Predicting acupuncture efficacy for functional dyspepsia based on functional brain network features: a machine learning study DOI
Tao Yin, Zhaoxuan He, Yuan Chen

et al.

Cerebral Cortex, Journal Year: 2022, Volume and Issue: 33(7), P. 3511 - 3522

Published: Aug. 13, 2022

Abstract Acupuncture is effective in treating functional dyspepsia (FD), while its efficacy varies significantly from different patients. Predicting the responsiveness of patients to acupuncture treatment based on objective biomarkers would assist physicians identify candidates for therapy. One hundred FD were enrolled, and their clinical characteristics brain MRI data collected before after treatment. Taking pre-treatment network as features, we constructed support vector machine models predict These features contributing critically accurate prediction identified, longitudinal analyses these performed responders non-responders. Results demonstrated that achieved an accuracy 0.76 ± 0.03 predicting non-responders, a R2 0.24 0.02 dyspeptic symptoms relief. Thirty-eight associated with orbitofrontal cortex, caudate, hippocampus, anterior insula identified critical predictive features. Changes more pronounced than In conclusion, this study provided promising approach expected facilitate optimization personalized plans FD.

Language: Английский

Functional connectomics in depression: insights into therapies DOI Creative Commons
Ya Chai, Yvette I. Sheline, Desmond J. Oathes

et al.

Trends in Cognitive Sciences, Journal Year: 2023, Volume and Issue: 27(9), P. 814 - 832

Published: June 5, 2023

Language: Английский

Citations

52
Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics DOI Creative Commons
Andrea I. Luppi, Helena M. Gellersen, Zhen-Qi Liu

et al.

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: June 4, 2024

Abstract Functional interactions between brain regions can be viewed as a network, enabling neuroscientists to investigate function through network science. Here, we systematically evaluate 768 data-processing pipelines for reconstruction from resting-state functional MRI, evaluating the effect of parcellation, connectivity definition, and global signal regression. Our criteria seek that minimise motion confounds spurious test-retest discrepancies topology, while being sensitive both inter-subject differences experimental effects interest. We reveal vast systematic variability across pipelines’ suitability connectomics. Inappropriate choice pipeline produce results are not only misleading, but so, with majority failing at least one criterion. However, set optimal consistently satisfy all different datasets, spanning minutes, weeks, months. provide full breakdown each pipeline’s performance inform future best practices in

Language: Английский

Citations

22
Towards precise resting-state fMRI biomarkers in psychiatry: synthesizing developments in transdiagnostic research, dimensional models of psychopathology, and normative neurodevelopment DOI Creative Commons
Linden Parkes, Theodore D. Satterthwaite, Danielle S. Bassett

et al.

Current Opinion in Neurobiology, Journal Year: 2020, Volume and Issue: 65, P. 120 - 128

Published: Nov. 23, 2020

Language: Английский

Citations

98
Structural and functional connectivity reconstruction with CATO - A Connectivity Analysis TOolbox DOI Creative Commons
Siemon C. de Lange,

Koen Helwegen,

Martijn P. van den Heuvel

et al.

NeuroImage, Journal Year: 2023, Volume and Issue: 273, P. 120108 - 120108

Published: April 12, 2023

We describe a Connectivity Analysis TOolbox (CATO) for the reconstruction of structural and functional brain connectivity based on diffusion weighted imaging resting-state MRI data. CATO is multimodal software package that enables researchers to run end-to-end reconstructions from data connectome maps, customize their analyses utilize various packages preprocess Structural maps can be reconstructed with respect user-defined (sub)cortical atlases providing aligned matrices integrative analyses. outline implementation usage processing pipelines in CATO. Performance was calibrated simulated ITC2015 challenge test-retest Human Connectome Project. open-source distributed under MIT License available as MATLAB toolbox stand-alone application at www.dutchconnectomelab.nl/CATO.

Language: Английский

Citations

35
Accounting for motion in resting-state fMRI: What part of the spectrum are we characterizing in autism spectrum disorder? DOI Creative Commons
Mary Beth Nebel, Daniel E. Lidstone, Liwei Wang

et al.

NeuroImage, Journal Year: 2022, Volume and Issue: 257, P. 119296 - 119296

Published: May 10, 2022

The exclusion of high-motion participants can reduce the impact motion in functional Magnetic Resonance Imaging (fMRI) data. However, may change distribution clinically relevant variables study sample, and resulting sample not be representative population. Our goals are two-fold: 1) to document biases introduced by common practices connectivity research 2) introduce a framework address these treating excluded scans as missing data problem. We use autism spectrum disorder children without an intellectual disability illustrate problem potential solution. aggregated from 545 (8–13 years old) who participated resting-state fMRI studies at Kennedy Krieger Institute (173 autistic 372 typically developing) between 2007 2020. found that were more likely than developing children, with 28.5% 16.1% excluded, respectively, using lenient criterion 81.0% 60.1% stricter criterion. usable tended older, have milder social deficits, better motor control, higher ability original sample. These measures also related strength among This suggests generalizability previous reporting naïve analyses (i.e., based only on data) limited selection older less severe clinical profiles because able remain still during rs-fMRI scan. adapt doubly robust targeted minimum loss estimation ensemble machine learning algorithms losses biases. proposed approach selects edges differ approach, supporting this promising solution improve heterogeneous populations which is common.

Language: Английский

Citations

29
Brain Dynamics Complexity as a Signature of Cognitive Decline in Parkinson's Disease DOI Creative Commons
Eleonora Fiorenzato, Sadaf Moaveninejad, Luca Weis

et al.

Movement Disorders, Journal Year: 2023, Volume and Issue: 39(2), P. 305 - 317

Published: Dec. 6, 2023

Abstract Background Higuchi's fractal dimension (FD) captures brain dynamics complexity and may be a promising method to analyze resting‐state functional magnetic resonance imaging (fMRI) data detect the neuronal interaction underlying Parkinson's disease (PD) cognitive decline. Objectives The aim was compare FD with more established index of spontaneous neural activity, fractional amplitude low‐frequency fluctuations (fALFF), identify through machine learning (ML) models which could best distinguish across PD‐cognitive states, ranging from normal cognition (PD‐NC), mild impairment (PD‐MCI) dementia (PDD). Finally, explore correlations between fALFF clinical PD features. Methods Among 118 patients age‐, sex‐, education matched 35 healthy controls, 52 were classified PD‐NC, 46 PD‐MCI, 20 PDD based on an extensive evaluation. metrics computed rs‐fMRI used train ML models. Results outperformed in differentiating reaching overall accuracy 78% (vs. 62%). showed increased within sensorimotor network, central executive network (CEN), default mode (DMN), paralleled by reduction activity CEN DMN, whose strongly linked presence dementia. Further, we found that some DMN critical hubs correlated worse performance severity. Conclusions Our study indicates decline is characterized altered temporal complexity, involving possibly reflecting segregation these networks. Therefore, propose as prognostic biomarker © 2023 Authors. Movement Disorders published Wiley Periodicals LLC behalf International Parkinson Disorder Society.

Language: Английский

Citations

17
The developing brain structural and functional connectome fingerprint DOI Creative Commons
Judit Ciarrusta, Daan Christiaens, Sean P. Fitzgibbon

et al.

Developmental Cognitive Neuroscience, Journal Year: 2022, Volume and Issue: 55, P. 101117 - 101117

Published: May 20, 2022

In the mature brain, structural and functional 'fingerprints' of brain connectivity can be used to identify uniqueness an individual. However, whether characteristics that make a given distinguishable from others already exist at birth remains unknown. Here, we neuroimaging data developing Human Connectome Project (dHCP) preterm born neonates who were scanned twice during perinatal period assess fingerprint. We found 62% participants could identified based on congruence later connectome initial matrix derived earlier timepoint. contrast, similarity between connectomes same subject different time points was low. Only 10% showed greater self-similarity in comparison self-to-other-similarity for connectome. These results suggest is more stable early life represent potential fingerprint individual: relatively appears support changing when must rapidly acquire new skills adapt their environment.

Language: Английский

Citations

25
Spatially heterogeneous structure-function coupling in haemodynamic and electromagnetic brain networks DOI Creative Commons
Zhen-Qi Liu, Golia Shafiei, Sylvain Baillet

et al.

NeuroImage, Journal Year: 2023, Volume and Issue: 278, P. 120276 - 120276

Published: July 13, 2023

The relationship between structural and functional connectivity in the brain is a key question connectomics. Here we quantify patterns of structure-function coupling across neocortex, by comparing estimated using diffusion MRI with both neurophysiological (MEG-based) haemodynamic (fMRI-based) recordings. We find that heterogeneous regions frequency bands. link generally stronger multiple MEG bands compared to resting state fMRI. Structure-function greater slower intermediate faster also systematically follows archetypal sensorimotor-association hierarchy, as well laminar differentiation, peaking granular layer IV. Finally, better explained structure-informed inter-regional communication metrics than alone. Collectively, these results place relationships common frame reference provide starting point for multi-modal understanding brain.

Language: Английский

Citations

12
Benchmarking methods for mapping functional connectivity in the brain DOI Creative Commons
Zhen-Qi Liu, Andrea I. Luppi, Justine Y. Hansen

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: May 8, 2024

The networked architecture of the brain promotes synchrony among neuronal populations and emergence coherent dynamics. These communication patterns can be comprehensively mapped using noninvasive functional imaging, resulting in connectivity (FC) networks. Despite its popularity, FC is a statistical construct operational definition arbitrary. While most studies use zero-lag Pearson's correlations by default, there exist hundreds pairwise interaction statistics broader scientific literature that used to estimate FC. How organization matrix varies with choice statistic fundamental methodological question affects all this rapidly growing field. Here we benchmark topological geometric organization, neurobiological associations, cognitive-behavioral relevance matrices computed large library 239 statistics. We investigate how canonical features networks vary statistic, including (1) hub mapping, (2) weight-distance trade-offs, (3) structure-function coupling, (4) correspondence other neurophysiological networks, (5) individual fingerprinting, (6) brain-behavior prediction. find substantial quantitative qualitative variation across methods. Throughout, observe measures such as covariance (full correlation), precision (partial correlation) distance display multiple desirable properties, close structural connectivity, capacity differentiate individuals predict differences behavior. Using information flow decomposition, methods may arise from differential sensitivity underlying mechanisms inter-regional communication, some more sensitive redundant synergistic flow. In summary, our report highlights importance tailoring specific mechanism research question, providing blueprint for future optimize their method.

Language: Английский

Citations

4
Functional brain network dynamics mediate the relationship between female reproductive aging and interpersonal adversity DOI Creative Commons
Raluca Petrican, Sidhant Chopra, Ashlea Segal

et al.

Nature Mental Health, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 7, 2025

Abstract Premature reproductive aging is linked to heightened stress sensitivity and psychological maladjustment across the life course. However, brain dynamics underlying this relationship are poorly understood. Here, address issue, we analyzed multimodal data from female participants in Adolescent Brain Cognitive Development (longitudinal, N = 441; aged 9–12 years) Human Connectome-Aging (cross-sectional, 130; 36–60 studies. Age-specific intrinsic functional network mediated link between perceptions of greater interpersonal adversity. The adolescent profile overlapped areas glutamatergic dopaminergic receptor density, middle-aged was concentrated visual, attentional default mode networks. two profiles showed opposite relationships with patterns neural variability cortical atrophy observed psychosis versus major depressive disorder. Our findings underscore divergent maturation senescence, which may explain developmentally specific vulnerabilities distinct disorders.

Language: Английский

Citations

0