ArticleNeuroImage. Clinical2019
Physiological significance of R-fMRI indices: Can functional metrics differentiate structural lesions (brain tumors)?
Article in NeuroImage. Clinical, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- Eight-week antidepressant treatment reduces functional connectivity in first-episode drug-naïve patients with major depressive disorder.Human brain mapping · 2021Trial
- Detection of Major Depressive Disorder from Functional Magnetic Resonance Imaging Using Regional Homogeneity and Feature/Sample Selective Evolving Voting Ensemble Approaches.Journal of imaging · 2025Article
- Voxel-based alterations in spontaneous brain activity among very-late-onset schizophrenia-like psychosis: A preliminary resting-state functional magnetic resonance imaging study.World journal of psychiatry · 2025Article
- Hemodynamic and metabolic correspondence of resting-state voxel-based physiological metrics in healthy adults.NeuroImage · 2022Article
- Individual localization value of resting-state fMRI in epilepsy presurgical evaluation: A combined study with stereo-EEG.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2021Article
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Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Resting-state functional MRI (R-fMRI) research has recently entered the era of "big data", however, few studies have provided a rigorous validation of the physiological underpinnings of R-fMRI indices. Although studies have reported that various neuropsychiatric disorders exhibit abnormalities in R-fMRI measures, these "biomarkers" have not been validated in differentiating structural lesions (brain tumors) as a concept proof. We enrolled 60 patients with intracranial tumors located in the unilateral cranialcavity and 60 matched normal controls to test whether R-fMRI indices can differentiate tumors, which represents a prerequisite for adapting such indices as biomarkers for neuropsychiatric disorders. Common R-fMRI indices of tumors and their counterpart control regions, which were defined as the contralateral normal areas (for amplitude of low frequency fluctuations (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo) and degree centrality (DC)) and ipsilateral regions surrounding the tumors (for voxel-mirrored homotopic connectivity (VMHC)), were comprehensively assessed. According to robust paired t-tests with a Bonferroni correction, only VMHC (Fisher's r-to-z transformed) could successfully differentiate substantial tumors from their counterpart normal regions in patients. Furthermore, ALFF and DC were not able to differentiate tumor from normal unless Z-standardization was employed. To validate the lower power of the between-subject design compared to the within-subject design, each metric was calculated in a matched control group, and robust two-sample t-tests were used to compare the patient tumors and the normal controls at the same place. Similarly, only VMHC succeeded in differentiating significant differences between tumors and the sham tumor areas of normal controls. This study tested the premise of R-fMRI biomarkers for differentiating lesions, and brings a new understanding to physical significance of the Z-standardization.
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