Objectives: This study aimed to investigate the relationship between blood microbiota,specifically bacterial DNA, and cognitive decline in individuals with subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI). The objective was to identify potential microbial signatures that could serve as biomarkers for cognitive deterioration. Methods: Forty-seven participants were recruited, including 13 with aMCI, 20 with SCD, and 14 normal cognition (NC). Blood samples were collected, and microbial DNA was analyzed using 16S rRNA sequencing on the Illumina MiSeq platform. Bioinformatics analyses—including α- and β-diversity measures and differential abundance testing (using edgeR)—were employed to assess microbial diversity and differences in bacterial composition among groups. Logistic regression models were used to evaluate the predictive impact of the microbiota on cognitive decline. Results: Microbial diversity differed significantly between groups, with NC exhibiting the highest α-diversity. Both the aMCI and SCD groups showed reduced diversity. Taxa such as Bacteroidia, Alphaproteobacteria, and Clostridia were significantly decreased in the aMCI group compared to NC (p < 0.05). In contrast, Gammaproteobacteria increased significantly in the aMCI group compared to both NC and SCD, indicating progressive microbial changes from SCD to aMCI. No significant differences were found between the NC and SCD groups. Conclusion Distinct bacterial taxa—particularly the increase in Gammaproteobacteria along with decreases in Bacteroidia, Alphaproteobacteria, and Clostridia—are associated with the progression of cognitive decline. These findings suggest that blood microbiota could serve as potential biomarkers for the early detection of aMCI. However, the small sample size and the lack of control for confounding factors such as diet and medication limit the findings. Larger studies are needed to validate these results and further explore the role of microbiota in neurodegeneration.

Objectives: This study aimed to investigate the relationship between blood microbiota,specifically bacterial DNA, and cognitive decline in individuals with subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI). The objective was to identify potential microbial signatures that could serve as biomarkers for cognitive deterioration. Methods: Forty-seven participants were recruited, including 13 with aMCI, 20 with SCD, and 14 normal cognition (NC). Blood samples were collected, and microbial DNA was analyzed using 16S rRNA sequencing on the Illumina MiSeq platform. Bioinformatics analyses—including α- and β-diversity measures and differential abundance testing (using edgeR)—were employed to assess microbial diversity and differences in bacterial composition among groups. Logistic regression models were used to evaluate the predictive impact of the microbiota on cognitive decline. Results: Microbial diversity differed significantly between groups, with NC exhibiting the highest α-diversity. Both the aMCI and SCD groups showed reduced diversity. Taxa such as Bacteroidia, Alphaproteobacteria, and Clostridia were significantly decreased in the aMCI group compared to NC (p < 0.05). In contrast, Gammaproteobacteria increased significantly in the aMCI group compared to both NC and SCD, indicating progressive microbial changes from SCD to aMCI. No significant differences were found between the NC and SCD groups. Conclusion Distinct bacterial taxa—particularly the increase in Gammaproteobacteria along with decreases in Bacteroidia, Alphaproteobacteria, and Clostridia—are associated with the progression of cognitive decline. These findings suggest that blood microbiota could serve as potential biomarkers for the early detection of aMCI. However, the small sample size and the lack of control for confounding factors such as diet and medication limit the findings. Larger studies are needed to validate these results and further explore the role of microbiota in neurodegeneration.

Objectives: This study aimed to investigate the relationship between blood microbiota,specifically bacterial DNA, and cognitive decline in individuals with subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI). The objective was to identify potential microbial signatures that could serve as biomarkers for cognitive deterioration. Methods: Forty-seven participants were recruited, including 13 with aMCI, 20 with SCD, and 14 normal cognition (NC). Blood samples were collected, and microbial DNA was analyzed using 16S rRNA sequencing on the Illumina MiSeq platform. Bioinformatics analyses—including α- and β-diversity measures and differential abundance testing (using edgeR)—were employed to assess microbial diversity and differences in bacterial composition among groups. Logistic regression models were used to evaluate the predictive impact of the microbiota on cognitive decline. Results: Microbial diversity differed significantly between groups, with NC exhibiting the highest α-diversity. Both the aMCI and SCD groups showed reduced diversity. Taxa such as Bacteroidia, Alphaproteobacteria, and Clostridia were significantly decreased in the aMCI group compared to NC (p < 0.05). In contrast, Gammaproteobacteria increased significantly in the aMCI group compared to both NC and SCD, indicating progressive microbial changes from SCD to aMCI. No significant differences were found between the NC and SCD groups. Conclusion Distinct bacterial taxa—particularly the increase in Gammaproteobacteria along with decreases in Bacteroidia, Alphaproteobacteria, and Clostridia—are associated with the progression of cognitive decline. These findings suggest that blood microbiota could serve as potential biomarkers for the early detection of aMCI. However, the small sample size and the lack of control for confounding factors such as diet and medication limit the findings. Larger studies are needed to validate these results and further explore the role of microbiota in neurodegeneration.

Objectives: This study aimed to investigate the relationship between blood microbiota,specifically bacterial DNA, and cognitive decline in individuals with subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI). The objective was to identify potential microbial signatures that could serve as biomarkers for cognitive deterioration. Methods: Forty-seven participants were recruited, including 13 with aMCI, 20 with SCD, and 14 normal cognition (NC). Blood samples were collected, and microbial DNA was analyzed using 16S rRNA sequencing on the Illumina MiSeq platform. Bioinformatics analyses—including α- and β-diversity measures and differential abundance testing (using edgeR)—were employed to assess microbial diversity and differences in bacterial composition among groups. Logistic regression models were used to evaluate the predictive impact of the microbiota on cognitive decline. Results: Microbial diversity differed significantly between groups, with NC exhibiting the highest α-diversity. Both the aMCI and SCD groups showed reduced diversity. Taxa such as Bacteroidia, Alphaproteobacteria, and Clostridia were significantly decreased in the aMCI group compared to NC (p < 0.05). In contrast, Gammaproteobacteria increased significantly in the aMCI group compared to both NC and SCD, indicating progressive microbial changes from SCD to aMCI. No significant differences were found between the NC and SCD groups. Conclusion Distinct bacterial taxa—particularly the increase in Gammaproteobacteria along with decreases in Bacteroidia, Alphaproteobacteria, and Clostridia—are associated with the progression of cognitive decline. These findings suggest that blood microbiota could serve as potential biomarkers for the early detection of aMCI. However, the small sample size and the lack of control for confounding factors such as diet and medication limit the findings. Larger studies are needed to validate these results and further explore the role of microbiota in neurodegeneration.

Objectives: This study aimed to investigate the relationship between blood microbiota,specifically bacterial DNA, and cognitive decline in individuals with subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI). The objective was to identify potential microbial signatures that could serve as biomarkers for cognitive deterioration. Methods: Forty-seven participants were recruited, including 13 with aMCI, 20 with SCD, and 14 normal cognition (NC). Blood samples were collected, and microbial DNA was analyzed using 16S rRNA sequencing on the Illumina MiSeq platform. Bioinformatics analyses—including α- and β-diversity measures and differential abundance testing (using edgeR)—were employed to assess microbial diversity and differences in bacterial composition among groups. Logistic regression models were used to evaluate the predictive impact of the microbiota on cognitive decline. Results: Microbial diversity differed significantly between groups, with NC exhibiting the highest α-diversity. Both the aMCI and SCD groups showed reduced diversity. Taxa such as Bacteroidia, Alphaproteobacteria, and Clostridia were significantly decreased in the aMCI group compared to NC (p < 0.05). In contrast, Gammaproteobacteria increased significantly in the aMCI group compared to both NC and SCD, indicating progressive microbial changes from SCD to aMCI. No significant differences were found between the NC and SCD groups. Conclusion Distinct bacterial taxa—particularly the increase in Gammaproteobacteria along with decreases in Bacteroidia, Alphaproteobacteria, and Clostridia—are associated with the progression of cognitive decline. These findings suggest that blood microbiota could serve as potential biomarkers for the early detection of aMCI. However, the small sample size and the lack of control for confounding factors such as diet and medication limit the findings. Larger studies are needed to validate these results and further explore the role of microbiota in neurodegeneration.

Background: and Purpose: We aimed to develop the diagnostic matrix of the Seoul Cognitive Status Test (SCST) and compare its performance with traditional paper-and-pencil neuropsychological tests, including the Seoul Neuropsychological Screening Battery-II (SNSB-II) and the Korean version of the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD-K). Methods: We recruited 197 participants from the head-to-head SCST-SNSB cohort, and 204 participants from the head-to-head SCST-CERAD cohort. They underwent either SNSB-II or CERAD-K, in addition to SCST. The diagnostic matrix was developed by combining cognitive function, determined by neuropsychological tests, and activities of daily living (ADL), determined by Instrumental-ADL scales. Results: The diagnostic agreement between the SCST and the SNSB-II was 83.9% (weighted kappa=0.87). The agreement between the SCST and the CERAD-K was 84.3% (weighted kappa=0.88). In the SCST-SNSB cohort, all differences in SCST scores between the cognitively unimpaired (CU), mild cognitive impairment (MCI), and dementia diagnosed with the SNSB-II were significant in all cognitive domains (all p<0.01), except for the executive domain between CU and MCI (p=0.145). In the SCST-CERAD cohort, all differences in SCST scores between the 3 groups diagnosed with the CERAD-K were significant in all cognitive domains (all p<0.01), except for the language and visuospatial domains between MCI and dementia (p=0.169 and p=0.778, respectively). Conclusions Our findings suggest that the tablet-based SCST may be another option to traditional paper-and-pencil neuropsychological tests, especially in situations where time and space are relatively limited, and neuropsychological testing specialists are not available.

Background: and Purpose: We aimed to develop the diagnostic matrix of the Seoul Cognitive Status Test (SCST) and compare its performance with traditional paper-and-pencil neuropsychological tests, including the Seoul Neuropsychological Screening Battery-II (SNSB-II) and the Korean version of the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD-K). Methods: We recruited 197 participants from the head-to-head SCST-SNSB cohort, and 204 participants from the head-to-head SCST-CERAD cohort. They underwent either SNSB-II or CERAD-K, in addition to SCST. The diagnostic matrix was developed by combining cognitive function, determined by neuropsychological tests, and activities of daily living (ADL), determined by Instrumental-ADL scales. Results: The diagnostic agreement between the SCST and the SNSB-II was 83.9% (weighted kappa=0.87). The agreement between the SCST and the CERAD-K was 84.3% (weighted kappa=0.88). In the SCST-SNSB cohort, all differences in SCST scores between the cognitively unimpaired (CU), mild cognitive impairment (MCI), and dementia diagnosed with the SNSB-II were significant in all cognitive domains (all p<0.01), except for the executive domain between CU and MCI (p=0.145). In the SCST-CERAD cohort, all differences in SCST scores between the 3 groups diagnosed with the CERAD-K were significant in all cognitive domains (all p<0.01), except for the language and visuospatial domains between MCI and dementia (p=0.169 and p=0.778, respectively). Conclusions Our findings suggest that the tablet-based SCST may be another option to traditional paper-and-pencil neuropsychological tests, especially in situations where time and space are relatively limited, and neuropsychological testing specialists are not available.

Background: and Purpose: We aimed to develop the diagnostic matrix of the Seoul Cognitive Status Test (SCST) and compare its performance with traditional paper-and-pencil neuropsychological tests, including the Seoul Neuropsychological Screening Battery-II (SNSB-II) and the Korean version of the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD-K). Methods: We recruited 197 participants from the head-to-head SCST-SNSB cohort, and 204 participants from the head-to-head SCST-CERAD cohort. They underwent either SNSB-II or CERAD-K, in addition to SCST. The diagnostic matrix was developed by combining cognitive function, determined by neuropsychological tests, and activities of daily living (ADL), determined by Instrumental-ADL scales. Results: The diagnostic agreement between the SCST and the SNSB-II was 83.9% (weighted kappa=0.87). The agreement between the SCST and the CERAD-K was 84.3% (weighted kappa=0.88). In the SCST-SNSB cohort, all differences in SCST scores between the cognitively unimpaired (CU), mild cognitive impairment (MCI), and dementia diagnosed with the SNSB-II were significant in all cognitive domains (all p<0.01), except for the executive domain between CU and MCI (p=0.145). In the SCST-CERAD cohort, all differences in SCST scores between the 3 groups diagnosed with the CERAD-K were significant in all cognitive domains (all p<0.01), except for the language and visuospatial domains between MCI and dementia (p=0.169 and p=0.778, respectively). Conclusions Our findings suggest that the tablet-based SCST may be another option to traditional paper-and-pencil neuropsychological tests, especially in situations where time and space are relatively limited, and neuropsychological testing specialists are not available.

Background: and Purpose: We aimed to develop the diagnostic matrix of the Seoul Cognitive Status Test (SCST) and compare its performance with traditional paper-and-pencil neuropsychological tests, including the Seoul Neuropsychological Screening Battery-II (SNSB-II) and the Korean version of the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD-K). Methods: We recruited 197 participants from the head-to-head SCST-SNSB cohort, and 204 participants from the head-to-head SCST-CERAD cohort. They underwent either SNSB-II or CERAD-K, in addition to SCST. The diagnostic matrix was developed by combining cognitive function, determined by neuropsychological tests, and activities of daily living (ADL), determined by Instrumental-ADL scales. Results: The diagnostic agreement between the SCST and the SNSB-II was 83.9% (weighted kappa=0.87). The agreement between the SCST and the CERAD-K was 84.3% (weighted kappa=0.88). In the SCST-SNSB cohort, all differences in SCST scores between the cognitively unimpaired (CU), mild cognitive impairment (MCI), and dementia diagnosed with the SNSB-II were significant in all cognitive domains (all p<0.01), except for the executive domain between CU and MCI (p=0.145). In the SCST-CERAD cohort, all differences in SCST scores between the 3 groups diagnosed with the CERAD-K were significant in all cognitive domains (all p<0.01), except for the language and visuospatial domains between MCI and dementia (p=0.169 and p=0.778, respectively). Conclusions Our findings suggest that the tablet-based SCST may be another option to traditional paper-and-pencil neuropsychological tests, especially in situations where time and space are relatively limited, and neuropsychological testing specialists are not available.

The vestibular cortex is a distributed network of multisensory areas that plays a crucial role in balance, posture, and spatial orientation. The core region of the vestibular cortex is the parietoinsular vestibular cortex (PIVC), which is located at the junction between the posterior insula, parietal operculum, and retroinsular region. The PIVC is connected to other vestibular areas, the primary and secondary somatosensory cortices, and the premotor and posterior parietal cortices. It also sends projections to the vestibular nuclei in the brainstem. The PIVC is a multisensory region that integrates vestibular, visual, and somatosensory information to create a representation of head-in-space motion, which is used to control eye movements, posture, and balance. Other regions of the vestibular cortex include the primary somatosensory, posterior parietal, and frontal cortices. The primary somatosensory cortex is involved in processing information about touch and body position. The posterior parietal cortex is involved in integrating vestibular, visual, and somatosensory information to create a representation of spatial orientation. The frontal cortex is involved in controlling posture, and eye movements. The various methods used to stimulate the vestibular receptors in neuroimaging studies include caloric vestibular stimulation (CVS), galvanic vestibular stimulation (GVS), and auditory vestibular stimulation (AVS). CVS uses warm or cold water or air to stimulate the semicircular canals, GVS uses a weak electrical current to stimulate the vestibular nerve, and AVS uses high-intensity clicks or short tone bursts to stimulate the otolithic receptors.