Enhanced electroencephalography effective connectivity in frontal low-gamma band correlates of emotional regulation after mindfulness training.

Practicing mindfulness, focusing attention on the internal and external experiences occurring in the present moment with open and nonjudgement stance, can lead to the development of emotional regulation skills. Yet, the effective connectivity of brain regions during mindfulness has been largely unexplored. Studies have shown that mindfulness practice promotes functional connectivity in practitioners, potentially due to improved emotional regulation abilities and increased connectivity in the lateral prefrontal areas. To examine the changes in effective connectivity due to mindfulness training, we analyzed electroencephalogram (EEG) signals taken before and after mindfulness training, focusing on training-related effective connectivity changes in the frontal area. The mindfulness training group participated in an 8-week mindfulness-based stress reduction (MBSR) program. The control group did not take part. Regardless of the specific mindfulness practice used, low-gamma band effective connectivity increased globally after the mindfulness training. High-beta band effective connectivity increased globally only during Breathing. Moreover, relatively higher outgoing effective connectivity strength was seen during Resting and Breathing and Body-scan. By analyzing the changes in outgoing and incoming connectivity edges, both F7 and F8 exhibited strong parietal connectivity during Resting and Breathing. Multiple regression analysis revealed that the changes in effective connectivity of the right lateral prefrontal area predicted mindfulness and emotional regulation abilities. These results partially support the theory that the lateral prefrontal areas have top-down modulatory control, as these areas have high outflow effective connectivity, implying that mindfulness training cultivates better emotional regulation.

The impact of patient factors and tumor characteristics on language neuroplasticity in left hemispheric diffuse gliomas prior to surgical resection.

PURPOSE: Language networks are reorganized during glioma growth, leading to varying language performance in patients with gliomas located in or around language-eloquent areas. Therefore, pre-treated language performance reflects the neuroplasticity potential. Different domains of language processing, such as speech expression, repetition, and comprehension, involving different neural networks. We analyzed the effects of patient factors and tumor characteristics on the pre-treated performance to investigate neuroplastic potential of different language domains. METHODS: Patient age, sex, education level, tumor grade, language pathway involvement, T1 contrast enhanced (C+), and FLAIR (T2) volume were selected as variables. The correlation with abnormal language performance was verified using univariate and multivariate logistic regression. RESULTS: In total, 104 left hemispheric glioma patients were enrolled in this study. 44% of patients had repetitive abnormalities, 34.9% had comprehensive abnormalities, and 32.1% had expressive abnormalities. The proportion of normal language performance was 60% in grade 2 and 3 gliomas and 16% in grade 4 gliomas. Tumor grade (p = 0.006) and T2 volume (p = 0.008) were associated with abnormal performance in the expressive domain, education level (p = 0.004) and T1 C+ volume (p = 0.049) in the repetitive domain, and education level (p = 0.013), T2 volume (p = 0.011), and tumor grade (p = 0.089) in the comprehensive domain. CONCLUSION: Different clinical and radiological factors affected the abnormal performance of the three language domains, indicating their functional connectivity and neuroplastic potential are inherently varied. The dynamic interactions between patient factors, tumor characteristics, and language processing should be considered when resecting left hemispheric gliomas.

Cingulate white matter mediates the effects of fecal Ruminococcus on neuropsychiatric symptoms in patients with amyloid-positive amnestic mild cognitive impairment.

BACKGROUND: Microbiota-gut-brain axis interacts with one another to regulate brain functions. However, whether the impacts of gut dysbiosis on limbic white matter (WM) tracts contribute to the neuropsychiatric symptoms (NPS) in patients with amyloid-positive amnestic mild cognitive impairment (aMCI+), have not been explored yet. This study aimed to investigate the mediation effects of limbic WM integrity on the association between gut microbiota and NPS in patients with aMCI+. METHODS: Twenty patients with aMCI + and 20 healthy controls (HCs) were enrolled. All subjects underwent neuropsychological assessments and their microbial compositions were characterized using 16S rRNA Miseq sequencing technique. Amyloid deposition inspected by positron emission tomography imaging and limbic WM tracts (i.e., fornix, cingulum, and uncinate fasciculus) detected by diffusion tensor imaging were additionally measured in patients with aMCI+. We employed a regression-based mediation analysis using Hayes's PROCESS macro in this study. RESULTS: The relative abundance of genera Ruminococcus and Lactococcus was significantly decreased in patients with aMCI + versus HCs. The relative abundance of Ruminococcus was negatively correlated with affective symptom cluster in the aMCI + group. Notably, this association was mediated by WM integrity of the left cingulate gyrus. CONCLUSIONS: Our findings suggest Ruminococcus as a potential target for the management of affective impairments in patients with aMCI+.

Comparison of the effect of 360° versus two-dimensional virtual reality video on history taking and physical examination skills learning among undergraduate medical students: a randomized controlled trial.

Before caring for patients, video instruction is commonly used for undergraduate medical students, and 360° virtual reality (VR) videos have gained increasing interest in clinical medical education. Therefore, the effect of immersive 360° VR video learning compared with two-dimensional (2D) VR video learning in clinical skills acquisition should be evaluated. This randomized, intervention-controlled clinical trial was aimed to assess whether immersive 360° VR video improves undergraduate medical students' learning effectiveness and reduces the cognitive load in history taking and physical examination (H&P) training. From May 1 2018 to October 30 2018, 64 senior undergraduate medical students in a tertiary academic hospital were randomized to receive a 10-min immersive 360° (360° VR video group; n = 32) or 2D VR instructional video (2D VR video group; n = 32), including essential knowledge and competency of H&P. The demographic characteristics of the two groups were comparable for age, sex, and cognitive style. The total procedure skill score, physical examination score, learner's satisfaction score, and total cognitive load in the 360° VR video group were significantly higher than those in the 2D VR video group (effect sizes [95% confidence interval]: 0.72 [0.21-1.22], 0.63 [0.12-1.13], 0.56 [0.06-1.06], and 0.53 [0.03-1.03], respectively). This study suggested that a10-minute 360° VR video instruction helped undergraduate medical students perform fundamental H&P skills as effectively as 2D VR video. Furthermore, the 360° VR video might result in significantly better procedural metrics of physical examinations with higher learner satisfaction despite the higher cognitive load. Supplementary Information: The online version contains supplementary material available at 10.1007/s10055-022-00664-0.

Oral presentation assessment and image reading behaviour on brain computed tomography reading in novice clinical learners: an eye-tracking study.

BACKGROUND: To study whether oral presentation (OP) assessment could reflect the novice learners' interpretation skills and reading behaviour on brain computed tomography (CT) reading. METHODS: Eighty fifth-year medical students were recruited, received a 2-hour interactive workshop on how to read brain CT, and were assigned to read two brain CT images before and after instruction. We evaluated their image reading behaviour in terms of overall OP post-test rating, the lesion identification, and competency in systematic image reading after instruction. Students' reading behaviour in searching for the target lesions were recorded by the eye-tracking technique and were used to validate the accuracy of lesion reports. Statistical analyses, including lag sequential analysis (LSA), linear mixed models, and transition entropy (TE) were conducted to reveal temporal relations and spatial complexity of systematic image reading from the eye movement perspective. RESULTS: The overall OP ratings [pre-test vs. post-test: 0 vs. 1 in case 1, 0 vs. 1 in case 2, p < 0.001] improved after instruction. Both the scores of systematic OP ratings [0 vs.1 in both cases, p < 0.001] and eye-tracking studies (Case 1: 3.42 ± 0.62 and 3.67 ± 0.37 in TE, p = 0.001; Case 2: 3.42 ± 0.76 and 3.75 ± 0.37 in TE, p = 0.002) showed that the image reading behaviour changed before and after instruction. The results of linear mixed models suggested a significant interaction between instruction and area of interests for case 1 (p < 0.001) and case 2 (p = 0.004). Visual attention to the target lesions in the case 1 assessed by dwell time were 506.50 ± 509.06 and 374.38 ± 464.68 milliseconds before and after instruction (p = 0.02). However, the dwell times in the case 2, the fixation counts and the frequencies of accurate lesion diagnoses in both cases did not change after instruction. CONCLUSION: Our results showed OP performance may change concurrently with the medical students' reading behaviour on brain CT after a structured instruction.

Mild simulator sickness can alter heart rate variability, mental workload, and learning outcomes in a 360° virtual reality application for medical education: a post hoc analysis of a randomized controlled trial.

Virtual reality (VR) applications could be beneficial for education, training, and treatment. However, VR may induce symptoms of simulator sickness (SS) such as difficulty focusing, difficulty concentrating, or dizziness that could impair autonomic nervous system function, affect mental workload, and worsen interventional outcomes. In the original randomized controlled trial, which explored the effectiveness of using a 360° VR video versus a two-dimensional VR video to learn history taking and physical examination skills, only the former group participants had SS. Therefore, 28 undergraduate medical students who participated in a 360° VR learning module were included in this post hoc study using a repeated measures design. Data of the Simulator Sickness Questionnaire (SSQ), heart rate variability (HRV) analysis, Task Load Index, and Mini-Clinical Evaluation Exercise were retrospectively reviewed and statistically analyzed. Ten (36%) participants had mild SS (total score > 0 and ≤ 20), and 18 (64%) had no SS symptom. Total SSQ score was positively related to the very low frequency (VLF) band power, physical demand subscale, and frustration subscale, and inversely related to physical examination score. Using multilevel modeling, the VLF power mediated the relationship between total SSQ score and physical examination score. Furthermore, frustration subscale moderated the mediating effects of the VLF power. Our results highlight the importance of documenting SS to evaluate a 360° VR training program. Furthermore, the combination of HRV analysis with mental workload measurement and outcome assessments provided the important clinical value in evaluating the effects of SS in VR applications in medical education.

Mindfulness-based cognitive therapy on bereavement grief: Alterations of resting-state network connectivity associate with changes of anxiety and mindfulness.

Bereavement, the experience of losing a loved one, is one of the most catastrophic but inevitable events in life. It causes grief and intense depression-like sadness. Recent studies have revealed the effectiveness and proficiency of mindfulness-based cognitive therapy (MBCT) in emotional regulation among bereavement populations. MBCT improves the well-being of the bereaved by enhancing cognitive performances. Regarding the neural correlates of bereavement grief, previous studies focused on the alleviation of emotion-cognition interferences at specific brain regions. Here, we hypothesized that the bereavement grief fundamentally triggers global alterations in the resting-state brain networks and part of the internetwork connectivity could be reformed after MBCT intervention. We recruited 19 bereaved individuals who participated the 8-week MBCT program. We evaluated (a) the large-scale changes in brain connectivity affected by the MBCT program; as well as (b) the association between connectivity changes and self-rated questionnaire. First, after MBCT, the bereaved individuals showed the reduction of the internetwork connectivity in the salience, default-mode and fronto-parietal networks in the resting state but not under emotional arousal, implying the alleviated attention to spontaneous mind wandering after MBCT. Second, the alterations of functional connectivity between subcortical (e.g., caudate) and cortical networks (e.g., cingulo-opercular/sensorimotor) were associated with the changes of the mindfulness scale, the anxiety and the emotion regulation ability. In summary, MBCT could enhance spontaneous emotion regulation among the bereaved individuals through the internetwork reorganizations in the resting state.

Pupil Size Prediction Techniques Based on Convolution Neural Network.

The size of one's pupil can indicate one's physical condition and mental state. When we search related papers about AI and the pupil, most studies focused on eye-tracking. This paper proposes an algorithm that can calculate pupil size based on a convolution neural network (CNN). Usually, the shape of the pupil is not round, and 50% of pupils can be calculated using ellipses as the best fitting shapes. This paper uses the major and minor axes of an ellipse to represent the size of pupils and uses the two parameters as the output of the network. Regarding the input of the network, the dataset is in video format (continuous frames). Taking each frame from the videos and using these to train the CNN model may cause overfitting since the images are too similar. This study used data augmentation and calculated the structural similarity to ensure that the images had a certain degree of difference to avoid this problem. For optimizing the network structure, this study compared the mean error with changes in the depth of the network and the field of view (FOV) of the convolution filter. The result shows that both deepening the network and widening the FOV of the convolution filter can reduce the mean error. According to the results, the mean error of the pupil length is 5.437% and the pupil area is 10.57%. It can operate in low-cost mobile embedded systems at 35 frames per second, demonstrating that low-cost designs can be used for pupil size prediction.

Clinical Value of Information Entropy Compared with Deep Learning for Ultrasound Grading of Hepatic Steatosis.

Entropy is a quantitative measure of signal uncertainty and has been widely applied to ultrasound tissue characterization. Ultrasound assessment of hepatic steatosis typically involves a backscattered statistical analysis of signals based on information entropy. Deep learning extracts features for classification without any physical assumptions or considerations in acoustics. In this study, we assessed clinical values of information entropy and deep learning in the grading of hepatic steatosis. A total of 205 participants underwent ultrasound examinations. The image raw data were used for Shannon entropy imaging and for training and testing by the pretrained VGG-16 model, which has been employed for medical data analysis. The entropy imaging and VGG-16 model predictions were compared with histological examinations. The diagnostic performances in grading hepatic steatosis were evaluated using receiver operating characteristic (ROC) curve analysis and the DeLong test. The areas under the ROC curves when using the VGG-16 model to grade mild, moderate, and severe hepatic steatosis were 0.71, 0.75, and 0.88, respectively; those for entropy imaging were 0.68, 0.85, and 0.9, respectively. Ultrasound entropy, which varies with fatty infiltration in the liver, outperformed VGG-16 in identifying participants with moderate or severe hepatic steatosis (p < 0.05). The results indicated that physics-based information entropy for backscattering statistics analysis can be recommended for ultrasound diagnosis of hepatic steatosis, providing not only improved performance in grading but also clinical interpretations of hepatic steatosis.

Intraoperative linguistic performance during awake brain surgery predicts postoperative linguistic deficits.

INTRODUCTION: Awake craniotomy pursues a balance between extensive tumor resection and preservation of postoperative language function. A dilemma exists in patients whose tumor resection is restricted due to signs of language impairment observed during awake craniotomy. In order to determine the degree to which recovery of language function caused by tumor resection can be achieved by spontaneous neuroplasticity, the change in postoperative language function was compared to quantified intraoperative linguistic performance. METHODS: The modified, short-form Boston Diagnostic Aphasia Examination (sfBDAE) was used to assess pre- and postoperative language functions; visual object naming (DO 80) and semantic-association (Pyramid and Palm Tree Test, PPTT) tests assessed intraoperative linguistic performance. DO 80 and PPTT were performed alternatively during subcortical functional monitoring while performing tumor resection and sfBDAE was assessed 1-week postoperatively. RESULTS: Most patients with observed language impairment during awake surgery showed improved language function postoperatively. Both intraoperative DO 80 and PPTT showed significant correlation to postoperative sfBDAE domain scores (p < 0.05), with a higher correlation observed with PPTT. A linear regression model showed that only PPTT predicted the postoperative sfBDAE domain scores with the adjusted R2 ranging from 0.51 to 0.89 (all p < 0.01). Receiver operating characteristic analysis showed a cutoff value of PPTT that yielded a sensitivity of 80% and specificity of 100%. CONCLUSION: PPTT may be a feasible tool for intraoperative linguistic evaluation that can predict postoperative language outcomes. Further studies are needed to determine the extent of tumor resection that optimizes the postoperative language following neuroplasticity.

Cognitive Style and Mobile E-Learning in Emergent Otorhinolaryngology-Head and Neck Surgery Disorders for Millennial Undergraduate Medical Students: Randomized Controlled Trial.

BACKGROUND: Electronic learning (e-learning) through mobile technology represents a novel way to teach emergent otorhinolaryngology-head and neck surgery (ORL-HNS) disorders to undergraduate medical students. Whether a cognitive style of education combined with learning modules can impact learning outcomes and satisfaction in millennial medical students is unknown. OBJECTIVE: The aim of this study was to assess the impact of cognitive styles and learning modules using mobile e-learning on knowledge gain, competence gain, and satisfaction for emergent ORL-HNS disorders. METHODS: This randomized controlled trial included 60 undergraduate medical students who were novices in ORL-HNS at an academic teaching hospital. The cognitive style of the participants was assessed using the group embedded figures test. The students were randomly assigned (1:1) to a novel interactive multimedia (IM) group and conventional Microsoft PowerPoint show (PPS) group matched by age, sex, and cognitive style. The content for the gamified IM module was derived from and corresponded to the textbook-based learning material of the PPS module (video lectures). The participants were unblinded and used fully automated courseware containing the IM or PPS module on a 7-inch tablet for 100 min. Knowledge and competence were assessed using multiple-choice questions and multimedia situation tests, respectively. Each participant also rated their global satisfaction. RESULTS: All of the participants (median age 23 years, range 22-26 years; 36 males and 24 females) received the intended intervention after randomization. Overall, the participants had significant gains in knowledge (median 50%, interquartile range [IQR]=17%-80%, P<.001) and competence (median 13%, IQR=0%-33%, P=.006). There were no significant differences in knowledge gain (40%, IQR=13%-76% vs 60%, IQR=20%-100%, P=.42) and competence gain (0%, IQR= -21% to 38% vs 25%, IQR=0%-33%, P=.16) between the IM and PPS groups. However, the IM group had a higher satisfaction score (8, IQR=6-9 vs 6, IQR=4-7, P=.01) compared with the PPS group. Using Friedman's two-way nonparametric analysis of variance, cognitive styles (field-independent, field-intermediate, or field-dependent classification) and learning modules (IM or PPS) had significant effects on both knowledge gain (both adjusted P<.001) and satisfaction (both adjusted P<.001). CONCLUSIONS: Mobile e-learning is an effective modality to improve knowledge of emergent ORL-HNS in millennial undergraduate medical students. Our findings suggest the necessity of developing various modules for undergraduate medical students with different cognitive styles. TRIAL REGISTRATION: Clinicaltrials.gov NCT02971735; https://clinicaltrials.gov/ct2/show/NCT02971735 (Archived by WebCite at http://www.webcitation.org/6waoOpCEV).

The Influence of Dynamic Taping on Landing Biomechanics after Fatigue in Young Football Athletes: A Randomized, Sham-Controlled Crossover Trial.

Fatigue is believed to increase the risk of anterior cruciate ligament (ACL) injury by directly promoting high-risk biomechanics in the lower limbs. Studies have shown that dynamic taping can help normalize inadequate biomechanics during landings. This study aims to examine the effects of dynamic taping on landing biomechanics in fatigued football athletes. Twenty-seven high-school football athletes were recruited and randomly allocated to groups of either active taping or sham taping, with a crossover allocation two weeks later. In each group, the participants underwent a functional agility short-term fatigue protocol and were evaluated using the landing error scoring system before and after the fatigue protocol. The landing error scoring system (LESS) scores in the sham taping group increased from 4.24 ± 1.83 to 5.36 ± 2.00 (t = -2.07, p = 0.04, effect size = 0.61). In contrast, the pre-post difference did not reach statistical significance in the active taping group (from 4.24 ± 1.69 to 4.52 ± 1.69, t = -1.50, p = 0.15, effect size 0.46). Furthermore, the pre-post changes between the sham and active taping groups were statistically significant (sham taping: 1.12 ± 1.20; active taping: 0.28 ± 0.94, p = 0.007). Dynamic taping, particularly using the spiral technique, appeared to mitigate faulty landing biomechanics in the fatigued athletes by reducing hip and knee flexion and increasing hip internal rotation during landing. These results suggest that dynamic taping can potentially offer protective benefits in landing mechanics, which could further be applied to prevent ACL injuries in fatigued athletes.

Development of a Dementia Case Management Information System App: Mixed Methods Study.

BACKGROUND: Case managers for persons with dementia not only coordinate patient care but also provide family caregivers with educational material and available support services. Taiwan uses a government-based information system for monitoring the provision of health care services. Unfortunately, scheduling patient care and providing information to family caregivers continues to be paper-based, which results in a duplication of patient assessments, complicates scheduling of follow-ups, and hinders communication with caregivers, which limits the ability of case managers to provide cohesive, quality care. OBJECTIVE: This multiphase study aimed to develop an electronic information system for dementia care case managers based on their perceived case management needs and what they would like included in an electronic health care app. METHODS: Case managers were recruited to participate (N=63) by purposive sampling from 28 facilities representing two types of community-based dementia care centers in Taiwan. A dementia case management information system (DCMIS) app was developed in four phases. Phase 1 assessed what should be included in the app by analyzing qualitative face-to-face or internet-based interviews with 33 case managers. Phase 2 formulated a framework for the app to support case managers based on key categories identified in phase 1. During phase 3, a multidisciplinary team of information technology engineers and dementia care experts developed the DCMIS app: hardware and software components were selected, including platforms for messaging, data management, and security. The app was designed to eventually interface with a family caregiver app. Phase 4 involved pilot-testing the DCMIS app with a second group of managers (n=30); feedback was provided via face-to-face interviews about their user experience. RESULTS: Findings from interviews in phase 1 indicated the DCMIS framework should include unified databases for patient reminder follow-up scheduling, support services, a health education module, and shared recordkeeping to facilitate teamwork, networking, and communication. The DCMIS app was built on the LINE (LY Corporation) messaging platform, which is the mobile app most widely used in Taiwan. An open-source database management system allows secure entry and storage of user information and patient data. Case managers had easy access to educational materials on dementia and caregiving for persons living with dementia that could be provided to caregivers. Interviews with case managers following pilot testing indicated that the DCMIS app facilitated the completion of tasks and management responsibilities. Some case managers thought it would be helpful to have a DCMIS desktop computer system rather than a mobile app. CONCLUSIONS: Based on pilot testing, the DCMIS app could reduce the growing challenges of high caseloads faced by case managers of persons with dementia, which could improve continuity of care. These findings will serve as a reference when the system is fully developed and integrated with the electronic health care system in Taiwan.

Classification of mindfulness experiences from gamma-band effective connectivity: Application of machine-learning algorithms on resting, breathing, and body scan.

BACKGROUND AND OBJECTIVE: Practicing mindfulness is a mental process toward interoceptive awareness, achieving stress reduction and emotion regulation through brain-function alteration. Literature has shown that electroencephalography (EEG)-derived connectivity possesses the potential to differentiate brain functions between mindfulness naïve and mindfulness experienced, where such quantitative differentiation could benefit telediagnosis for mental health. However, there is no prior guidance in model selection targeting on the mindfulness-experience prediction. Here we hypothesized that the EEG effective connectivity could reach a good prediction performance in mindfulness experiences with brain interpretability. METHODS: We aimed at probing direct Directed Transfer Function (dDTF) to classify the participants' history of mindfulness-based stress reduction (MBSR), and aimed at optimizing the prediction accuracy by comparing multiple machine learning (ML) algorithms. Targeting the gamma-band effective connectivity, we evaluated the EEG-based prediction of the mindfulness experiences across 7 machine learning (ML) algorithms and 3 sessions (i.e., resting, focus-breathing, and body-scan). RESULTS: The support vector machine and naïve Bayes classifiers exhibited significant accuracies above the chance level across all three sessions, and the decision tree algorithm reached the highest prediction accuracy of 91.7 % with the resting state, compared to the classification accuracies with the other two mindful states. We further conducted the analysis on essential EEG channels to preserve the classification accuracy, revealing that preserving just four channels (F7, F8, T7, and P7) out of 19 yielded the accuracy of 83.3 %. Delving into the contribution of connectivity features, specific connectivity features predominantly located in the frontal lobe contributed more to classifier construction, which aligned well with the existing mindfulness literature. CONCLUSION: In the present study, we initiated a milestone of developing an EEG-based classifier to detect a person's mindfulness experience objectively. The prediction accuracy of the decision tree was optimal to differentiate the mindfulness experiences using the local resting-state EEG data. The suggested algorithm and key channels on the mindfulness-experience prediction may provide guidance for predicting mindfulness experiences using the EEG-based classification embedded in future wearable neurofeedback systems or plausible digital therapeutics.

Lack of effects of eight-week left dorsolateral prefrontal theta burst stimulation on white matter macro/microstructure and connection in autism.

Whether brain stimulation could modulate brain structure in autism remains unknown. This study explored the impact of continuous theta burst stimulation (cTBS) over the left dorsolateral prefrontal cortex (DLPFC) on white matter macro/microstructure in intellectually able children and emerging adults with autism. Sixty autistic participants were randomized (30 active) and received active or sham cTBS for eight weeks twice per week, 16 total sessions using a double-blind (participant-, rater-, analyst-blinded) design. All participants received high-angular resolution diffusion MR imaging at baseline and week 8. Twenty-eight participants in the active group and twenty-seven in the sham group with good imaging quality entered the final analysis. With longitudinal fixel-based analysis and network-based statistics, we found no significant difference between the active and sham groups in changes of white matter macro/microstructure and connections following cTBS. In addition, we found no association between baseline white matter macro/microstructure and autistic symptom changes from baseline to week 8 in the active group. In conclusion, we did not find a significant impact of left DLPFC cTBS on white matter macro/microstructure and connections in children and emerging adults with autism. These findings need to be interpreted in the context that the current intellectually able cohort in a single university hospital site limits the generalizability. Future studies are required to investigate if higher stimulation intensities and/or doses, other personal factors, or rTMS parameters might confer significant brain structural changes visible on MRI in ASD.

Yearly change in air pollution and brain aging among older adults: A community-based study in Taiwan.

BACKGROUND: Air pollution is recognized as a modifiable risk factor for dementia, and recent evidence suggests that improving air quality could attenuate cognitive decline and reduce dementia risk. However, studies have yet to explore the effects of improved air quality on brain structures. This study aims to investigate the impact of air pollution reduction on cognitive functions and structural brain differences among cognitively normal older adults. METHODS: Four hundred and thirty-one cognitively normal older adults were from the Epidemiology of Mild Cognitive Impairment study in Taiwan (EMCIT), a community-based cohort of adults aged 60 and older, between year 2017- 2021. Annual concentrations of PM2.5, NO2, O3, and PM10 at participants' residential addresses during the 10 years before enrollment were estimated using ensemble mixed spatial models. The yearly rate of change (slope) in air pollutants was estimated for each participant. Cognitive functions and structural brain images were collected during enrollment. The relationships between the rate of air pollution change and cognitive functions were examined using linear regression models. For air pollutants with significant findings in relation to cognitive function, we further explored the association with brain structure. RESULTS: Overall, all pollutant concentrations, except O3, decreased over the 10-year period. The yearly rates of change (slopes) in PM2.5 and NO2 were correlated with better attention (PM2.5: r = -0.1, p = 0.047; NO2: r = -0.1, p = 0.03) and higher white matter integrity in several brain regions. These regions included anterior thalamic radiation, superior longitudinal fasciculus, inferior longitudinal fasciculus, corticospinal tract, and inferior fronto-occipital fasciculus. CONCLUSIONS: Greater rate of reduction in air pollution was associated with better attention and attention-related white matter integrity. These results provide insight into the mechanism underlying the relationship between air pollution, brain health, and cognitive aging among older adults.

Differences in gray matter volumes of subcortical nuclei between major depressive disorder with and without persistent depressive disorder.

OBJECTIVE: This study aimed to compare the differences in gray matter volumes (GMVs) of subcortical nuclei between major depressive disorder (MDD) patients with and without persistent depressive disorder (PDD) at long-term follow-up. METHODS: 114 and 94 subjects with MDD, including 48 and 41 with comorbid PDD, were enrolled to undergo high-resolution T1-weighted imaging at first (FIP) and second (three years later, SIP) investigation points, respectively. FreeSurfer was used to extract the GMVs of seven subcortical nuclei, and Generalized Estimating Equation models were employed to estimate the differences in GMVs of subcortical nuclei between the two subgroups. RESULTS: The PDD subgroup had a significantly greater depressive severity and a higher percentage of patients undergoing pharmacotherapy at the FIP as compared with the non-PDD subgroup. These differences became insignificant at the SIP. The PDD subgroup had a significantly (p < 0.003) smaller GMV in the right putamen at the SIP and in the right nucleus accumbens (NAc) at the FIP and SIP as compared with the non-PDD subgroup. After controlling for clinical variables, PDD was independently associated with smaller GMVs in the right putamen and NAc. LIMITATIONS: Imaging was not performed at baseline and pharmacotherapy was not controlled at the FIP and SIP. CONCLUSIONS: MDD with PDD was associated with smaller GMVs in the right putamen and NAc as compared with MDD without PDD. Whether the two regions are biomarkers related to a poor prognosis and the chronicity of depression requires further study.

Subjective cognitive decline exhibits alterations of resting-state phase-amplitude coupling in precuneus.

OBJECTIVE: Subjective cognitive decline (SCD) is associated with increased risks for progressing to Alzheimer's disease (AD). This study aimed to investigate phase-amplitude coupling (PAC) in individuals with SCD and healthy controls (HCs) in the baseline year and determined the predictability of cognitive changes in the clinical follow-up. METHODS: Resting-state magnetoencephalographic signals in 29 HCs and 23 SCD subjects were recorded in the baseline year. The parahippocampal gyrus, posterior cingulate cortex and precuneus were selected as regions of interest (ROIs). Based on the grand-averaged comodulograms, delta-beta, delta-gamma and theta-gamma PAC values were extracted from each ROI. RESULTS: Compared with the HCs, the SCD group showed decreased theta-gamma PAC in the precuneus. Theta-gamma PAC of the left precuneus was associated with SCD severity and performance of immediate recall in the baseline year. The SCD group was followed for 3 years and divided into SCD-Stable and SCD-Decline groups based on scores of Mini-Mental State Examination. No significant differences in PAC of the baseline year were found between SCD-Stable and SCD-Decline groups. CONCLUSIONS: The SCD group demonstrated reduced theta-gamma PAC in the precuneus. SIGNIFICANCE: Subjective perception of cognitive decline is reflected by objective alterations of brain function.

Improving the brain image resolution of generalized q-sampling MRI revealed by a three-dimensional CNN-based method.

Background: Understanding neural connections facilitates the neuroscience and cognitive behavioral research. There are many nerve fiber intersections in the brain that need to be observed, and the size is between 30 and 50 nanometers. Improving image resolution has become an important issue for mapping the neural connections non-invasively. Generalized q-sampling imaging (GQI) was used to reveal the fiber geometry of straight and crossing. In this work, we attempted to achieve super-resolution with a deep learning method on diffusion weighted imaging (DWI). Materials and methods: A three-dimensional super-resolution convolutional neural network (3D SRCNN) was utilized to achieve super-resolution on DWI. Then, generalized fractional anisotropy (GFA), normalized quantitative anisotropy (NQA), and the isotropic value of the orientation distribution function (ISO) mapping were reconstructed using GQI with super-resolution DWI. We also reconstructed the orientation distribution function (ODF) of brain fibers using GQI. Results: With the proposed super-resolution method, the reconstructed DWI was closer to the target image than the interpolation method. The peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) were also significantly improved. The diffusion index mapping reconstructed by GQI also had higher performance. The ventricles and white matter regions were much clearer. Conclusion: This super-resolution method can assist in postprocessing low-resolution images. With SRCNN, high-resolution images can be effectively and accurately generated. The method can clearly reconstruct the intersection structure in the brain connectome and has the potential to accurately describe the fiber geometry on a subvoxel scale.

Altered gut microbiota in older adults with mild cognitive impairment: a case-control study.

Introduction: The microbiota-gut-brain axis is implicated in Alzheimer's disease. Gut microbiota alterations in mild cognitive impairment (MCI) are inconsistent and remain to be understood. This study aims to investigate the gut microbial composition associated with MCI, cognitive functions, and structural brain differences. Methods: A nested case-control study was conducted in a community-based prospective cohort where detailed cognitive functions and structural brain images were collected. Thirty-one individuals with MCI were matched to sixty-five cognitively normal controls by age strata, gender, and urban/rural area. Fecal samples were examined using 16S ribosomal RNA (rRNA) V3-V4 sequencing. Compositional differences between the two groups were identified and correlated with the cognitive functions and volumes/thickness of brain structures. Results: There was no significant difference in alpha and beta diversity between MCIs and cognitively normal older adults. However, the abundance of the genus Ruminococcus, Butyricimonas, and Oxalobacter decreased in MCI patients, while an increased abundance of nine other genera, such as Flavonifractor, were found in MCIs. Altered genera discriminated MCI patients well from controls (AUC = 84.0%) and were associated with attention and executive function. Conclusion: This study provides insights into the role of gut microbiota in the neurodegenerative process.