with moderate to severe traumatic brain injury (TBI).MethodA sample of 44 individuals (TBI, n = 21; healthy controls [HCs], n = 23) were included in the study. All participants underwent an MRI scan and completed neuropsychological testing. Metacognitive accuracy was defined as participants' ability to correctly judge their item-by-item performance on an abstract reasoning task. Metacognitive values were calculated using the signal detection theory approach of area under the receiver operating characteristic curve. Large-scale subnetworks were created using Power's 264 Functional Atlas. The graph theory metric of network strength was calculated for six subsystem networks to measure functional connectivity.ResultsThere were significant interactions between head injury status (TBI or HC) and internetwork connectivity between the anterior default mode network (DMN) and salience network on metacognitive accuracy (R2 = 0.13, p = .047) and between the posterior DMN and salience network on metacognitive accuracy (R2 = 0.15, p = .038). There was an interpretable interaction between head injury status and internetwork connectivity between the attention network and salience network on metacognitive accuracy (R2 = 0.13, p = .067). In all interactions, higher connectivity predicted better metacognitive accuracy in the TBI group, but this relationship was reversed for the HC group.ConclusionEnhanced connectivity to both anterior and posterior regions within the DMN facilitates metacognitive accuracy postinjury. These findings are integrated into a larger literature examining network plasticity in TBI. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
| S-EPMC6763355 | biostudies-literature