scholarly journals General Functional Connectivity: shared features of resting-state and task fMRI drive reliable and heritable individual differences in functional brain networks

2018 ◽  
Author(s):  
Maxwell L. Elliott ◽  
Annchen R. Knodt ◽  
Megan Cooke ◽  
M. Justin Kim ◽  
Tracy R. Melzer ◽  
...  
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Resting State ◽  
Biomarker Discovery ◽  
Resting State Functional Connectivity ◽  
Heritable Variation ◽  
State Data ◽  
Human Connectome Project ◽  
Intrinsic Connectivity ◽  
And Task

AbstractIntrinsic connectivity, measured using resting-state fMRI, has emerged as a fundamental tool in the study of the human brain. However, due to practical limitations, many studies do not collect enough resting-state data to generate reliable measures of intrinsic connectivity necessary for studying individual differences. Here we present general functional connectivity (GFC) as a method for leveraging shared features across resting-state and task fMRI and demonstrate in the Human Connectome Project and the Dunedin Study that GFC offers better test-retest reliability than intrinsic connectivity estimated from the same amount of resting-state data alone. Furthermore, at equivalent scan lengths, GFC displays higher heritability on average than resting-state functional connectivity. We also show that predictions of cognitive ability from GFC generalize across datasets, performing as well or better than resting-state or task data alone. Collectively, our work suggests that GFC can improve the reliability of intrinsic connectivity estimates in existing datasets and, subsequently, the opportunity to identify meaningful correlates of individual differences in behavior. Given that task and resting-state data are often collected together, many researchers can immediately derive more reliable measures of intrinsic connectivity through the adoption of GFC rather than solely using resting-state data. Moreover, by better capturing heritable variation in intrinsic connectivity, GFC represents a novel endophenotype with broad applications in clinical neuroscience and biomarker discovery.

Resting-state functional connectivity in multiple sclerosis: An examination of group differences and individual differences

2013 ◽  
Vol 51 (13) ◽  
pp. 2918-2929 ◽  
Author(s):  
Alisha L. Janssen ◽  
Aaron Boster ◽  
Beth A. Patterson ◽  
Amir Abduljalil ◽  
Ruchika Shaurya Prakash
Keyword(s):  
Multiple Sclerosis ◽  
Individual Differences ◽  
Functional Connectivity ◽  
Resting State ◽  
Group Differences ◽  
Resting State Functional Connectivity

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

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.

scholarly journals Predicting Individual Variability in Task-Evoked Brain Activity in Schizophrenia

2020 ◽  
Author(s):  
Niv Tik ◽  
Abigail Livny ◽  
Shachar Gal ◽  
Karny Gigi ◽  
Galia Tsarfaty ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Prediction Model ◽  
Resting State ◽  
Brain Activity ◽  
Strong Association ◽  
Brain Activation ◽  
Individual Variability ◽  
Resting State Functional Connectivity ◽  
Successful Prediction ◽  
And Task

AbstractBACKGROUNDPatients suffering from schizophrenia demonstrate abnormal brain activity, as well as alterations in patterns of functional connectivity assessed by functional magnetic resonance imaging (fMRI). Previous studies in healthy participants suggest a strong association between resting-state functional connectivity and task-evoked brain activity that could be detected at an individual level, and show that brain activation in various tasks could be predicted from task-free fMRI scans. In the current study we aimed to predict brain activity in patients diagnosed with schizophrenia, using a prediction model based on healthy individuals exclusively. This offers novel insights regarding the interrelations between brain connectivity and activity in schizophrenia.METHODSWe generated a prediction model using a group of 80 healthy controls that performed the well-validated N-back task, and used it to predict individual variability in task-evoked brain activation in 20 patients diagnosed with schizophrenia.RESULTSWe demonstrated a successful prediction of individual variability in the task-evoked brain activation based on resting-state functional connectivity. The predictions were highly sensitive, reflected by high correlations between predicted and actual activation maps (Median = 0.589, SD = 0.193) and specific, evaluated by a Kolomogrov-Smirnov test (D = 0.25, p < 0.0001).CONCLUSIONSA Successful prediction of brain activity from resting-state functional connectivity highlights the strong coupling between the two. Moreover, our results support the notion that even though resting-state functional connectivity and task-evoked brain activity are frequently reported to be altered in schizophrenia, the relations between them remains unaffected. This may allow to generate task activity maps for clinical populations without the need the actually perform the task.

scholarly journals Advancing motion denoising of multiband resting state functional connectivity fMRI data

2019 ◽  
Author(s):  
John C. Williams ◽  
Philip N. Tubiolo ◽  
Jacob R. Luceno ◽  
Jared X. Van Snellenberg
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Quantitative Methods ◽  
Resting State Functional Connectivity ◽  
Quantitative Metrics ◽  
Human Connectome Project ◽  
Optimal Censoring ◽  
Temporal And Spatial ◽  
Control Functional ◽  
Subject Motion

AbstractMultiband-accelerated fMRI provides dramatically improved temporal and spatial resolution of resting state functional connectivity (RSFC) studies of the human brain, but poses unique challenges for denoising of subject motion induced data artifacts, a major confound in RSFC research. We comprehensively evaluated existing and novel approaches to volume censoring-based motion denoising in the Human Connectome Project dataset. We show that assumptions underlying common metrics for evaluating motion denoising pipelines, especially those based on quality control-functional connectivity (QC-FC) correlations and differences between high- and low-motion participants, are problematic, making these criteria inappropriate for quantifying pipeline performance. We further develop two new quantitative metrics that are free from these issues and demonstrate their use as benchmarks for comparing volume censoring methods. Finally, we develop rigorous, quantitative methods for determining optimal censoring thresholds and provide straightforward recommendations and code for all investigators to apply this optimized approach to their own RSFC datasets.

scholarly journals Individual differences in frontoparietal plasticity in humans

2021 ◽  
Author(s):  
Austin L Boroshok ◽  
Anne T Park ◽  
Panagiotis Fotiadis ◽  
Gerardo H Velasquez ◽  
Ursula A Tooley ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Individual Differences ◽  
Functional Connectivity ◽  
Animal Models ◽  
Neural Plasticity ◽  
Resting State ◽  
Response Times ◽  
Resting State Functional Connectivity ◽  
Short Term ◽  
Complex Cognition

Neuroplasticity, defined as the brain's ability to change in response to its environment, has been extensively studied at the cellular and molecular levels. Work in animal models suggests that stimulation to the ventral tegmental area (VTA) enhances plasticity, and that myelination constrains plasticity. Little is known, however, about whether proxy measures of these properties in the human brain are associated with learning. Here we investigated the plasticity of the frontoparietal system (FPS), which supports complex cognition. We asked whether VTA resting-state functional connectivity and myelin map (T1-w/T2-w ratio) values predicted learning after short-term training on a FPS-dependent task: the adaptive n-back (n = 46, ages 18-25). We found that stronger connectivity between VTA and lateral prefrontal cortex at baseline predicted greater improvements in accuracy. Lower myelin map values predicted improvement in response times, but not accuracy. Our findings suggest that proxy markers of neural plasticity can predict learning in humans.

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