The habenula is a small epithalamic structure composed of two distinct subregions, the medial (MHb) and lateral (LHb) habenula. It serves as a critical hub for integrating fronto-limbic and brainstem signals to regulate motivation, mood, and reward processing. Therefore, it is unsurprising that dysfunction of the habenula has been implicated in several mood disorders including major depressive disorder (MDD), a debilitating mood disorder marked by low mood and feelings of hopelessness. This review synthesizes recent advances in understanding the habenula's neurocircuitry, molecular landscape, and role in MDD pathophysiology, while evaluating its potential as a therapeutic target. Specifically, emerging evidence highlights subregion-specific pathology. Indeed, in MDD and in animal models of depression, the MHb has been shown to exhibit marked downregulation of calcium-dependent activator protein for secretion 2 (CAPS2) and deficits in nicotinic acetylcholine receptor-mediated signaling. While in the LHb, dysregulated expression profiles of inward-rectifying potassium channel Kir4.1, the β isoform of calcium/calmodulin-dependent protein kinase II (CaMKIIβ), protein phosphatase 2 A (PP2A), and small nucleolar RNA SNORA69 have been found in animal models of depression and MDD postmortem studies. Structural imaging and postmortem neurohistological studies in MDD patients have further revealed habenular volume changes, reduced neuronal cell counts, diminished cell area, and abnormal functional connectivity. As research unravels the habenula's complexities, its potential in treating mood disorders grows increasingly salient, offering new avenues for intervention in mental health.

Abstract We present the IMAGMASEIS project, a large-N seismic experiment carried out on La Palma (Canary Islands, Spain) between 2023 and 2024, aimed at high-resolution imaging of the crustal and upper mantle structure using passive seismic methods. The project involved the deployment of 235 temporary broadband and short-period seismic stations, supplementing 21 permanent stations, thus creating the densest seismic network ever installed on the island. The main goal is to characterise the magmatic plumbing system beneath Cumbre Vieja volcano, identify magma accumulation zones, and investigate structural changes related to the 2021 Tajogaite eruption. We describe the experimental design, network configuration, instrumentation, deployment strategies, and challenges encountered, including difficult terrain and logistical constraints. Preliminary results demonstrate the potential of the dataset for ambient noise tomography, receiver function analysis, and local earthquake studies. IMAGMASEIS provides a valuable resource for understanding volcanic and tectonic processes in oceanic island settings and serves as a model for cost-effective, high-density seismic deployments in similar environments.

This study evaluates the performance of a deep learning framework supported by a cross-replication strategy for predicting Alzheimer’s disease (AD) from structural magnetic resonance imaging (MRI). EfficientNetV2-B0 was selected due to its favorable accuracy-efficiency trade-off. The workflow consisted of two stages: (i) clustering-based relabeling of the full dataset into five clinically meaningful categories, and (ii) training a classifier on the relabeled data. To assess the stability of the proposed approach, the model was trained across multiple random initializations on a fixed train/validation/test split. Class-wise Average Precision, macro- and micro-averaged Precision-Recall Area Under the Curve (PR–AUC) and Receiver Operating Characteristic Area Under the Curve (ROC–AUC), and their 95% confidence intervals were reported using bootstrap resampling. The cross-replication strategy yielded improved stability across initializations, with a mean test accuracy of 0.95 compared with 0.94 for the single-run baseline, along with consistently higher PR–AUC and ROC–AUC values. These findings suggest that cross-replication enhances the reliability of AD stage prediction by reducing performance variability due to stochastic initialization, although further evaluation with alternative data partitions or external validation cohorts is warranted.

Fear of pain and reinjury significantly hinders return to sports (RTS) after anterior cruciate ligament reconstruction (ACLR). However, the neural basis of this psychological barrier remains unclear. ACLR patients will exhibit structural and functional brain changes in regions related to pain and emotion, influencing their psychological readiness to RTS. Cross-sectional study. Level 3. We recruited 36 ACLR patients and 36 healthy controls, collecting visual analog scale (VAS) scores for knee pain, Anterior Cruciate Ligament-Return to Sport after Injury (ACL-RSI) scores, and structural and functional magnetic resonance imaging data. Significant smaller gray matter (GM) volume was observed in the thalamus (effect size, -0.813), periaqueductal gray (PAG) (effect size, -0.737), and prefrontal cortex (PFC) (effect size, -0.690) in ACLR patients. We also found weakened functional connectivity between the PAG and the anterior cingulate cortex (ACC). GM volume in the thalamus was correlated positively with ACL-RSI scores (r = 0.362). Notably, the effect of VAS scores on ACL-RSI was mediated by the reduced FC between PAG and ACC (direct effect, -2.071, indirect effect, -0.826). This study reveals that the psychological readiness to RTS after ACLR may be due to peripheral nociceptive input causing changes in pain-related brain structures and functions. Recognizing these neuropsychological changes may guide comprehensive rehabilitation strategies post-ACLR, emphasizing the need for interventions targeting central neural mechanisms alongside physical recovery.

Early-stage identification of Alzheimer’s Disease (AD) is a sizeable challenge to health care globally due to its progressive nature and the fact that there is no available effective treatment. It becomes strategic for practices using interventions meant to halt or reverse cognitive decline if diagnosed early. Recent medical imaging advancements, mainly Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI), have greatly unveiled subtle pathological changes associated with this disease. Studies have shown that multimodal neuroimaging can provide crucial information regarding the structural and functional changes in the brain that are associated with AD. However, more research is required to establish sustainable techniques for the detection of AD at all its stages. In this research, a framework of Moth Flame Optimized UNet++ with self-attention is proposed to analyze multi-modal inputs of Structural Magnetic Resonance Imaging (sMRI), PET, and neuropsychological test data for the classification of Alzheimer’s disease ( UNet + + SA –MFO). The framework involves pre-processing the sMRI and PET images to denoise, skull strip, denormalization, next captures complex texture patterns and spatial relationships from both images by placing attention gates at skip connections to ensure reduction of irrelevant features and enhanced localization of significant features. Neuropsychological assessments are passed through fully connected layers of UNet++. Moth Flame Optimization optimizes hyperparameters. Then, a fused feature set is created as a concatenation of all features from multi-modal inputs. Support vector machine (SVM), k-nearest neighbors (k-NN), and Random Forest are used to model, and later weighted stacking ensemble is used to predict the output. The framework is implemented in Python, and evaluation metrics like precision, recall, F1-score, Accuracy, and Area Under the Receiver Operating Characteristic curve (AUC-ROC) are analyzed. UNet++ SA –MFO attains effective 90.5%, 89.9%, 90.2%, 91.8% and 94.1% precision, recall, F1-score, Accuracy and AUC-ROC against existing multi-modal ensemble frameworks. The above findings highlight the potential of enhanced UNet++ augmented with self-attention-based feature extraction and the benefit of integrating innovative optimization for more precise Alzheimer’s Disease diagnosis and classification at the early stage. This proposed framework provides valuable contributions to Alzheimer’s Disease pathology insight, improved diagnostic sensitivity, and ultimately, improved management of this neurological disease.

Objective Alzheimer’s disease (AD), the most common neurodegenerative disorder, involves the progressive loss of vulnerable neurons. Tracking its progression via structural magnetic resonance imaging (sMRI), which captures subtle brain anatomical changes, is vital for advancing diagnosis and treatment. Although generative models show promise in simulating disease progression by forecasting future magnetic resonance imaging (MRI) sequences, generating high-quality MRI with faithful anatomical structures remains challenging. Methods To narrow this gap, we proposed a progress map-guided generative adversarial network (pg-GAN) that leverages population-level longitudinal data to enhance individual-level prediction. First, progress maps were constructed by averaging intensity residuals between MRI scans acquired at different time points across a population, thereby preserving the comprehensive volumetric evolution of the brain over time. Then, the progress maps served as spatiotemporal priors and were embedded into a backbone generative adversarial network (GAN) via a proposed feature-wise fusion module (FFM) to predict future MRI for individuals. Results We performed extensive experiments on 210 individuals with longitudinal MRIs from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. The results demonstrated that our pg-GAN outperformed other conditioning models. The quantitative results showed that the normalized root mean squared error (NRMSE) decreased from 0.1623 to 0.1549, while the peak signal-to-noise ratio (PSNR) increased from 25.9353 dB to 26.3157 dB. Conclusion Incorporating group-level progression priors into the generative model can significantly improve the accuracy and anatomical fidelity of predicted MRIs, enhance the visualization of disease progression at the voxel level, and advance the development of precision treatment for AD.

Early and accurate diagnosis of mild cognitive impairment (MCI), a prodromal stage of Alzheimer's disease (AD), is critical for timely intervention and management. Nevertheless, effectively integrating heterogeneous multi-modal data for AD diagnosis remains worthy of further investigation. Therefore, we propose a supervised contrastive learning framework that integrates single nucleotide polymorphisms (SNPs), plasma proteomics, and T1-weighted structural magnetic resonance imaging (sMRI) from a biologically informed perspective, with SNPs influencing protein structure or gene expression levels, ultimately altering brain structure. Through a supervised contrastive learning mechanism, we construct a cross-modal feature space and introduce a similarity-based symmetrical attention mechanism to capture intermodal interactions and mitigate modality heterogeneity. We validate the proposed method on the Alzheimer's Disease Neuroimaging Initiative dataset, and experimental results demonstrate accuracy of 96.1%, 86.2%, and 86.1% for the AD-NC task, MCI-NC task, and AD-MCI task. In addition, the application of explainable methods to our model identified multi-modal biomarkers related to AD diagnosis. The experimental results validate the effectiveness of our model in the diagnosis of AD and MCI.

Neural signatures of bipolar disorder subtypes: A comprehensive systematic review of neuroimaging studies.

Design of an NNO-based fluorogenic chemosensor for Hg2+ ion detection: Structural characterization and biological imaging in cells and zebrafish

Alterations of neurofluid transport in patients with obstructive sleep apnea and insomnia disorder.

Diffuse Pediatric High Grade Gliomas.

This study sought to determine the intrasession repeatability of the diffusion-weighted (DW) arterial spin labeling (ASL) sequence at different postlabel delays (PLDs). We first performed numerical simulations to study the accuracy of the two-compartment water exchange rate (Kw) fitting model with added Gaussian noise for DW PLDs at 1500, 1800, and 2100 ms. Ten young, healthy participants then underwent a structural T1 scan and two intrasession in vivo DW ASL scans at each PLD on a 3T MRI. The Kw, arterial transit time (ATT), and cerebral blood flow maps were linearly registered to the structural images, which were then segmented using FreeSurfer into masks with 35 bilateral gray-matter regions. Simulation results showed that the Kw fitting model performed at an error rate less than 10% at physiological ATTs and Kw values, but that error and bias increased at a PLD of 2100 ms and at ATT ranges where the overall blood signal fraction (A1) is low. In vivo analysis showed a significant positive correlation between intrasession measurements of regional Kw at a DW PLD of 1800 ms (β = 0.33, p < 0.001) only. Furthermore, a significant positive relationship between Kw and cerebral blood flow was seen at a DW PLD of 1500 ms (β = 0.26, p = 0.005) and DW PLD of 2100 ms (β = 0.39, p = 0.006). Overall, DW ASL provides the strongest intrasession repeatability at a PLD of 1800 ms in young, healthy subjects, and a simulation study shows accurate Kw fits at physiologic range of ATTs and Kw values.

A novel method of measuring cortical thickness in schizophrenia spectrum disorders: a preliminary analysis of multi-echo fast spoiled gradient echo MRI.

ABSTRACTPurposeAutism spectrum condition (ASC) is a neurodevelopmental condition characterized by impairments in communication, social interaction, and restricted or repetitive behaviors. Extensive research has aimed to identify structural brain distinctions between individuals with ASC and neurotypical individuals using neuroimaging techniques. However, limited attention has been given to evaluating how variations in image acquisition protocols across different centers influence these observed differences.MethodThis analysis focuses on structural magnetic resonance imaging (sMRI) data from the Autism Brain Imaging Data Exchange I (ABIDE I) database, considering both subjects' condition and individual centers to identify disparities between ASC and control groups. Statistical analysis, employing permutation tests, utilizes two distinct statistical mapping methods: statistical agnostic mapping (SAM) and statistical parametric mapping (SPM).FindingResults from the SAM mapping method show greater consistency with existing literature. However, no statistically significant differences were found in any brain region. This outcome is attributed to factors such as limited sample sizes within certain centers, noise effects, and the challenges posed by multi‐center databases in a heterogeneous condition such as autism.ConclusionThe study indicates limitations in using the ABIDE I database to detect structural differences in the brain between neurotypical individuals and those diagnosed with ASC. Multi‐center variability and sample size constraints significantly affect the reliability of findings in structural neuroimaging studies of autism.

Abstract Regional brain atrophy estimated from structural magnetic resonance imaging (MRI) is a widely used measure of neurodegeneration in Alzheimer’s disease (AD), Frontotemporal Lobar Degeneration (FTLD), and other dementias. Yet, traditional MRI-derived morphometric estimates are susceptible to measurement errors, posing a challenge for detecting longitudinal atrophy over short intervals. Here, we examined the utility of multiple MRI scans acquired in rapid succession (i.e., cluster scanning) for detecting longitudinal cortical atrophy over 3- and 6-month intervals within individual participants. Four individuals with mild cognitive impairment or mild dementia likely due to AD or FTLD participated in this study. At baseline, 3 months, and 6 months, structural MRI data were collected on a 3 Tesla scanner using a fast 1.2-mm T1-weighted multi-echo magnetization-prepared rapid gradient echo (MEMPRAGE) sequence (acquisition time = 2’23’’). At each timepoint, participants underwent up to 32 MEMPRAGE scans acquired in four separate sessions over two days. Using linear mixed-effects models, we found that phenotypically vulnerable cortical (“core atrophy”) regions exhibited statistically significant longitudinal atrophy in all participants (i.e., decreased cortical thickness) by 3 months and further demonstrated preferential vulnerability compared to control regions in three of the participants over at least one of the 3-month intervals. These findings provide proof-of-concept evidence that pooling multiple morphometric estimates derived from cluster scanning can detect longitudinal cortical atrophy over short intervals in individual patients with neurodegenerative dementias.

BackgroundThe quantitative assessment of the oculomotor system has emerged as a promising biomarker for neurodegenerative disorders. Although oculomotor impairments are commonly observed in multiple system atrophy (MSA) patients, the specific abnormalities and underlying neural structural changes remain poorly understood.ObjectivesTo explore oculomotor abnormalities and associated brain changes in MSA, evaluating their potential as biomarkers for diagnosis and disease monitoring.MethodsA total of 100 MSA patients and 50 healthy controls (HCs) were included in this study. All subjects underwent comprehensive evaluations, including clinical assessments, virtual reality (VR)-based ocular-tracking tasks and structural magnetic resonance imaging (MRI).ResultsCompared with HCs, MSA patients showed significantly impaired smooth pursuit (SP) with increased number of deviations (13.33 [29.33] vs. 5.67 [7.67], p < 0.001); reduced prosaccadic (PS) average velocity (194.80 ± 82.45 °/s vs. 263.07 ± 68.17 °/s, p < 0.001); and reduced antisaccade (AS) average velocity (165.82 ± 85.75 °/s vs. 257.05 ± 74.39 °/s, p < 0.001). A combination of PS and AS average velocities with SP number of deviations effectively distinguished MSA patients from HCs with an AUC of 0.814. PS average velocity was negatively correlated with UMSARS total scores (r = -0.354, p < 0.001), whereas AS accuracy was positively correlated with MoCA scores (r = 0.375, p = 0.001). Voxel-based morphometry revealed significant associations between these oculomotor parameters and atrophy in the cerebellum and frontal gyrus (p < 0.05, family-wise error correction).ConclusionsOur study provides comprehensive insights into the VR-based quantitative oculomotor analysis and its association with regional brain atrophy in MSA, contributing to novel biomarkers identification and therapeutic targets exploration.

Characterization of myelin degradation in the white matter (WM) is important for understanding neurodegeneration. We demonstrate label-free in vivo imaging of myelin structure in the WM of mice, through intact cortex, using third harmonic generation (THG) microscopy at 1320-nm excitation. Longitudinal THG imaging of the same axons in the cuprizone mouse model of multiple sclerosis revealed dynamics of myelin blistering. Further, we measured intranodal distance at nodes of Ranvier in vivo and developed a novel metric of myelin structural change based on spatial concentration of the brightest THG signal. We also demonstrated compatibility with three-photon excited fluorescence microscopy by imaging GFP-labeled microglia in the WM in parallel with THG microscopy, thereby enabling detailed tracking of subcortical myelin and other cellular dynamics in neurodegenerative disease models.

Age of symptom onset is highly variable in familial frontotemporal lobar degeneration (f-FTLD). Accurate prediction of onset would inform clinical management and trial enrollment. Prior studies indicate that individualized maps of brain atrophy can predict conversion to dementia in f-FTLD. We used a Bayesian linear mixed-effect (BLME) prediction method for identifying accelerated brain volume loss to predict conversion to dementia. Participants included 234 asymptomatic or prodromal carriers of C9orf72, GRN, or MAPT mutations (including 21 dementia converters) with ≥3 longitudinal magnetic resonance imaging (MRI) T1-weighted scans. The BLME models established individual voxel-wise gray matter trajectories using the first 2 scans. Person-specific clusters of accelerated volume loss were estimated in subsequent scans and tested as predictors of dementia conversion compared with other approaches in time-varying Cox proportional hazard models covarying for age. Receiver-operating characteristic (ROC) curves estimated utility of cluster volume in discriminating which participants converted to dementia within 24 months. The BLME cluster volume predicted conversion to dementia in f-FTLD mutation carriers overall and separately in C9orf72, GRN, and MAPT, with comparable hazard ratios observed for atrophy W-maps and regional volumes. Within a 24-month timeframe, BLME cluster volume discriminated dementia converters from non-converters with larger areas under the curve (AUCs) than other approaches. Bayesian-modeled individualized atrophy scores predict dementia progression among asymptomatic f-FTLD mutation carriers and may have increased utility compared with other structural imaging methods when studying individuals over shorter timeframes that align with clinical trial design. ANN NEUROL 2026.

Purpose: There are different types of hair loss known as alopecia. Various methods for treating Androgenetic Alopecia (AGA) are being investigated in the preclinical stage using C57BL/6 mice affected by this condition. The purpose of the study was to evaluate the effects of Dihydrotestosterone (DHT) on the skin layers of male C57BL/6 mice, simulating a model of AGA using high-resolution ultrasound imaging. Materials and Methods: Seven-week-old male C57BL/6 mice were selected for the study. To induce AGA, three of the mice received intraperitoneal injections of DHT at a dosage of 1 mg per day for five consecutive days, a known method for provoking hair loss via androgenic pathways. High-resolution ultrasound imaging at 40 and 75 MHz frequencies allowed detailed observation of skin layer changes due to DHT administration. Shear modulus and Young modulus were extracted using dynamic loading throughout ultrasonography with a 40 MHz frequency. Both control and AGA-affected groups were evaluated through structural imaging and were compared with histopathological results. Tissues were stained with Hematoxylin-Eosin (H&amp;E) and Trichrome Mason. Results: Ultrasound imaging revealed that the epidermis thickness was 0.22±0.01 mm in the control group compared to 0.31±0.02 mm in the AGA group at 40 MHz. At 75 MHz, these measurements were 0.10±0.05 mm for the control group and 0.20±0.01 mm for the AGA group. The dermis thickness measurements showed 0.30±0.02 mm in the control group and 0.70±0.04 mm in the AGA group at 40 MHz, while at 75 MHz, the thicknesses were 0.40±0.02 mm for the control group and 0.70±0.04 mm for the AGA group. H&amp;E staining results aligned with these ultrasound findings, confirming increased epidermal and dermal thicknesses in the AGA group. Elasticity metrics indicated a shear modulus of 1.19±0.60 kPa for the control group and 6.70±0.33 kPa for the AGA group, while Young modulus demonstrated values of 6.47±0.32 kPa for the control group and 22.69±1.13 kPa for the AGA group. Further corroboration of altered tissue elasticity was provided by Trichrome staining, indicating significant changes in skin structure. Conclusion: The administration of DHT in the C57BL/6 mice model leads to notable structural changes in skin layers, evidenced by an increased thickness of both the epidermis and dermis, along with diminished mechanical properties of skin elasticity.

This review examines Marine-Lenhart syndrome (MLS), an uncommon thyroid disorder that combines Graves' disease with autonomously functioning thyroid nodules (AFTNs) and demonstrates why nuclear medicine imaging is essential for accurate diagnosis and treatment planning. We reviewed case reports and case series published over the past three decades and analyzed clinical presentation, diagnostic approaches, prevalence rates, disease mechanisms, and treatment outcomes of MLS. This relatively rare syndrome occurs in approximately 0.8-4.3% of patients with Graves' disease, though rates vary depending on the diagnostic criteria and imaging methods used. It presents a diagnostic challenge because AFTNs often remain suppressed and appear "cold" on initial scans, only becoming visible after treatment - the characteristic "unmasking effect". Thyroid scintigraphy with either 99mTc-pertechnetate or 123I provides functional information that structural imaging cannot show. Treatment differs from standard Graves' disease management as MLS requires higher radioiodine activities because nodules may escape radiation damage, and patients may need radioiodine re-ablation. Type 3 MLS, which includes cold nodules, requires careful cancer risk evaluation with ultrasound and fine-needle aspiration when appropriate. Nuclear medicine imaging is crucial for MLS diagnosis and treatment planning. Functional imaging identifies AFTNs, guides appropriate radioiodine treatment, and prevents treatment failure. Routine thyroid scintigraphy is recommended in all patients with hyperthyroidism and thyroid nodules before starting therapy.