Objective: Thought-action fusion (TAF), one of the most-studied dysfunctional beliefs in obsessive-compulsive disorder, represents an individual’s belief that his/her thoughts directly influence events. TAF belief types are divided into personal thoughts relating to positive (positive TAF) and negative outcomes (negative TAF). However, the neural mechanisms underlying both aspects of the TAF response remain elusive. Methods: This functional magnetic resonance imaging study aimed to investigate the neural circuits related to positive and negative TAF and their relationships with psychological measures. Thirty-one healthy male volunteers participated in a modified TAF task wherein they were asked to read the name of a close person embedded in positive statements (PS) or negative statements (NS). Results: Conjunction analysis revealed activation of the fusiform and lingual gyri, midcingulate and superior medial frontal gyri, inferior orbitofrontal gyrus, and temporoparietal junction. The NS ＞ PS comparison showed additional activation in the precuneus and medial prefrontal cortex, superior frontal gyrus, insula, globus pallidus, thalamus, and midbrain. Precuneus activity was associated with the TAF score among these areas. Moreover, activity in the inferior orbitofrontal gyrus, insula, superior, middle and medial frontal gyri, globus pallidus, inferior parietal lobule, and precuneus was associated with dimensional obsessive-compulsive scores. In contrast, the PS ＞ NS comparison revealed no significant activation. Conclusion These results suggest that negative TAF, relative to positive TAF, recruits additional regions for self-referential processing, salience, and habitual responding, which may contribute to the activation of the belief that a negative thought increases the probability of that negative outcome.

Background: s and Objectives: This study aimed to evaluate the applicability and precision of a ring-type cuffless blood pressure (BP) measurement device, CART-I Plus, compared to conventional 24-hour ambulatory BP monitoring (ABPM). Methods: Forty patients were recruited, and 33 participants were included in the final analysis.Each participant wore both CART-I Plus and ABPM devices on the same arm for approximately 24 hours. BP estimation from CART-I Plus, derived from photoplethysmography (PPG) signals, were compared with the corresponding ABPM measurements. Results: The CART-I Plus recorded systolic blood pressure (SBP)/diastolic blood pressure (DBP) values of 131.4±14.1/81.1±12.0, 132.7±13.9/81.9±11.9, and 128.7±14.6/79.3±12.2 mmHg for 24-hour, daytime, and nighttime periods respectively, compared to ABPM values of 129.7±11.7/84.4±11.2, 131.9±11.6/86.3±11.1, and 124.5±13.6/80.0±12.2 mmHg.Mean differences in SBP/DBP between the two devices were 1.74±6.69/−3.24±6.51 mmHg, 0.75±7.44/−4.41±7.42 mmHg, and 4.15±6.15/−0.67±5.23 mmHg for 24-hour, daytime, and nighttime periods respectively. Strong correlations were also observed between the devices, with r=0.725 and r=0.750 for transitions in SBP and DBP from daytime to nighttime, respectively (both p<0.001). Conclusions The CART-I Plus device, with its unique ring-type design, shows promising accuracy in BP estimation and offers a potential avenue for continuous BP monitoring in clinical practice.

Background: s and Objectives: This study aimed to evaluate the applicability and precision of a ring-type cuffless blood pressure (BP) measurement device, CART-I Plus, compared to conventional 24-hour ambulatory BP monitoring (ABPM). Methods: Forty patients were recruited, and 33 participants were included in the final analysis.Each participant wore both CART-I Plus and ABPM devices on the same arm for approximately 24 hours. BP estimation from CART-I Plus, derived from photoplethysmography (PPG) signals, were compared with the corresponding ABPM measurements. Results: The CART-I Plus recorded systolic blood pressure (SBP)/diastolic blood pressure (DBP) values of 131.4±14.1/81.1±12.0, 132.7±13.9/81.9±11.9, and 128.7±14.6/79.3±12.2 mmHg for 24-hour, daytime, and nighttime periods respectively, compared to ABPM values of 129.7±11.7/84.4±11.2, 131.9±11.6/86.3±11.1, and 124.5±13.6/80.0±12.2 mmHg.Mean differences in SBP/DBP between the two devices were 1.74±6.69/−3.24±6.51 mmHg, 0.75±7.44/−4.41±7.42 mmHg, and 4.15±6.15/−0.67±5.23 mmHg for 24-hour, daytime, and nighttime periods respectively. Strong correlations were also observed between the devices, with r=0.725 and r=0.750 for transitions in SBP and DBP from daytime to nighttime, respectively (both p<0.001). Conclusions The CART-I Plus device, with its unique ring-type design, shows promising accuracy in BP estimation and offers a potential avenue for continuous BP monitoring in clinical practice.

Background: s and Objectives: This study aimed to evaluate the applicability and precision of a ring-type cuffless blood pressure (BP) measurement device, CART-I Plus, compared to conventional 24-hour ambulatory BP monitoring (ABPM). Methods: Forty patients were recruited, and 33 participants were included in the final analysis.Each participant wore both CART-I Plus and ABPM devices on the same arm for approximately 24 hours. BP estimation from CART-I Plus, derived from photoplethysmography (PPG) signals, were compared with the corresponding ABPM measurements. Results: The CART-I Plus recorded systolic blood pressure (SBP)/diastolic blood pressure (DBP) values of 131.4±14.1/81.1±12.0, 132.7±13.9/81.9±11.9, and 128.7±14.6/79.3±12.2 mmHg for 24-hour, daytime, and nighttime periods respectively, compared to ABPM values of 129.7±11.7/84.4±11.2, 131.9±11.6/86.3±11.1, and 124.5±13.6/80.0±12.2 mmHg.Mean differences in SBP/DBP between the two devices were 1.74±6.69/−3.24±6.51 mmHg, 0.75±7.44/−4.41±7.42 mmHg, and 4.15±6.15/−0.67±5.23 mmHg for 24-hour, daytime, and nighttime periods respectively. Strong correlations were also observed between the devices, with r=0.725 and r=0.750 for transitions in SBP and DBP from daytime to nighttime, respectively (both p<0.001). Conclusions The CART-I Plus device, with its unique ring-type design, shows promising accuracy in BP estimation and offers a potential avenue for continuous BP monitoring in clinical practice.

Objective: Recent studies highlighted the triple-network model which illustrated the interactions among three large-scale networks including salience network (SN). The functional magnetic resonance imaging used in this study was designed to investigate the characteristics of three large-scale networks associated with the thought-action fusion (TAF) in patients with obsessive-compulsive disorder (OCD) using power spectral density (PSD) analysis. Methods: This study included 32 OCD patients and 38 age-matched healthy controls (HC). The TAF task was modified from the experiment of Rassin. PSD from time courses in large-scale networks of each subject was measured to compare between the groups for both TAF and resting state. Results: In SN, OCD reported lower power in the low-frequency domain of SN compared to HC using the two-sample t test during the TAF task (t = −2.395, p = 0.019) but not in the resting state. The PSD in the low-frequency domain of the SN had a significant negative correlation with state score in the guilty inventory (r = −0.361, p = 0.042) in OCD patients. Conclusion This study suggests that OCD patients showed reduced SN power which can be prominent in a certain situation, such as TAF. In addition, the PSD alterations in SN cause difficulty in processing ambiguous emotional cues in social situations, and the difficulty can be connected with a negative feeling (e.g., guilt).

Objective: To investigate the agreement and reliability of estimating the volumes and normative percentiles (N%) of segmented brain regions among NeuroQuant (NQ), DeepBrain (DB), and FreeSurfer (FS) software programs, focusing on the comparison between NQ and DB. Materials and Methods: Three-dimensional T1-weighted images of 145 participants (48 healthy participants, 50 patients with mild cognitive impairment, and 47 patients with Alzheimer’s disease) from a single medical center (SMC) dataset and 130 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset were included in this retrospective study. All images were analyzed with DB, NQ, and FS software to obtain volume estimates and N% of various segmented brain regions. We used Bland–Altman analysis, repeated measures ANOVA, reproducibility coefficient, effect size, and intraclass correlation coefficient (ICC) to evaluate inter-method agreement and reliability. Results: Among the three software programs, the Bland–Altman plot showed a substantial bias, the ICC showed a broad range of reliability (0.004–0.97), and repeated-measures ANOVA revealed significant mean volume differences in all brain regions.Similarly, the volume differences of the three software programs had large effect sizes in most regions (0.73–5.51). The effect size was largest in the pallidum in both datasets and smallest in the thalamus and cerebral white matter in the SMC and ADNI datasets, respectively. N% of NQ and DB showed an unacceptably broad Bland–Altman limit of agreement in all brain regions and a very wide range of ICC values (-0.142–0.844) in most brain regions. Conclusion NQ and DB showed significant differences in the measured volume and N%, with limited agreement and reliability for most brain regions. Therefore, users should be aware of the lack of interchangeability between these software programs when they are applied in clinical practice.

Purpose: Mild cognitive impairment (MCI) is a prodromal stage of Alzheimer's disease (AD). Brain atrophy in this disease spectrum begins in the medial temporal lobe structure, which can be recognized by magnetic resonance imaging. To overcome the unsatisfactory inter-observer reliability of visual evaluation, quantitative brain volumetry has been developed and widely investigated for the diagnosis of MCI and AD. The aim of this study was to assess the prediction accuracy of quantitative brain volumetry using a fully automated segmentation software package, NeuroQuant®, for the diagnosis of MCI. Materials and Methods: A total of 418 subjects from the Korean Brain Aging Study for Early Diagnosis and Prediction of Alzheimer’s Disease cohort were included in our study. Each participant was allocated to either a cognitively normal old group (n = 285) or an MCI group (n = 133). Brain volumetric data were obtained from T1-weighted images using the NeuroQuant software package. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed to investigate relevant brain regions and their prediction accuracies. Results: Multivariate logistic regression analysis revealed that normative percentiles of the hippocampus (P < 0.001), amygdala (P = 0.003), frontal lobe (P = 0.049), medial parietal lobe (P = 0.023), and third ventricle (P = 0.012) were independent predictive factors for MCI. In ROC analysis, normative percentiles of the hippocampus and amygdala showed fair accuracies in the diagnosis of MCI (area under the curve: 0.739 and 0.727, respectively). Conclusion Normative percentiles of the hippocampus and amygdala provided by the fully automated segmentation software could be used for screening MCI with a reasonable post-processing time. This information might help us interpret structural MRI in patients with cognitive impairment.

Purpose: Mild cognitive impairment (MCI) is a prodromal stage of Alzheimer's disease (AD). Brain atrophy in this disease spectrum begins in the medial temporal lobe structure, which can be recognized by magnetic resonance imaging. To overcome the unsatisfactory inter-observer reliability of visual evaluation, quantitative brain volumetry has been developed and widely investigated for the diagnosis of MCI and AD. The aim of this study was to assess the prediction accuracy of quantitative brain volumetry using a fully automated segmentation software package, NeuroQuant®, for the diagnosis of MCI. Materials and Methods: A total of 418 subjects from the Korean Brain Aging Study for Early Diagnosis and Prediction of Alzheimer’s Disease cohort were included in our study. Each participant was allocated to either a cognitively normal old group (n = 285) or an MCI group (n = 133). Brain volumetric data were obtained from T1-weighted images using the NeuroQuant software package. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed to investigate relevant brain regions and their prediction accuracies. Results: Multivariate logistic regression analysis revealed that normative percentiles of the hippocampus (P < 0.001), amygdala (P = 0.003), frontal lobe (P = 0.049), medial parietal lobe (P = 0.023), and third ventricle (P = 0.012) were independent predictive factors for MCI. In ROC analysis, normative percentiles of the hippocampus and amygdala showed fair accuracies in the diagnosis of MCI (area under the curve: 0.739 and 0.727, respectively). Conclusion Normative percentiles of the hippocampus and amygdala provided by the fully automated segmentation software could be used for screening MCI with a reasonable post-processing time. This information might help us interpret structural MRI in patients with cognitive impairment.