How do tasks impact the reliability of fMRI functional connectivity? DOI Creative Commons
Shefali Rai, Kirk Graff, Ryann Tansey

et al.

Human Brain Mapping, Journal Year: 2024, Volume and Issue: 45(3)

Published: Feb. 13, 2024

Abstract While there is growing interest in the use of functional magnetic resonance imaging‐functional connectivity (fMRI‐FC) for biomarker research, low measurement reliability conventional acquisitions may limit applications. Factors known to impact FC include scan length, head motion, signal properties, such as temporal signal‐to‐noise ratio (tSNR), and acquisition state or task. As tasks a region‐wise fashion, they likely differently across brain, making task an important decision study design. Here, we densely sampled Midnight Scan Club (MSC) dataset, comprising 5 h rest 6 fMRI data 10 healthy adults, investigate regional effects on reliability. We further considered how BOLD properties contributing tSNR, that is, mean (tMean) standard deviation (tSD), vary associate with reliability, are modulated by tasks. found that, relative rest, enhanced increased tSD specific task‐engaged regions. However, variability broadly dampened during outside From our analyses, observed was strongest driver Overall, findings suggest choice can have should be relation maximizing networks part

Language: Английский

On the stability of canonical correlation analysis and partial least squares with application to brain-behavior associations DOI Creative Commons
Markus Helmer, Shaun Warrington, Ali‐Reza Mohammadi‐Nejad

et al.

Communications Biology, Journal Year: 2024, Volume and Issue: 7(1)

Published: Feb. 21, 2024

Abstract Associations between datasets can be discovered through multivariate methods like Canonical Correlation Analysis (CCA) or Partial Least Squares (PLS). A requisite property for interpretability and generalizability of CCA/PLS associations is stability their feature patterns. However, in high-dimensional questionable, as found empirical characterizations. To study these issues systematically, we developed a generative modeling framework to simulate synthetic datasets. We that when sample size relatively small, but comparable typical studies, are highly unstable inaccurate; both magnitude importantly the pattern underlying association. confirmed trends across two neuroimaging modalities independent with n ≈ 1000 = 20,000, only latter comprised sufficient observations stable mappings imaging-derived behavioral features. further power calculator provide sizes required reliability analyses. Collectively, characterize how limit detrimental effects overfitting on stability, recommendations future studies.

Language: Английский

Citations

60
Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions DOI Open Access
Aristotle N. Voineskos, Colin Hawco, Nicholas H. Neufeld

et al.

World Psychiatry, Journal Year: 2024, Volume and Issue: 23(1), P. 26 - 51

Published: Jan. 12, 2024

Functional neuroimaging emerged with great promise and has provided fundamental insights into the neurobiology of schizophrenia. However, it faced challenges criticisms, most notably a lack clinical translation. This paper provides comprehensive review critical summary literature on functional neuroimaging, in particular magnetic resonance imaging (fMRI), We begin by reviewing research fMRI biomarkers schizophrenia high risk phase through historical lens, moving from case-control regional brain activation to global connectivity advanced analytical approaches, more recent machine learning algorithms identify predictive features. Findings studies negative symptoms as well neurocognitive social cognitive deficits are then reviewed. neural markers these may represent promising treatment targets Next, we summarize related antipsychotic medication, psychotherapy psychosocial interventions, neurostimulation, including response resistance, therapeutic mechanisms, targeting. also utility data-driven approaches dissect heterogeneity schizophrenia, beyond comparisons, methodological considerations advances, consortia precision fMRI. Lastly, limitations future directions field discussed. Our suggests that, order for be clinically useful care patients should address potentially actionable decisions that routine treatment, such which prescribed or whether given patient is likely have persistent impairment. The potential influenced must weighed against cost accessibility factors. Future evaluations prognostic consider health economics analysis.

Language: Английский

Citations

29
Two common and distinct forms of variation in human functional brain networks DOI

Ally Dworetsky,

Benjamin A. Seitzman,

Babatunde Adeyemo

et al.

Nature Neuroscience, Journal Year: 2024, Volume and Issue: 27(6), P. 1187 - 1198

Published: April 30, 2024

Language: Английский

Citations

21
On stability of Canonical Correlation Analysis and Partial Least Squares with application to brain-behavior associations DOI Open Access
Markus Helmer, Shaun Warrington, Ali‐Reza Mohammadi‐Nejad

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2020, Volume and Issue: unknown

Published: Aug. 25, 2020

Abstract Associations between datasets can be discovered through multivariate methods like Canonical Correlation Analysis (CCA) or Partial Least Squares (PLS). A requisite property for interpretability and generalizability of CCA/PLS solutions is stability feature patterns driving an association. However, in high-dimensional questionable, as found empirical characterizations. To study these issues a systematic manner, we developed generative modeling framework to simulate synthetic datasets, parameterized by dimensionality, variance structure, association strength. We that when sample size relatively small, but comparable typical studies, associations are highly unstable inaccurate; both their magnitude importantly the latent pattern underlying confirmed trends across two neuroimaging modalities, functional diffusion MRI, independent Human Connectome Project (n ≈ 1000) UK Biobank 20000) only latter comprised sufficient samples stable mappings imaging-derived behavioral features. further power calculator provide sizes required reliability analyses future studies.

Language: Английский

Citations

73
Functional neuroimaging in psychiatry and the case for failing better DOI Creative Commons
Matthew M. Nour, Yunzhe Liu, Raymond J. Dolan

et al.

Neuron, Journal Year: 2022, Volume and Issue: 110(16), P. 2524 - 2544

Published: Aug. 1, 2022

Language: Английский

Citations

71
The Burden of Reliability: How Measurement Noise Limits Brain-Behaviour Predictions DOI Creative Commons
Martin Gell, Simon B. Eickhoff, Amir Omidvarnia

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Feb. 10, 2023

Abstract Major efforts in human neuroimaging strive to understand individual differences and find biomarkers for clinical applications by predicting behavioural phenotypes from brain imaging data. An essential prerequisite identifying generalizable replicable brain-behaviour prediction models is sufficient measurement reliability. However, the selection of targets predominantly guided scientific interest or data availability rather than reliability considerations. Here we demonstrate impact low phenotypic on out-of-sample performance. Using simulated empirical Human Connectome Projects, found that levels common across many can markedly limit ability link behaviour. Next, using 5000 subjects UK Biobank, show only highly reliable fully benefit increasing sample sizes hundreds thousands participants. Overall, our findings highlight importance brain–behaviour associations differences.

Language: Английский

Citations

43
Replicable brain–phenotype associations require large-scale neuroimaging data DOI
Shu Liu, Abdel Abdellaoui, Karin J. H. Verweij

et al.

Nature Human Behaviour, Journal Year: 2023, Volume and Issue: 7(8), P. 1344 - 1356

Published: June 26, 2023

Language: Английский

Citations

43
Microstructural and functional plasticity following repeated brain stimulation during cognitive training in older adults DOI Creative Commons
Daria Antonenko, Anna Elisabeth Fromm, Friederike Thams

et al.

Nature Communications, Journal Year: 2023, Volume and Issue: 14(1)

Published: June 2, 2023

Abstract The combination of repeated behavioral training with transcranial direct current stimulation (tDCS) holds promise to exert beneficial effects on brain function beyond the trained task. However, little is known about underlying mechanisms. We performed a monocenter, single-blind randomized, placebo-controlled trial comparing cognitive concurrent anodal tDCS (target intervention) sham (control intervention), registered at ClinicalTrial.gov (Identifier NCT03838211). primary outcome (performance in task) and secondary outcomes transfer tasks) were reported elsewhere. Here, mechanisms addressed by pre-specified analyses multimodal magnetic resonance imaging before after three-week executive prefrontal 48 older adults. Results demonstrate that combined active modulated white matter microstructure which predicted individual task performance gain. Training-plus-tDCS also resulted microstructural grey alterations site, increased functional connectivity. provide insight into neuromodulatory interventions, suggesting tDCS-induced changes fiber organization myelin formation, glia-related synaptic processes target region, synchronization within targeted networks. These findings advance mechanistic understanding neural effects, thereby contributing more network modulation future experimental translation applications.

Language: Английский

Citations

38
Reply to: Multivariate BWAS can be replicable with moderate sample sizes DOI Creative Commons
Brenden Tervo‐Clemmens, Scott Marek, Roselyne J. Chauvin

et al.

Nature, Journal Year: 2023, Volume and Issue: 615(7951), P. E8 - E12

Published: March 8, 2023

Language: Английский

Citations

30
The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium DOI Creative Commons
Willem B. Bruin, Yoshinari Abe,

Pino Alonso

et al.

Molecular Psychiatry, Journal Year: 2023, Volume and Issue: 28(10), P. 4307 - 4319

Published: May 2, 2023

Abstract Current knowledge about functional connectivity in obsessive-compulsive disorder (OCD) is based on small-scale studies, limiting the generalizability of results. Moreover, majority studies have focused only predefined regions or networks rather than throughout entire brain. Here, we investigated differences resting-state between OCD patients and healthy controls (HC) using mega-analysis data from 1024 1028 HC 28 independent samples ENIGMA-OCD consortium. We assessed group whole-brain at both regional network level, whether could serve as biomarker to identify patient status individual level machine learning analysis. The mega-analyses revealed widespread abnormalities OCD, with global hypo-connectivity (Cohen’s d : -0.27 -0.13) few hyper-connections, mainly thalamus 0.19 0.22). Most hypo-connections were located within sensorimotor no fronto-striatal found. Overall, classification performances poor, area-under-the-receiver-operating-characteristic curve (AUC) scores ranging 0.567 0.673, better for medicated (AUC = 0.702) unmedicated 0.608) versus controls. These findings provide partial support existing pathophysiological models highlight important role OCD. However, does not so far an accurate identifying level.

Language: Английский

Citations

30