Background Chronic unpredictable stress (CUS) is widely used to model depression-like behaviors in rodents. However, its validation has been established in male animals, and its applicability to females remains poorly characterized. New method We evaluated the validity of a CUS protocol in male and female Balb/c mice using the sucrose preference test (SPT), forced swim test (FST), elevated plus maze (EPM), open field test (OFT), and integrated emotionality z-scores. Predictive validity was assessed using different imipramine treatment schedules. Additional experiments examined the influence of sucrose concentration and estrous cycle stage on basal sucrose preference in females. Results In male mice, 21 days of CUS reduced sucrose preference and emotionality z-scores. Chronic imipramine treatment initiated during the last 14 days of stress exposure prevented these behavioral alterations and reduced immobility time in the FST, whereas no significant effects were detected in the EPM. In female mice, CUS did not induce changes in sucrose preference or emotionality z-scores, although imipramine reduced immobility in the FST. Comparison with existing methods Although CUS protocols are widely validated in male rodents, the protocol showed limited sensitivity in females. Basal sucrose preference was influenced by estrous cycle stage and sucrose concentration, with reduced preference during periovulatory and greater preference during post-ovulatory stages only when sucrose was 2%. Conclusions These findings indicate that behavioral endpoints validated in male rodents may not show comparable sensitivity in females and highlight the importance of considering baseline behavioral characteristics, estrous cycle stage, and sucrose concentration when designing and interpreting chronic stress studies.
BACKGROUND Transcranial temporal interference stimulation (TIS) is a promising tool for non-invasive deep brain modulation, yet inter-individual anatomical differences cause substantial electric field (E-field) variability under identical parameters, hindering clinical translation. OBJECTIVE We review recent progress (2017-2026) in individualized TIS, focusing on why inter-individual variability occurs and how to address it for clinical applications. METHODS We searched PubMed/MEDLINE, Web of Science, Scopus, IEEE Xplore, preprint repositories (bioRxiv, medRxiv, arXiv), and clinical trial registries from June 2017 through August 2026, synthesizing studies across quantitative E-field variability, biophysical mediation mechanisms, computational optimization frameworks, and human trials. RESULTS Even with individualized optimization, the focal spot can shift by up to 1.2cm between subjects. Target modulation depth varies with age and sex, largely due to variations in skull thickness and cerebrospinal fluid volume. For E-field optimization, individualized finite element method (FEM) modeling remains the most accurate approach, whereas faster population-based optimization (17% error margin) is viable only for superficial brain areas. In human translational studies, a clear distinction must be maintained across levels of evidence: while individualized montages targeting the striatum and subthalamic nucleus demonstrate physiological target engagement, behavioral modulation in healthy cohorts, and preliminary acute symptom relief in pilot patient trials, definitive clinical efficacy in large-scale randomized trials (e.g., ongoing nucleus accumbens protocols) and effective active sham protocols remain ongoing challenges. CONCLUSION Individualized planning is essential for deep TIS. Future studies should focus on multi-center trials and standardized in vivo closed-loop validation using functional neuroimaging and electrophysiology.
Background Diffusion MRI (dMRI) infers white-matter tracts using stored gradient tables. If a table is flipped or permuted relative to the image coordinates during conversion or transfer, downstream tractography is silently corrupted. New method GradLint is a fast, open-source tool that detects gradient flips, audits acquisition schemes, and repairs corrupted tables. It evaluates all 48 signed-permutation candidate conventions from a single DTI fit using an analytic matrix transformation, ranking them with an anatomy-anchored, directional-stepping fiber-coherence index. Results On 1106 corrupted cases (HCP, single-site, and clinical datasets), GradLint ranked the correct convention first in every case and retained antipode-aware agreement with the field reference dwigradcheck. At the default 2% relative-margin threshold, --apply-repair yielded 1022 correct FLAG repairs; 84 L5 WARN cases required additional --force-repair authorization. Without authorization, corrections were withheld. It successfully resolved acquisition obliquity in voxel space. The frame-aware writer verified correctness on 190 subjects. With eight threads per tool, the median within-subject speed-up was 12.90–23.25 × across the three timing groups; with automatic/default threading it was 5.27–8.91 × . GradLint's eight-thread median check times were 2.85–14.07 s. Comparison with existing methods Unlike tools that repeat expensive tracking or DTI fitting for every candidate, GradLint obtains all candidate eigenvector fields analytically from one fit. It integrates a scheme quality-control audit, outputs machine-readable provenance, returns exit codes for CI/CD integration, and generates optional DTI principal-direction glyph figures. Conclusions GradLint is a fast, open-source Rust tool that acts as a quality-control gate, guarding dMRI preprocessing pipelines against silent orientation errors.
Background The recognition of affective states from EEG recordings considering the sleep condition can be an important supporting tool in therapy and in the treatment of psychological or psychiatric disorders. New method Since sleep deprivation changes EEG characteristics, a two-stage framework is developed for affective state classification. First, a classification between "Normal Sleep" (NS) and "Sleep-Deprived" (SD) is conducted using resting-state EEG data. The data are then fed into one of two newly proposed, lightweight Convolutional Neural Networks (CNNs) to recognize four affective states. Results In the first stage, CNN-BiLSTM achieves 95.96% accuracy under window-level splitting and 70.46% under subject-independent 5-fold cross-validation, while in the second stage the proposed models achieve 93.40% (NS) and 90.95% (SD) under window-level splitting, and 38.98% (NS) and 38.97% (SD) under cross-validation. The lower subject-independent accuracies reflect the class imbalance and the clustering of scores near the class boundaries, which make cross-subject four-class generalization challenging. Comparison with existing methods As existing studies mostly classify momentary emotions without addressing sleep conditions, direct comparison with prior work is difficult; we therefore benchmarked our framework against several established baseline models, including SqueezeNet, EEGNet, ShallowConvNet, Support Vector Machine, and Random Forest. Conclusions This study suggests that considering physiological states can improve multi-class classification of affective states from time-varying neurophysiological signals such as EEG. The framework performs strongly under window-level evaluation, whereas its performance under the stricter subject-independent setting is more limited and should be interpreted accordingly. It can nonetheless contribute to more comprehensive research in affective neuroscience.
