Efficacy of personalized repetitive transcranial magnetic stimulation based on functional reserve to enhance ambulatory function in patients with Parkinson's disease: study protocol for a randomized controlled trial.

BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) is one of the non-invasive brain stimulations that modulate cortical excitability through magnetic pulses. However, the effects of rTMS on Parkinson's disease (PD) have yielded mixed results, influenced by factors including various rTMS stimulation parameters as well as the clinical characteristics of patients with PD. There is no clear evidence regarding which patients should be applied with which parameters of rTMS. The study aims to investigate the efficacy and safety of personalized rTMS in patients with PD, focusing on individual functional reserves to improve ambulatory function. METHODS: This is a prospective, exploratory, multi-center, single-blind, parallel-group, randomized controlled trial. Sixty patients with PD will be recruited for this study. This study comprises two sub-studies, each structured as a two-arm trial. Participants are classified into sub-studies based on their functional reserves for ambulatory function, into either the motor or cognitive priority group. The Timed-Up and Go (TUG) test is employed under both single and cognitive dual-task conditions (serial 3 subtraction). The motor dual-task effect, using stride length, and the cognitive dual-task effect, using the correct response rate of subtraction, are calculated. In the motor priority group, high-frequency rTMS targets the primary motor cortex of the lower limb, whereas the cognitive priority group receives rTMS over the left dorsolateral prefrontal cortex. The active comparator for each sub-study is bilateral rTMS of the primary motor cortex of the upper limb. Over 4 weeks, the participants will undergo 10 rTMS sessions, with evaluations conducted pre-intervention, mid-intervention, immediately post-intervention, and at 2-month follow-up. The primary outcome is a change in TUG time between the pre- and immediate post-intervention evaluations. The secondary outcome variables are the TUG under cognitive dual-task conditions, Movement Disorder Society-Unified Parkinson's Disease Rating Scale Part III, New Freezing of Gait Questionnaire, Digit Span, trail-making test, transcranial magnetic stimulation-induced motor-evoked potentials, diffusion tensor imaging, and resting state functional magnetic resonance imaging. DISCUSSION: The study will reveal the effect of personalized rTMS based on functional reserve compared to the conventional rTMS approach in PD. Furthermore, the findings of this study may provide empirical evidence for an rTMS protocol tailored to individual functional reserves to enhance ambulatory function in patients with PD. TRIAL REGISTRATION: ClinicalTrials.gov NCT06350617. Registered on 5 April 2024.

Inhalation Toxicity Screening of Consumer Products Chemicals using OECD Test Guideline Data-based Machine Learning Models.

This study aimed to screen the inhalation toxicity of chemicals found in consumer products such as air fresheners, fragrances, and anti-fogging agents submitted to K-REACH using machine learning models. We manually curated inhalation toxicity data based on OECD test guideline 403 (Acute inhalation), 412 (Sub-acute inhalation), and 413 (Sub-chronic inhalation) for 1709 chemicals from the OECD eChemPortal database. Machine learning models were trained using ten algorithms, along with four molecular fingerprints (MACCS, Morgan, Topo, RDKit) and molecular descriptors, achieving F1 scores ranging from 51 % to 91 % in test dataset. Leveraging the high-performing models, we conducted a virtual screening of chemicals, initially applying them to data-rich chemicals generally used in occupational settings to determine the prediction uncertainty. Results showed high sensitivity (75 %) but low specificity (23 %), suggesting that our models can contribute to conservative screening of chemicals. Subsequently, we applied the models to consumer product chemicals, identifying 79 as of high concern. Most of the prioritized chemicals lacked GHS classifications related to inhalation toxicity, even though they were predicted to be used in many consumer products. This study highlights a potential regulatory blind spot concerning the inhalation risk of consumer product chemicals while also indicating the potential of artificial intelligence (AI) models to aid in prioritizing chemicals at the screening level.

Effects of transcranial direct current stimulation on cognition in MCI with Alzheimer's disease risk factors using Bayesian analysis.

Despite the growing interest in precision medicine-based therapies for Alzheimer's disease (AD), little research has been conducted on how individual AD risk factors influence changes in cognitive function following transcranial direct current stimulation (tDCS). This study evaluates the cognitive effects of sequential tDCS on 63 mild cognitive impairment (MCI) patients, considering AD risk factors such as amyloid-beta deposition, APOE ε4, BDNF polymorphism, and sex. Using both frequentist and Bayesian methods, we assessed the interaction of tDCS with these risk factors on cognitive performance. Notably, we found that amyloid-beta deposition significantly interacted with tDCS in improving executive function, specifically Stroop Word-Color scores, with strong Bayesian support for this finding. Memory enhancements were differentially influenced by BDNF Met carrier status. However, sex and APOE ε4 status did not show significant effects. Our results highlight the importance of individual AD risk factors in modulating cognitive outcomes from tDCS, suggesting that precision medicine may offer more effective tDCS treatments tailored to individual risk profiles in early AD stages.

Efficacy of personalized rTMS to enhance upper limb function in subacute stroke patients: a protocol for a multi-center, randomized controlled study.

Background: Repetitive transcranial magnetic stimulation (rTMS) is widely used therapy to enhance motor deficit in stroke patients. To date, rTMS protocols used in stroke patients are relatively unified. However, as the pathophysiology of stroke is diverse and individual functional deficits are distinctive, more precise application of rTMS is warranted. Therefore, the objective of this study was to determine the effects of personalized protocols of rTMS therapy based on the functional reserve of each stroke patient in subacute phase. Methods: This study will recruit 120 patients with stroke in subacute phase suffering from the upper extremity motor impairment, from five different hospitals in Korea. The participants will be allocated into three different study conditions based on the functional reserve of each participant, measured by the results of TMS-induced motor evoked potentials (MEPs), and brain MRI with diffusion tensor imaging (DTI) evaluations. The participants of the intervention-group in the three study conditions will receive different protocols of rTMS intervention, a total of 10 sessions for 2 weeks: high-frequency rTMS on ipsilesional primary motor cortex (M1), high-frequency rTMS on ipsilesional ventral premotor cortex, and high-frequency rTMS on contralesional M1. The participants of the control-group in all three study conditions will receive the same rTMS protocol: low-frequency rTMS on contralesional M1. For outcome measures, the following assessments will be performed at baseline (T0), during-intervention (T1), post-intervention (T2), and follow-up (T3) periods: Fugl-Meyer Assessment (FMA), Box-and-block test, Action Research Arm Test, Jebsen-Taylor hand function test, hand grip strength, Functional Ambulatory Category, fractional anisotropy measured by the DTI, and brain network connectivity obtained from MRI. The primary outcome will be the difference of upper limb function, as measured by FMA from T0 to T2. The secondary outcomes will be the differences of other assessments. Discussion: This study will determine the effects of applying different protocols of rTMS therapy based on the functional reserve of each patient. In addition, this methodology may prove to be more efficient than conventional rTMS protocols. Therefore, effective personalized application of rTMS to stroke patients can be achieved based on their severity, predicted mechanism of motor recovery, or functional reserves. Clinical trial registration: https://clinicaltrials.gov/, identifier NCT06270238.

A Comparative Analysis of Two Automated Quantification Methods for Regional Cerebral Amyloid Retention: PET-Only and PET-and-MRI-Based Methods.

Accurate quantification of amyloid positron emission tomography (PET) is essential for early detection of and intervention in Alzheimer's disease (AD) but there is still a lack of studies comparing the performance of various automated methods. This study compared the PET-only method and PET-and-MRI-based method with a pre-trained deep learning segmentation model. A large sample of 1180 participants in the Catholic Aging Brain Imaging (CABI) database was analyzed to calculate the regional standardized uptake value ratio (SUVR) using both methods. The logistic regression models were employed to assess the discriminability of amyloid-positive and negative groups through 10-fold cross-validation and area under the receiver operating characteristics (AUROC) metrics. The two methods showed a high correlation in calculating SUVRs but the PET-MRI method, incorporating MRI data for anatomical accuracy, demonstrated superior performance in predicting amyloid-positivity. The parietal, frontal, and cingulate importantly contributed to the prediction. The PET-MRI method with a pre-trained deep learning model approach provides an efficient and precise method for earlier diagnosis and intervention in the AD continuum.

Development of a prediction model for cognitive impairment of sarcopenia using multimodal neuroimaging in non-demented older adults.

INTRODUCTION: Despite prior research on the association between sarcopenia and cognitive impairment in the elderly, a comprehensive model that integrates various brain pathologies is still lacking. METHODS: We used data from 528 non-demented older adults with or without sarcopenia in the Catholic Aging Brain Imaging (CABI) database, containing magnetic resonance imaging scans, positron emission tomography scans, and clinical data. We also measured three key components of sarcopenia: skeletal muscle index (SMI), hand grip strength (HGS), and the five times sit-to-stand test (5STS). RESULTS: All components of sarcopenia were significantly correlated with global cognitive function, but cortical thickness and amyloid-beta (Aß) retention had distinctive relationships with each measure. In the path model, brain atrophy resulting in cognitive impairment was mediated by Aß retention for SMI and periventricular white matter hyperintensity for HGS, but directly affected by the 5STS. DISCUSSION: Treatments targeting each sub-domain of sarcopenia should be considered to prevent cognitive decline. HIGHLIGHTS: We identified distinct impacts of three sarcopenia measures on brain structure and Aß. Muscle mass is mainly associated with Aß and has an influence on the brain atrophy. Muscle strength linked with periventricular WMH and brain atrophy. Muscle function associated with cortical thinning in specific brain regions. Interventions on sarcopenia may be important to ease cognitive decline in the elderly.

Strategies to Enrich Electrochemical Sensing Data with Analytical Relevance for Machine Learning Applications: A Focused Review.

In this review, recent advances regarding the integration of machine learning into electrochemical analysis are overviewed, focusing on the strategies to increase the analytical context of electrochemical data for enhanced machine learning applications. While information-rich electrochemical data offer great potential for machine learning applications, limitations arise when sensors struggle to identify or quantitatively detect target substances in a complex matrix of non-target substances. Advanced machine learning techniques are crucial, but equally important is the development of methods to ensure that electrochemical systems can generate data with reasonable variations across different targets or the different concentrations of a single target. We discuss five strategies developed for building such electrochemical systems, employed in the steps of preparing sensing electrodes, recording signals, and analyzing data. In addition, we explore approaches for acquiring and augmenting the datasets used to train and validate machine learning models. Through these insights, we aim to inspire researchers to fully leverage the potential of machine learning in electroanalytical science.

Automated Scoring of Alzheimer's Disease Atrophy Scale with Subtype Classification Using Deep Learning-Based T1-Weighted Magnetic Resonance Image Segmentation.

Background: Application of visual scoring scales for regional atrophy in Alzheimer's disease (AD) in clinical settings is limited by their high time cost and low intra/inter-rater agreement. Objective: To provide automated atrophy scoring using objective volume driven from deep-learning segmentation methods for AD subtype classification using magnetic resonance imaging (MRI). Methods: We enrolled 3,959 participants (1,732 cognitively normal [CN], 1594 with mild cognitive impairment [MCI], and 633 with AD). The occupancy indices for each regional volume were calculated by dividing each volume by the size of the lateral and inferior ventricular volumes. MR images from 355 participants (119 CN, 119 MCI, and 117 AD) from three different centers were used for validation. Two neuroradiologists performed visual assessments of the medial temporal, posterior, and global cortical atrophy scores in the frontal lobe using T1-weighted MR images. Images were also analyzed using the deep learning-based segmentation software, Neurophet AQUA. Cutoff values for the three scores were determined using the data distribution according to age. The scoring results were compared for consistency and reliability. Results: Four volumetric-driven scoring results showed a high correlation with the visual scoring results for AD, MCI, and CN. The overall agreement with human raters was weak-to-moderate for atrophy scoring in CN participants, and good-to-almost perfect in AD and MCI participants. AD subtyping by automated scores also showed usefulness as a research tool. Conclusions: Determining AD subtypes using automated atrophy scoring for late-MCI and AD could be useful in clinical settings or multicenter studies with large datasets.

The Effects of Microbial Additive Supplementation on Growth Performance, Blood Metabolites, Fecal Microflora, and Carcass Characteristics of Growing-Finishing Pigs.

This study aimed to assess the effects of microbial additives that produce antimicrobial and digestive enzymes on the growth performance, blood metabolites, fecal microflora, and carcass characteristics of growing-finishing pigs. A total of 180 growing-finishing pigs (Landrace × Yorkshire × Duroc; mixed sex; 14 weeks of age; 58.0 ± 1.00 kg) were then assigned to one of three groups with three repetitions (20 pigs) per treatment for 60 days of adaptation and 7 days of collection. Dietary treatments included 0, 0.5, and 1.0% microbial additives in the basal diet. For growth performance, no significant differences in the initial and final weights were observed among the dietary microbial additive treatments, except for the average daily feed intake, average daily gain, and feed efficiency. In terms of blood metabolites and fecal microflora, immunoglobulin G (IgG), blood urea nitrogen, blood glucose, and fecal lactic acid bacteria count increased linearly, and fecal E. coli counts decreased linearly with increasing levels of microbial additives but not growth hormones and Salmonella. Carcass quality grade was improved by the microbial additive. In addition, carcass characteristics were not influenced by dietary microbial additives. In conclusion, dietary supplementation with 1.0% microbial additive improved average daily gain, feed efficiency, IgG content, and fecal microflora in growing-finishing pigs.

A multimodal machine learning model for predicting dementia conversion in Alzheimer's disease.

Alzheimer's disease (AD) accounts for 60-70% of the population with dementia. Mild cognitive impairment (MCI) is a diagnostic entity defined as an intermediate stage between subjective cognitive decline and dementia, and about 10-15% of people annually convert to AD. We aimed to investigate the most robust model and modality combination by combining multi-modality image features based on demographic characteristics in six machine learning models. A total of 196 subjects were enrolled from four hospitals and the Alzheimer's Disease Neuroimaging Initiative dataset. During the four-year follow-up period, 47 (24%) patients progressed from MCI to AD. Volumes of the regions of interest, white matter hyperintensity, and regional Standardized Uptake Value Ratio (SUVR) were analyzed using T1, T2-weighted-Fluid-Attenuated Inversion Recovery (T2-FLAIR) MRIs, and amyloid PET (αPET), along with automatically provided hippocampal occupancy scores (HOC) and Fazekas scales. As a result of testing the robustness of the model, the GBM model was the most stable, and in modality combination, model performance was further improved in the absence of T2-FLAIR image features. Our study predicts the probability of AD conversion in MCI patients, which is expected to be useful information for clinician's early diagnosis and treatment plan design.

Safety and feasibility of optimized transcranial direct current stimulation in patients with mild cognitive impairment due to Alzheimer's disease: a multicenter study protocol for a randomized controlled trial.

Introduction: Transcranial direct current stimulation (tDCS) may effectively preserve and improve cognitive function in patients with mild cognitive impairment (MCI). Research has shown that Individual brain characteristics can influence the effects of tDCS. Computer three-dimensional brain modeling based on magnetic resonance imaging (MRI) has been suggested as an alternative for determining the most accurate tDCS electrode position based on the patients' individual brain characteristics to enhance tDCS effects. Therefore, this study aims to determine the feasibility and safety of applying tDCS treatment using optimized and personalized tDCS electrode positions in patients with Alzheimer's disease (AD)-induced MCI using computer modeling and compare the results with those of a sham group to improve cognitive function. Method: A prospective active-sham group feasibility study was set to recruit 40 participants, who will be randomized into Optimized-tDCS and Sham-tDCS groups. The parameters for tDCS will be 2 mA (disk electrodes R = 1.5 cm) for 30 min during two sets of 15 sessions (2 weeks of resting period in between), using two electrodes in pairs. Using computer modeling, the tDCS electrode positions of each participant will be personalized. Outcome measurements are going to be obtained at three points: baseline, first post-test, and second post-test. The AD assessment scale-cognitive subscale (ADAS-Cog) and the Korean version of Mini-Mental State Examination (K-MMSE), together with other secondary outcomes and safety tests will be used. Discussion: For the present study, we hypothesize that compared to a sham group, the optimized personalized tDCS application would be effective in improving the cognitive function of patients with AD-induced MCI and the participants would tolerate the tDCS intervention without any significant adverse effects.Clinical trial registration: https://cris.nih.go.kr, identifier [KCT0008918].

Impact of Apolipoprotein E4 on the Locus Coeruleus Functional Connectivity in Preclinical Alzheimer's Disease.

Background: Recent interest has surged in the locus coeruleus (LC) for its early involvement in Alzheimer's disease (AD), notably concerning the apolipoprotein ɛ4 allele (APOE4). Objective: This study aimed to discern LC functional connectivity (FC) variations in preclinical AD subjects, dissecting the roles of APOE4 carrier status and amyloid-ß (Aß) deposition. Methods: A cohort of 112 cognitively intact individuals, all Aß-positive, split into 70 APOE4 noncarriers and 42 carriers, underwent functional MRI scans, neuropsychological assessments, and APOE genotyping. The research utilized seed to voxel analysis for illustrating LC rsFC discrepancies between APOE4 statuses and employed a general linear model to examine the interactive influence of APOE4 carrier status and Aß deposition on LC FC values. Results: The investigation revealed no significant differences in sex, age, or SUVR between APOE4 carriers and noncarriers. It found diminished LC FC with the occipital cortex in APOE4 carriers and identified a significant interaction between APOE4 carrier status and temporal lobe SUVR in LC FC with the occipital cortex. This interaction suggested a proportional increase in LC FC for APOE4 carriers. Additional notable interactions were observed affecting LC FC with various brain regions, indicating a proportional decrease in LC FC for APOE4 carriers. Conclusions: These findings confirm that APOE4 carrier status significantly influences LC FC in preclinical AD, showcasing an intricate relationship with regional Aß deposition. This underscores the critical role of genetic and pathological factors in early AD pathophysiology, offering insights into potential biomarkers for early detection and intervention strategies.

Hydrophilic/Hydrophobic Janus Nanofibers Containing Compound K for Cartilage Regeneration.

Introduction: Cartilage regeneration is a challenging issue due to poor regenerative properties of tissues. Electrospun nanofibers hold enormous potentials for treatments of cartilage defects. However, nanofibrous materials used for the treatment of cartilage defects often require physical and/or chemical modifications to promote the adhesion, proliferation, and differentiation of cells. Thus, it is highly desirable to improve their surface properties with functionality. We aim to design hydrophilic, adhesive, and compound K-loaded nanofibers for treatments of cartilage defects. Methods: Hydrophilic and adhesive compound K-containing polycaprolactone nanofibers (CK/PCL NFs) were prepared by coatings of gallic acid-conjugated chitosan (CHI-GA). Therapeutic effects of CHI-GA/CK/PCL NFs were assessed by the expression level of genes involved in the cartilage matrix degradation, inflammatory response, and lipid accumulations in the chondrocytes. In addition, Cartilage damage was evaluated by safranin O staining and immunohistochemistry of interleukin-1ß (IL-1ß) using OA animal models. To explore the pathway associated with therapeutic effects of CHI-GA/CK/PCL NFs, cell adhesion, phalloidin staining, and the expression level of integrins and peroxisome proliferator-activated receptor (PPARs) were evaluated. Results: CHI-GA-coated side of the PCL NFs showed hydrophilic and adhesive properties, whereas the unmodified opposite side remained hydrophobic. The expression levels of genes involved in the degradation of the cartilage matrix, inflammation, and lipogenesis were decreased in CHI-GA/CK/PCL NFs owing to the release of CK. In vivo implantation of CHI-GA/CK/PCL NFs into the cartilage reduced cartilage degradation induced by destabilization of the medial meniscus (DMM) surgery. Furthermore, the accumulation of lipid deposition and expression levels of IL-1ß was reduced through the upregulation of PPAR. Conclusion: CHI-GA/CK/PCL NFs were effective in the treatments of cartilage defects by inhibiting the expression levels of genes involved in cartilage degradation, inflammation, and lipogenesis as well as reducing lipid accumulation and the expression level of IL-1ß via increasing PPAR.

Comparative efficacy and safety of subcutaneous infliximab and vedolizumab in patients with Crohn's disease and ulcerative colitis included in randomised controlled trials.

BACKGROUND: While indirect comparison of infliximab (IFX) and vedolizumab (VDZ) in adults with Crohn's disease (CD) or ulcerative colitis (UC) shows that IFX has better effectiveness during induction, and comparable efficacy during maintenance treatment, comparative data specific to subcutaneous (SC) IFX (i.e., CT-P13 SC) versus VDZ are limited. AIM: Pooled analysis of randomised studies to compare efficacy and safety with IFX SC and VDZ in moderate-to-severe inflammatory bowel disease. METHODS: Parallel-group, randomised studies evaluating IFX SC and VDZ in patients with moderate-to-severe CD or UC were identified. Eligible studies reported ≥ 1 prespecified outcome of interest at Week 6 (reflecting treatment during the induction phase) and/or at 1 year (Weeks 50-54; reflecting treatment during the maintenance phase). Prespecified efficacy and safety outcomes considered in this pooled analysis included the proportions of patients achieving disease-specific clinical responses, clinical remission, or discontinuing due to lack of efficacy, and the proportions of patients experiencing adverse events (AEs), serious AEs, infections, serious infections, or discontinuing due to AEs. Data from multiple studies or study arms were extracted and pooled using a random-effect model; comparative analyses were performed separately for patients with CD and UC. RESULTS: We identified three eligible CD trials and four eligible UC trials that assigned over 1200 participants per disease cohort to either IFX SC or VDZ. In patients with CD, intravenous induction therapy with IFX demonstrated better efficacy (non-overlapping 95% confidence intervals [CIs]) compared with VDZ; during the maintenance phase, IFX SC showed numerically better efficacy (overlapping 95% CIs) than VDZ. A lower proportion of IFX SC-treated patients discontinued therapy due to lack of efficacy over 1 year. In patients with UC, efficacy profiles were similar with IFX SC and VDZ during the induction and maintenance phases, and a lower proportion of IFX SC-treated patients discontinued therapy due to lack of efficacy over 1 year. In both cohorts, safety profiles for IFX SC and VDZ were generally comparable during 1 year. CONCLUSION: IFX SC demonstrated better efficacy than VDZ in patients with CD, and similar efficacy to VDZ in patients with UC; 1-year safety was comparable with IFX SC and VDZ.

Switching from intravenous to subcutaneous infliximab maintenance therapy in inflammatory bowel disease: Post hoc longitudinal analysis of a randomized trial.

BACKGROUND: Pharmacokinetic non-inferiority of subcutaneous (SC) to intravenous (IV) CT-P13 maintenance therapy was demonstrated in a randomized trial (NCT02883452). This post hoc analysis evaluated longitudinal clinical outcomes with the two infliximab treatment strategies. METHODS: Patients with Crohn's disease or ulcerative colitis received CT­P13 IV loading doses (5 mg/kg; Week [W] 0 and W2) before randomization (1:1) to receive CT-P13 SC (body weight-based dosing every 2 weeks [Q2W]; W6-54; 'SC maintenance group') or CT­P13 IV (5 mg/kg Q8W; W6-22) then CT-P13 SC (Q2W; W30-54; 'IV-to-SC switch group'). Paired W30/W54 patient-level data were analyzed. RESULTS: Fifty-three (IV-to-SC switch) and fifty-nine (SC maintenance) patients were analyzed. Median trough serum CT-P13 concentrations were significantly higher at W54 versus W30 in the IV-to-SC switch group (20.4 versus 2.3 µg/mL; p < 0.00001), while remaining consistent in the SC maintenance group. Statistically significant improvements in pharmacokinetics, efficacy, fecal calprotectin levels, and quality of life were seen following switch to SC administration at W30 in the IV-to-SC switch group; safety findings were similar pre- and post-switch. CONCLUSION: Formulation switching from IV to SC infliximab maintenance therapy was well tolerated and may provide additional clinical improvements. Findings require confirmation in larger prospective studies.

Associations between Education Years and Resting-state Functional Connectivity Modulated by APOE ε4 Carrier Status in Cognitively Normal Older Adults.

Objective: : Cognitive reserve has emerged as a concept to explain the variable expression of clinical symptoms in the pathology of Alzheimer's disease (AD). The association between years of education, a proxy of cognitive reserve, and resting-state functional connectivity (rFC), a representative intermediate phenotype, has not been explored in the preclinical phase, considering risk factors for AD. We aimed to evaluate whether the relationship between years of education and rFC in cognitively preserved older adults differs depending on amyloid-beta deposition and APOE ε4 carrier status as effect modifiers. Methods: : A total of 121 participants underwent functional magnetic resonance imaging, [18F] flutemetamol positron emission tomography-computed tomography, APOE genotyping, and a neuropsychological battery. Potential interactions between years of education and AD risk factors for rFC of AD-vulnerable neural networks were assessed with whole-brain voxel-wise analysis. Results: : We found a significant education years-by-APOE ε4 carrier status interaction for the rFC from the seed region of the central executive (CEN) and dorsal attention networks. Moreover, there was a significant interaction of rFC between right superior occipital gyrus and the CEN seed region by APOE ε4 carrier status for memory performances and overall cognitive function. Conclusion: : In preclinical APOE ε4 carriers, higher years of education were associated with higher rFC of the AD vulnerable network, but this contributed to lower cognitive function. These results contribute to a deeper understanding of the impact of cognitive reserve on sensitive functional intermediate phenotypic markers in the preclinical phase of AD.

Development of Efficient Brain Age Estimation Method Based on Regional Brain Volume From Structural Magnetic Resonance Imaging.

OBJECTIVE: We aimed to create an efficient and valid predicting model which can estimate individuals' brain age by quantifying their regional brain volumes. METHODS: A total of 2,560 structural brain magnetic resonance imaging (MRI) scans, along with demographic and clinical data, were obtained. Pretrained deep-learning models were employed to automatically segment the MRI data, which enabled fast calculation of regional brain volumes. Brain age gaps for each subject were estimated using volumetric values from predefined 12 regions of interest (ROIs): bilateral frontal, parietal, occipital, and temporal lobes, as well as bilateral hippocampus and lateral ventricles. A larger weight was given to the ROIs having a larger mean volumetric difference between the cognitively unimpaired (CU) and cognitively impaired group including mild cognitive impairment (MCI), and dementia groups. The brain age was predicted by adding or subtracting the brain age gap to the chronological age according to the presence or absence of the atrophy region. RESULTS: The study showed significant differences in brain age gaps among CU, MCI, and dementia groups. Furthermore, the brain age gaps exhibited significant correlations with education level and measures of cognitive function, including the clinical dementia rating sum-of-boxes and the Korean version of the Mini-Mental State Examination. CONCLUSION: The brain age that we developed enabled fast and efficient brain age calculations, and it also reflected individual's cognitive function and cognitive reserve. Thus, our study suggested that the brain age might be an important marker of brain health that can be used effectively in real clinical settings.

Determining optimal bead central angle by applying machine learning to wire arc additive manufacturing (WAAM).

Wire arc additive manufacturing (WAAM) is being extensively used in various industrial fields. In WAAM, if a bead is deposited without considering the central angle, its shape may collapse with increasing number of layers. To address this problem, a new method for optimizing the bead geometry using a support vector machine (SVM) classifier was established in this study. The ranges of the optimal deposition conditions were determined using the SVM classifier and verified by experiments. Geometric data of deposited beads were extracted using a laser profiler, and an SVM binary classifier was used to predict suitable ranges of the deposition conditions. Data were extracted through 20 single-layer basic experiments, classification was performed based on 4°, and the appropriateness of SVM classification was found through 8 single-layer and 3 multi-layer verification experiments. The results showed that the SVM classifier successfully selected the ranges of the optimal deposition conditions. Verification experiments revealed that the results in all cases were appropriately classified based on the boundary of the classification line. Moreover, the SVM classifier was efficient even when a small amount of input data was available. The contribution of this study is that the developed method can help build desired bead geometries in scenarios where deposition is required in the WAAM process, such as re-manufacturing. Thus, this method can be used in real-world industrial applications through further research on the bead shape with multi-layer deposition.

Increased Likelihood of Dementia with Coexisting Atrophy of Multiple Regions of Interest.

BACKGROUND: Brain volume is associated with cognitive decline in later life, and cortical brain atrophy exceeding the normal range is related to inferior cognitive and behavioral outcomes in later life. OBJECTIVE: To investigate the likelihood of cognitive decline, mild cognitive impairment (MCI), or dementia, when regional atrophy is present in participants' magnetic resonance imaging (MRI). METHODS: Multi-center MRI data of 2,545 adults were utilized to measure regional volumes using NEUROPHET AQUA. Four lobes (frontal, parietal, temporal, and occipital), four Alzheimer's disease-related regions (entorhinal, fusiform, inferior temporal, and middle temporal area), and the hippocampus in the left and right hemispheres were measured and analyzed. The presence of regional atrophy from brain MRI was defined as ≤1.5 standard deviation (SD) compared to the age- and sex-matched cognitively normal population. The risk ratio for cognitive decline was investigated for participants with regional atrophy in contrast to those without regional atrophy. RESULTS: The risk ratio for cognitive decline was significantly higher when hippocampal atrophy was present (MCI, 1.84, p < 0.001; dementia, 4.17, p < 0.001). Additionally, participants with joint atrophy in multiple regions showed a higher risk ratio for dementia, e.g., 9.6 risk ratio (95% confidence interval, 8.0-11.5), with atrophy identified in the frontal, temporal, and hippocampal gray matter, than those without atrophy. CONCLUSIONS: Our study showed that individuals with multiple regional atrophy (either lobar or AD-specific regions) have a higher likelihood of developing dementia compared to the age- and sex-matched population without atrophy. Thus, further consideration is needed when assessing MRI findings.

The effect of deep brain structure modeling on transcranial direct current stimulation-induced electric fields: An in-silico study.

To study transcranial direct current stimulation (tDCS) and its effect on the brain, it could be useful to predict the distribution of the electric field induced in the brain with given tDCS parameters. As a solution, simulation with realistic computational models using magnetic resonance images (MRIs) have been widely used in the fields. With the recent advance of deep learning-based segmentation techniques of the brain, questions have been raised about if tDCS-induced electric field is affected by the deep brain structures. This study aimed to investigate the effect of the deep brain structure modeling on the induced electric field. To this end, we generated models with and without the deep brain structures by using an open MRI dataset comprising tDCS parameters, electric field simulation results and in-vivo intracranial recordings in the deep brain structures. We investigated the difference between the simulation results of the two models with a statistical analysis. Our results indicated that tDCS-induced electric fields and current flow in the brain are significantly different when the deep brain structures are considered.