Diagnostic classification of autism using resting-state fMRI data improves with full correlation functional brain connectivity compared to partial correlation

AbstractNeurofeedback (NF) aptitude, which refers to an individual’s ability to change its brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical NF applications. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude independent of NF-targeting brain regions. We combined the data in fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect the resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Next we validated the prediction model using independent test data from another site. The result showed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting NF aptitude may be involved in the attentional mode-orientation modulation system’s characteristics in task-free resting-state brain activity.

Download Full-text

Network analysis of functional brain connectivity in borderline personality disorder using resting-state fMRI

NeuroImage Clinical ◽

10.1016/j.nicl.2016.02.006 ◽

2016 ◽

Vol 11 ◽

pp. 302-315 ◽

Cited By ~ 32

Author(s):

Tingting Xu ◽

Kathryn R. Cullen ◽

Bryon Mueller ◽

Mindy W. Schreiner ◽

Kelvin O. Lim ◽

...

Keyword(s):

Borderline Personality Disorder ◽

Personality Disorder ◽

Network Analysis ◽

Resting State ◽

Brain Connectivity ◽

Resting State Fmri ◽

Borderline Personality ◽

Functional Brain

Download Full-text

Multi-LSTM Networks for Accurate Classification of Attention Deficit Hyperactivity Disorder from Resting-State fMRI Data

2020 2nd International Conference on Industrial Artificial Intelligence (IAI) ◽

10.1109/iai50351.2020.9262176 ◽

2020 ◽

Author(s):

Rui Liu ◽

Zhi-an Huang ◽

Min Jiang ◽

Kay Chen Tan

Keyword(s):

Attention Deficit Hyperactivity Disorder ◽

Attention Deficit ◽

Resting State ◽

Resting State Fmri ◽

Fmri Data ◽

Hyperactivity Disorder

Download Full-text

Influences of Hunger, Satiety and Oral Glucose on Functional Brain Connectivity: A Multimethod Resting-State fMRI Study

Neuroscience ◽

10.1016/j.neuroscience.2018.04.029 ◽

2018 ◽

Vol 382 ◽

pp. 80-92 ◽

Cited By ~ 15

Author(s):

Arkan Al-Zubaidi ◽

Marcus Heldmann ◽

Alfred Mertins ◽

Kamila Jauch-Chara ◽

Thomas F. Münte

Keyword(s):

Resting State ◽

Brain Connectivity ◽

Resting State Fmri ◽

Oral Glucose ◽

Functional Brain ◽

Fmri Study

Download Full-text

Functional Brain Areas Mapping in Patients with Glioma Based on Resting-State fMRI Data Decomposition

2018 IEEE International Conference on Data Mining Workshops (ICDMW) ◽

10.1109/icdmw.2018.00049 ◽

2018 ◽

Cited By ~ 2

Author(s):

Maksim Sharaev ◽

Alexander Smirnov ◽

Tatiana Melnikova-Pitskhelauri ◽

Vyacheslav Orlov ◽

Evgeny Burnaev ◽

...

Keyword(s):

Resting State ◽

Resting State Fmri ◽

Fmri Data ◽

Data Decomposition ◽

Brain Areas ◽

Functional Brain

Download Full-text

Functional brain connectivity in resting-state fMRI using phase and magnitude data

Journal of Neuroscience Methods ◽

10.1016/j.jneumeth.2017.10.016 ◽

2018 ◽

Vol 293 ◽

pp. 299-309 ◽

Cited By ~ 12

Author(s):

Zikuan Chen ◽

Arvind Caprihan ◽

Eswar Damaraju ◽

Srinivas Rachakonda ◽

Vince Calhoun

Keyword(s):

Resting State ◽

Brain Connectivity ◽

Resting State Fmri ◽

Functional Brain

Download Full-text

Disrupted Functional Brain Connectivity in Partial Epilepsy: A Resting-State fMRI Study

PLoS ONE ◽

10.1371/journal.pone.0028196 ◽

2012 ◽

Vol 7 (1) ◽

pp. e28196 ◽

Cited By ~ 63

Author(s):

Cheng Luo ◽

Chuan Qiu ◽

Zhiwei Guo ◽

Jiajia Fang ◽

Qifu Li ◽

...

Keyword(s):

Resting State ◽

Brain Connectivity ◽

Resting State Fmri ◽

Partial Epilepsy ◽

Functional Brain ◽

Fmri Study

Download Full-text

Evaluating the sensitivity of functional connectivity measures to motion artifact in resting-state fMRI data

10.1101/2020.05.04.072868 ◽

2020 ◽

Author(s):

Arun S. Mahadevan ◽

Ursula A. Tooley ◽

Maxwell A. Bertolero ◽

Allyson P. Mackey ◽

Danielle S. Bassett

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Partial Correlation ◽

Motion Artifact ◽

Resting State Fmri ◽

Alternative Methods ◽

Fmri Data ◽

Retest Reliability ◽

Human Connectome Project ◽

Test Retest Reliability

AbstractFunctional connectivity (FC) networks are typically inferred from resting-state fMRI data using the Pearson correlation between BOLD time series from pairs of brain regions. However, alternative methods of estimating functional connectivity have not been systematically tested for their sensitivity or robustness to head motion artifact. Here, we evaluate the sensitivity of six different functional connectivity measures to motion artifact using resting-state data from the Human Connectome Project. We report that FC estimated using full correlation has a relatively high residual distance-dependent relationship with motion compared to partial correlation, coherence and information theory-based measures, even after implementing rigorous methods for motion artifact mitigation. This disadvantage of full correlation, however, may be offset by higher test-retest reliability and system identifiability. FC estimated by partial correlation offers the best of both worlds, with low sensitivity to motion artifact and intermediate system identifiability, with the caveat of low test-retest reliability. We highlight spatial differences in the sub-networks affected by motion with different FC metrics. Further, we report that intra-network edges in the default mode and retrosplenial temporal sub-networks are highly correlated with motion in all FC methods. Our findings indicate that the method of estimating functional connectivity is an important consideration in resting-state fMRI studies and must be chosen carefully based on the parameters of the study.

Download Full-text

Template-based prediction of vigilance fluctuations in resting-state fMRI

10.1101/234179 ◽

2017 ◽

Cited By ~ 1

Author(s):

Maryam Falahpour ◽

Catie Chang ◽

Chi Wah Wong ◽

Thomas T. Liu

Keyword(s):

Resting State ◽

Brain Connectivity ◽

Resting State Fmri ◽

Fmri Data ◽

Eyes Closed ◽

Vigilance State ◽

Global Signal ◽

Significant Difference ◽

Eyes Open ◽

Positive Correlations

AbstractChanges in vigilance or alertness during a typical resting state fMRI scan are inevitable and have been found to affect measures of functional brain connectivity. Since it is not often feasible to monitor vigilance with EEG during fMRI scans, it would be of great value to have methods for estimating vigilance levels from fMRI data alone. A recent study, conducted in macaque monkeys, proposed a template-based approach for fMRI-based estimation of vigilance fluctuations. Here, we use simultaneously acquired EEG/fMRI data to investigate whether the same template-based approach can be employed to estimate vigilance fluctuations of awake humans across different resting-state conditions. We first demonstrate that the spatial pattern of correlations between EEG-defined vigilance and fMRI in our data is consistent with the previous literature. Notably, however, we observed a significant difference between the eyes-closed (EC) and eyes-open (EO) conditions finding stronger negative correlations with vigilance in regions forming the default mode network and higher positive correlations in thalamus and insula in the EC condition when compared to the EO condition. Taking these correlation maps as “templates” for vigilance estimation, we found that the template-based approach produced fMRI-based vigilance estimates that were significantly correlated with EEG-based vigilance measures, indicating its generalizability from macaques to humans. We also demonstrate that the performance of this method was related to the overall amount of variability in a subject’s vigilance state, and that the template-based approach outperformed the use of the global signal as a vigilance estimator. In addition, we show that the template-based approach can be used to estimate the variability across scans in the amplitude of the vigilance fluctuations. We discuss the benefits and tradeoffs of using the template-based approach in future fMRI studies.

Download Full-text