Preprocessed Consortium for Neuropsychiatric Phenomics dataset

Gorgolewski, Krzysztof J.; Durnez, Joke; Poldrack, Russell A.

doi:10.12688/f1000research.11964.1

Cited by 53 publications

(21 citation statements)

References 24 publications

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“…For details about the full dataset and its procedures, see (Di Martino et al, 2014 ; Di Martino et al ( 2017 ). The data that support the verification of the findings are openly available at https://openneuro.org/datasets/ds000030/versions/00016 , version 00016, and details about the full dataset can be found in Gorgolewski et al ( 2017 ). The preprocessing pipeline used is available at https://version.aalto.fi/gitlab/BML/bramila , commit hash 4f1e6388d6b2e5024ef2380d29e6526bb878242a.…”

Section: Data and Software Availabilitymentioning

confidence: 60%

“…To verify that our results generalize to other datasets, we repeated all analysis using unpreprocessed data from a second, independent dataset. This dataset, UCLA, contains fMRIs of 272 subjects divided in four populations: healthy controls (130 subjects), patients diagnosed with ADHD (43 subjects), bipolar disorder (49 subjects), and schizophrenia (50 subjects; Gorgolewski et al, 2017 ). We chose to compare patients diagnosed with bipolar disorder and healthy controls, as the bipolar population had the largest number of scans that comply with our restrictions in image quality control and framewise displacement.…”

Section: Methodsmentioning

confidence: 99%

“…We assess group differences using network-based statistics (NBS; Zalesky, Fornito, & Bullmore, 2010 ) and compare the results obtained for all spatial smoothing levels. In addition, to address the generalizability of the results, we repeat the analysis for fMRI data from 44 subjects from the UCLA Consortium for Neuropsychiatric Phenomics LA5c Study (Gorgolewski, Durnez, & Poldrack, 2017 ). These subjects are also divided into two groups ( N = 22): bipolar disorder patients and typical controls.…”

Section: Introductionmentioning

confidence: 99%

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Effects of spatial smoothing on group-level differences in functional brain networks

Hoyos

Glerean

Saramäki

et al. 2020

Network Neuroscience

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Brain connectivity with functional magnetic resonance imaging (fMRI) is a popular approach for detecting differences between healthy and clinical populations. Before creating a functional brain network, the fMRI time series must undergo several preprocessing steps to control for artifacts and to improve data quality. However, preprocessing may affect the results in an undesirable way. Spatial smoothing, for example, is known to alter functional network structure. Yet, its effects on group-level network differences remain unknown. Here, we investigate the effects of spatial smoothing on the difference between patients and controls for two clinical conditions: autism spectrum disorder and bipolar disorder, considering fMRI data smoothed with Gaussian kernels (0–32 mm). We find that smoothing affects network differences between groups. For weighted networks, incrementing the smoothing kernel makes networks more different. For thresholded networks, larger smoothing kernels lead to more similar networks, although this depends on the network density. Smoothing also alters the effect sizes of the individual link differences. This is independent of the region of interest (ROI) size, but varies with link length. The effects of spatial smoothing are diverse, nontrivial, and difficult to predict. This has important consequences: The choice of smoothing kernel affects the observed network differences.

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Section: Data and Software Availabilitymentioning

confidence: 60%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Effects of spatial smoothing on group-level differences in functional brain networks

Hoyos

Glerean

Saramäki

et al. 2020

Network Neuroscience

View full text Add to dashboard Cite

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“…In addition, we develop variants of the BART and so we have expertise with the task and any findings would have local applications. We obtained the preprocessed data which is de-identified, motion corrected and coregistered to Montreal Neurological Institute (MNI) standard space K. Gorgolewski, Durnez, and Poldrack (2017).…”

Section: Supportive Informationmentioning

confidence: 99%

“…We used FSL Jenkinson, Beckmann, Behrens, Woolrich, and Smith (2012); Smith et al (2004) to correct for motion and apply a high-pass filter to remove low-frequency noise (cut-off frequency of 100 seconds). The data also includes potential confound regressors K. Gorgolewski et al (2017).…”

Section: Supportive Informationmentioning

confidence: 99%

Identifying Brain Network Topology Changes in Task Processes and Psychiatric Disorders

Rezaeinia

Fairley

Pal

et al. 2019

Preprint

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A central goal in neuroscience is to understand how dynamic networks of neural activity produce effective 12 representations of the world. Advances in the theory of graph measures raise the possibility of elucidating 13 network topologies central to the construction of these representations. We leverage a result from the 14 description of lollipop graphs to identify an iconic network topology in functional magnetic resonance 15 imaging data and characterize changes to those networks during task performance and in populations 16 diagnosed with psychiatric disorders. During task performance, we find that task-relevant subnetworks 17 change topology, becoming more integrated by increasing connectivity throughout cortex. Analysis of 18 resting-state connectivity in clinical populations shows a similar pattern of subnetwork topology changes; 19 resting-scans becoming less default-like with more integrated sensory paths. The study of brain network 20 topologies and their relationship to cognitive models of information processing raises new opportunities for 21 understanding brain function and its disorders. 22 AUTHOR SUMMARY 1 Our mental lives are made up of a series of predictions about the world calculated by our brains. The 23calculations that produce these predictions are a result of how areas in our brain interact. Measures based 24 on graph representations can make it clear what information can be combined and therefore help us better 25 understand the computations the brain is performing. We make use of cutting-edge techniques that 26 overcome a number of previous limitations to identify specific shapes in the functional brain network. 27These shapes are similar to hierarchical processing streams which play a fundamental role in cognitive 28 neuroscience. The importance of these structures and the technique is highlighted by how they change 29 under different task constraints and in individuals diagnosed with psychiatric disorders. 30 INTRODUCTIONHow do we link dynamic changes in functional brain structure to the processing of information? Brain 31 activity organizes into stable networks that vary in strength and change with task demands Greicius, 32 Krasnow, Reiss, and Menon (2003); Smith et al. (2009). 33 Because of its ease of implementation and relatively low cost, the analysis of resting functional magnetic 34 resonance imaging (rfMRI) data Raichle et al. (2001) in particular has had a tremendous impact, leading to 35 several large-scale public initiatives like the Human Connectome Project (HCP) Essen et al. (2013). One of 36the most promising methods used to study rfMRI activation has been to construct network models of 37 functional connectivity between areas of the brain E. T. Bullmore and Bassett (2011); Goñi et al. (2014); 38 van den Heuvel and Pol (2010). These models are characterized by network measures like efficiency 39 Fornito, Zalesky, and Bullmore (2016) and have been applied to a wide variety of challenges including the 40 study of psychiatric disorders (for review, see Avena-Koeni...

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Distinct and temporally associated neural mechanisms underlying concurrent, postsuccess, and posterror cognitive controls: Evidence from a stop‐signal task

Cao

Cannon

2021

Human Brain Mapping

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Cognitive control is built upon the interactions of multiple brain regions. It is currently unclear whether the involved regions are temporally separable in relation to different cognitive processes and how these regions are temporally associated in relation to different task performances. Here, using stop‐signal task data acquired from 119 healthy participants, we showed that concurrent and poststop cognitive controls were associated with temporally distinct but interrelated neural mechanisms. Specifically, concurrent cognitive control activated regions in the cingulo‐opercular network (including the dorsal anterior cingulate cortex [dACC], insula, and thalamus), together with superior temporal gyrus, secondary motor areas, and visual cortex; while regions in the fronto‐parietal network (including the lateral prefrontal cortex [lPFC] and inferior parietal lobule) and cerebellum were only activated during poststop cognitive control. The associations of activities between concurrent and poststop regions were dependent on task performance, with the most notable difference in the cerebellum. Importantly, while concurrent and poststop signals were significantly correlated during successful cognitive control, concurrent activations during erroneous trials were only correlated with posterror activations in the fronto‐parietal network but not cerebellum. Instead, the cerebellar activation during posterror cognitive control was likely to be driven secondarily by posterror activation in the lPFC. Further, a dynamic causal modeling analysis demonstrated that postsuccess cognitive control was associated with inhibitory connectivity from the lPFC to cerebellum, while excitatory connectivity from the lPFC to cerebellum was present during posterror cognitive control. Overall, these findings suggest dissociable but temporally related neural mechanisms underlying concurrent, postsuccess, and posterror cognitive control processes in healthy individuals.

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Preprocessed Consortium for Neuropsychiatric Phenomics dataset

Cited by 53 publications

References 24 publications

Effects of spatial smoothing on group-level differences in functional brain networks

Effects of spatial smoothing on group-level differences in functional brain networks

Identifying Brain Network Topology Changes in Task Processes and Psychiatric Disorders

Distinct and temporally associated neural mechanisms underlying concurrent, postsuccess, and posterror cognitive controls: Evidence from a stop‐signal task

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