Olfaction could prove to be an early marker of neurodegenerative diseases, including Alzheimer's and Parkinson's diseases. To use olfaction for disease diagnosis, elucidating the standard olfactory functions in healthy humans is necessary. However, the olfactory function in the human brain is less frequently assessed because of methodological difficulties associated with olfactory-related cerebral areas. Using ultra-high fields (UHF), functional magnetic resonance imaging (fMRI) with high spatial resolution and sensitivity may allow for the measurement of activation in the cerebral areas. This study aimed to apply 7-Tesla fMRI to assess olfactory function in the human brain by exposing individuals to four different odorants for 8 s. We found that olfactory stimulation mainly activated the piriform and orbitofrontal cortex in addition to the amygdala. Among these regions, univariate fMRI analysis indicated that subjective odor intensity significantly correlated with the averaged fMRI signals in the piriform cortex but not with subjective hedonic tone in any region. In contrast, multivariate fMRI analysis showed that subjective hedonic tone could be discriminated from the fMRI response patterns in the posterior orbitofrontal cortex. Thus, the piriform cortex is mainly associated with subjective odor intensity, whereas the posterior orbitofrontal cortex are involved in the discrimination of the subjective hedonic tone of the odorant. UHF-fMRI may be useful for assessing olfactory function in the human brain.

Brain age prediction is a practical method used to quantify brain aging and detect neurodegenerative diseases such as Alzheimer’s disease (AD). However, very few studies have considered brain age prediction as a biomarker for the conversion of cognitively normal (CN) to mild cognitive impairment (MCI). In this study, we developed a novel brain age prediction model using brain volume and cortical thickness features. We calculated an acceleration of brain age (ABA) derived from the suggested model to estimate different diagnostic groups (CN, MCI, and AD) and to classify CN to MCI and MCI to AD conversion groups. We observed a strong association between ABA and the 3 diagnostic groups. Additionally, the classification models for CN to MCI conversion and MCI to AD conversion exhibited acceptable and robust performances, with area under the curve values of 0.66 and 0.76, respectively. We believe that our proposed model provides a reliable estimate of brain age for elderly individuals and can identify those at risk of progressing from CN to MCI. This model has great potential to reveal a diagnosis associated with a change in cognitive decline.

Resting-state fMRIs (rs-fMRIs) have been widely used for investigation of diverse brain functions, including brain cognition. The rs-fMRI has easily elucidated rs-fMRI metrics, such as the fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC), and degree centrality (DC). To increase the applicability of these metrics, higher reliability is required by reducing confounders that are not related to the functional connectivity signal. Many previous studies already demonstrated the effects of physiological artifact removal from rs-fMRI data, but few have evaluated the effect on rs-fMRI metrics. In this study, we examined the effect of physiological noise correction on the most common rs-fMRI metrics. We calculated the intraclass correlation coefficient of repeated measurements on parcellated brain areas by applying physiological noise correction based on the RETROICOR method. Then, we evaluated the correction effect for five rs-fMRI metrics for the whole brain: FC, fALFF, ReHo, VMHC, and DC. The correction effect depended not only on the brain region, but also on the metric. Among the five metrics, the reliability in terms of the mean value of all ROIs was significantly improved for FC, but it deteriorated for fALFF, with no significant differences for ReHo, VMHC, and DC. Therefore, the decision on whether to perform the physiological correction should be based on the type of metric used.

Noise suppression is essential for improving the reliability of functional MRI (fMRI), particularly at ultra-high field strengths where system- and receiver-related fluctuations become prominent. We introduce a hardware-informed denoising method, Coil Sensitivity Filter (CsFilter), which exploits spatial sensitivity relationships among receiver coil elements to identify and suppress non-brain-origin signal components. CsFilter operates in the frequency domain by retaining temporal fluctuations whose coil-amplitude ordering is consistent with receiver sensitivity profiles. Using 7T task-based fMRI data acquired with a 32-channel head coil, CsFilter produced robust increases in voxel-wise temporal signal-to-noise ratio relative to conventional high-pass filtering, with more than 62% of voxels showing greater than twofold improvement. Task-based analyses showed increased statistical strength, with mean t-values rising from 4.68 to 5.74 and median t-values from 4.39 to 5.28. These results indicate that coil-sensitivity-guided filtering provides an effective and complementary strategy for suppressing additive non-brain-origin fluctuations in multi-channel fMRI data.

Objectives: Brain magnetic resonance imaging (MRI) contains identifiable facial and cranial features, creating privacy risks that can limit secondary research use. This review examines current MRI de-identification technologies, quantitative validation methods, and governance frameworks to identify practical strategies for preserving data utility while protecting patient privacy. Methods: A descriptive narrative review was conducted across technical and policy domains. Studies of facial deidentification were analyzed according to the tools used, validation procedures, and downstream analytic performance. The reviewed approaches included traditional defacing, refacing, and deep-learning-based anonymization. Evaluation frameworks used the structural similarity index measure (SSIM), Dice similarity coefficient (DSC), intraclass correlation coefficient (ICC), and the paired t-test to quantify both privacy preservation and analytic fidelity. A parallel policy analysis compared the Health Insurance Portability and Accountability Act (HIPAA), the General Data Protection Regulation (GDPR), Japan’s Act on Anonymized Medical Information, Taiwan’s Personal Data Protection Act, and South Korea’s Personal Information Protection Act and 2024 Health Data Use Guidelines to assess policy convergence and institutional consistency. Results: Visual inspection studies reported that FreeSurfer preserved cortical anatomy but incompletely removed facial features, whereas FSL_deface overmasked some nonfacial regions. Artificial intelligence (AI)-based recognition tests achieved 28%–38% accuracy on defaced data, confirming measurable residual re-identification risk. Quantitative assessments identified segmentation degradation, including a DSC decrease from 0.970 to 0.918, and regional volumetric variability, including a hippocampal ICC of 0.742, with p < 0.05. Generative adversarial network-based refacing improved perceptual similarity, with SSIM values >0.7, but retained subtle facial geometry. The governance analysis indicated that HIPAA and GDPR provide established standards, whereas South Korea’s Data Review Board oversight remains discretionary and nonuniform, limiting reproducibility across institutions. Conclusions: MRI de-identification requires integrated pipelines that combine AI-based facial masking and metadata cleansing with standardized evaluation metrics and enforceable review protocols.

Task-based functional MRI (tb-fMRI) has gained prominence for investigating brain connectivity by engaging specific functional networks during cognitive or behavioral tasks. Compared to resting-state fMRI (rs-fMRI), tb-fMRI provides greater specificity and interpretability, making it a valuable tool for examining task-relevant networks and individual differences in brain function. In this study, we evaluated the utility of the BOLD-filter-a method originally developed to extract reliable BOLD (blood oxygenation level-dependent) components from rs-fMRI-by applying it to tb-fMRI data as a preprocessing step for functional connectivity (FC) analysis. The goal was to enhance the sensitivity and specificity of detecting task-induced functional activity. Compared to the conventional preprocessing method, the BOLD-filter substantially improved the isolation of task-evoked BOLD signals. It identified over eleven times more activation voxels at a high statistical threshold and more than twice as many at a lower threshold. Moreover, FC networks derived from BOLD-filtered signals revealed clearer task-related patterns, including gender-specific differences in brain regions linked to everyday behaviors. These patterns were not detectable using conventional preprocessing approaches. Our findings demonstrate that the BOLD-filter enhances the robustness and interpretability of FC analysis in tb-fMRI. By effectively isolating meaningful functional networks, this approach offers advantages over conventional preprocessing methods. Overall, the BOLD-filter provides a useful improvement for enhancing the characterization of task-induced brain activity in tb-fMRI analysis.