Epilepsy is a disease characterized by abnormal spontaneous activity in the brain.Resting-state functional magnetic resonance imaging(RS-fMRI) is a powerful technique for exploring this activity.With good spatial and temporal resolution,RS-fMRI is a promising approach for accurate localization of the focus of seizure activity.Although simultaneous electroencephalogram-fMRI has been performed with patients in the resting state,most studies focused on activation.This mini-review focuses on RS-fMRI alone,including its computational methods and its application to epilepsy.
Objective Behavioral studies have suggested a low-frequency(0.05 Hz) fluctuation of sustained attention on the basis of the intra-individual variability of reaction-time.Conventional task designs for functional magnetic resonance imaging(fMRI) studies are not appropriate for frequency analysis.The present study aimed to propose a new paradigm,real-time finger force feedback(RT-FFF),to study the brain mechanisms of sustained attention and neurofeedback.Methods We compared the low-frequency fluctuations in both behavioral and fMRI data from 38 healthy adults(19 males;mean age,22.3 years).Two fMRI sessions,in RT-FFF and sham finger force feedback(S-FFF) states,were acquired(TR 2 s,Siemens Trio 3-Tesla scanner,8 min each,counter-balanced).Behavioral data of finger force were obtained simultaneously at a sampling rate of 250 Hz.Results Frequency analysis of the behavioral data showed lower amplitude in the lowfrequency band(0.004-0.104 Hz) but higher amplitude in the high-frequency band(27.02-125 Hz) in the RT-FFF than the S-FFF states.The mean finger force was not significantly different between the two states.fMRI data analysis showed higher fractional amplitude of low-frequency fluctuation(fALFF) in the S-FFF than in the RT-FFF state in the visual cortex,but higher fALFF in RT-FFF than S-FFF in the middle frontal gyrus,the superior frontal gyrus,and the default mode network.Conclusion The behavioral results suggest that the proposed paradigm may provide a new approach to studies of sustained attention.The fMRI results suggest that a distributed network including visual,motor,attentional,and default mode networks may be involved in sustained attention and/or real-time feedback.This paradigm may be helpful for future studies on deficits of attention,such as attention deficit hyperactivity disorder and mild traumatic brain injury.
Regional homogeneity(ReHo)and the amplitude of low-frequency fluctuation(ALFF)are two approaches to depicting different regional characteristics of resting-state functional magnetic resonance imaging(RS-fMRI)data.Whether they can complementarily reveal brain regional functional abnormalities in attention-deficit/hyperactivity disorder(ADHD)remains unknown.In this study,we applied ReHo and ALFF to 23 medication-na ve boys diagnosed with ADHD and 25 age-matched healthy male controls using whole-brain voxel-wise analysis.Correlation analyses were conducted in the ADHD group to investigate the relationship between the regional spontaneous brain activity measured by the two approaches and the clinical symptoms of ADHD.We found that the ReHo method showed widely-distributed differences between the two groups in the fronto-cingulo-occipitocerebellar circuitry,while the ALFF method showed a difference only in the right occipital area.When a larger smoothing kernel and a more lenient threshold were used for ALFF,more overlapped regions were found between ALFF and ReHo,and ALFF even found some new regions with group differences.The ADHD symptom scores were correlated with the ReHo values in the right cerebellum,dorsal anterior cingulate cortex and left lingual gyrus in the ADHD group,while no correlation was detected between ALFF and ADHD symptoms.In conclusion,ReHo may be more sensitive to regional abnormalities,at least in boys with ADHD,than ALFF.And ALFF may be complementary to ReHo in measuring local spontaneous activity.Combination of the two may yield a more comprehensive pathophy-siological framework for ADHD.
Li AnQing-Jiu CaoMan-Qiu SuiLi SunQi-Hong ZouYu-Feng ZangYu-Feng Wang
