Structural Imaging Research Articles

Dissecting depression symptoms: Multi-omics clustering uncovers immune-related subgroups and cell-type specific dysregulation

In a subset of patients with mental disorders, such as depression, low-grade inflammation and altered immune marker concentrations are observed. However, these immune alterations are often assessed by only one data type and small marker panels. Here, we used a transdiagnostic approach and combined data from two cohorts to define subgroups of depression symptoms across the diagnostic spectrum through a large-scale multi-omics clustering approach in 237 individuals. The method incorporated age, body mass index (BMI), 43 plasma immune markers and RNA-seq data from peripheral mononuclear blood cells (PBMCs). Our initial clustering revealed four clusters, including two immune-related depression symptom clusters characterized by elevated BMI, higher depression severity and elevated levels of immune markers such as interleukin-1 receptor antagonist (IL-1RA), C-reactive protein (CRP) and C-C motif chemokine 2 (CCL2 or MCP-1). In contrast, the RNA-seq data mostly differentiated a cluster with low depression severity, enriched in brain related gene sets. This cluster was also distinguished by electrocardiography data, while structural imaging data revealed differences in ventricle volumes across the clusters. Incorporating predicted cell type proportions into the clustering resulted in three clusters, with one showing elevated immune marker concentrations. The cell type proportion and genes related to cell types were most pronounced in an intermediate depression symptoms cluster, suggesting that RNA-seq and immune markers measure different aspects of immune dysregulation. Lastly, we found a dysregulation of the SERPINF1/VEGF-A pathway that was specific to dendritic cells by integrating immune marker and RNA-seq data. This shows the advantages of combining different data modalities and highlights possible markers for further stratification research of depression symptoms.

The Mindful Brain: A Systematic Review of the Neural Correlates of Trait Mindfulness.

Trait self-report mindfulness scales measure one's disposition to pay nonjudgmental attention to the present moment. Concerns have been raised about the validity of trait mindfulness scales. Despite this, there is extensive literature correlating mindfulness scales with objective brain measures, with the goal of providing insight into mechanisms of mindfulness, and insight into associated positive mental health outcomes. Here, we systematically examined the neural correlates of trait mindfulness. We assessed 68 correlational studies across structural magnetic resonance imaging, task-based fMRI, resting-state fMRI, and EEG. Several consistent findings were identified, associating greater trait mindfulness with decreased amygdala reactivity to emotional stimuli, increased cortical thickness in frontal regions and insular cortex regions, and decreased connectivity within the default-mode network. These findings converged with results from intervention studies and those that included mindfulness experts. On the other hand, the connections between trait mindfulness and EEG metrics remain inconclusive, as do the associations between trait mindfulness and between-network resting-state fMRI metrics. ERP measures from EEG used to measure attentional or emotional processing may not show reliable individual variation. Research on body awareness and self-relevant processing is scarce. For a more robust correlational neuroscience of trait mindfulness, we recommend larger sample sizes, data-driven, multivariate approaches to self-report and brain measures, and careful consideration of test-retest reliability. In addition, we should leave behind simplistic explanations of mindfulness, as there are many ways to be mindful, and leave behind simplistic explanations of the brain, as distributed networks of brain areas support mindfulness.

Breakdown of TMS evoked EEG signal propagation within the default mode network in Alzheimer’s disease

BackgroundThe neural activity of the Default Mode Network (DMN) is disrupted in patients with In Alzheimer’s disease (AD). ObjectivesWe used a novel multimodal approach to track neural signal propagation within the DMN in AD patients. MethodsTwenty mild to moderate AD patients were recruited. We used transcranial magnetic stimulation (TMS) pulses to probe with a millisecond time resolution the propagation of evoked electroencephalography (EEG) signal following the neural activation of the Precuneus (PC), which is a key hub area of the DMN. Moreover, functional and structural magnetic resonance imaging (MRI) data were collected to reconstruct individual features of the DMN. ResultsIn AD patients a probe TMS pulse applied over the PC evokes an increased local activity unmasking underlying hyperexcitability. In contrast, the EEG evoked neural signal did not propagate efficiently within the DMN showing a remarkable breakdown of signal propagation. fMRI and structural tractography showed that impaired signal propagation was related to the same connectivity matrices derived from DMN BOLD signal and transferred by specific white matter bundles forming the cingulum. These features were not detectable stimulating other areas (left dorsolateral prefrontal cortex) or for different networks (fronto-parietal network). Finally, connectivity breakdown was associated with cognitive impairment, as measured with the Clinical Dementia Rating Scale sum of boxes (CDR-SB). ConclusionsTMS-EEG in AD shows both local hyperexcitability and a lack of signal propagation within the DMN. These neurophysiological features also correlate with structural and cognitive attributes of the patients. SignificanceNeuronavigated TMS-EEG may be used as a novel neurophysiological biomarker of DMN connectivity in AD patients.

Children born very preterm experience altered cortical expansion over the first decade of life.

The brain develops rapidly from the final trimester of gestation through childhood, with cortical surface area expanding greatly in the first decade of life. However, it is unclear exactly where and how cortical surface area changes after birth, or how prematurity affects these developmental trajectories. Fifty-two very preterm (gestational age at birth = 26 ± 1.6 weeks) and 41 full-term (gestational age at birth = 39 ± 1.2 weeks) infants were scanned using structural magnetic resonance imaging at term-equivalent age and again at 9/10 years of age. Individual cortical surface reconstructions were extracted for each scan. Infant and 9/10 cortical surfaces were aligned using anatomically constrained Multimodal Surface Matching (aMSM), a technique that allows calculation of local expansion gradients across the cortical surface for each individual subject. At the neonatal time point, very preterm infants had significantly smaller surface area than their full-term peers (P < 0.001), but at the age 9/10-year time point, very preterm and full-term children had comparable surface area (P > 0.05). Across all subjects, cortical expansion by age 9/10 years was most pronounced in frontal, temporal, and supramarginal/inferior parietal junction areas, which are key association cortices (P Spin < 0.001). Very preterm children showed greater cortical surface area expansion between term-equivalent age and age 9/10 compared to their full-term peers in the medial and lateral frontal areas, precuneus, and middle temporal/banks of the superior sulcus junction (P < 0.05). Furthermore, within the very preterm group, expansion was highly variable within the orbitofrontal cortex and posterior regions of the brain. By mapping these patterns across the cortex, we identify differences in association cortices that are known to be important for executive functioning, emotion processing, and social cognition. Additional longitudinal work will be needed to understand if increased expansion in very preterm children is adaptive, or if differences persist into adulthood.

Association between Neural Plasticity and Pain-Related Fear in Chronic Ankle Instability: A Structural Neuroimaging Study.

Pain-related movement fear is a contributing factor to residual pain and functional deficits in chronic ankle instability (CAI), but its underlying neural mechanisms remain unclear. We aimed to (1) delineate whether participants with CAI exhibit discernible differences in specific emotion and pain-related brain regions, compared to a healthy control (HC) cohort and (2) explore potential neural mechanisms underlying pain and fear in participants with CAI, with an emphasis on investigating possible associations with pain-related neural plasticity. Cross-sectional study. University research laboratory. 28 participants with CAI (17males and 11 females; age: 31.28±6.31 years) and 28 HCs (16 males and 12 females; age: 30.18±7.59 years). We analyzed T1 structural imaging data from participants and assessed their fear of movement and pain intensity using the Tampa Scale for Kinesiophobia (TSK) and the Visual Analog Scale (VAS) for pain, respectively. We compared the mean gray matter (GM) density of pain-related area between the two groups and their correlations with the TSK and VAS scores. In comparison with the HC group, participants with CAI showed a significant decrease in the mean GM density in the prefrontal cortex (Cohen's d = -0.808) and periaqueductal gray (Cohen's d = -0.934). In participants with CAI, the mean GM density of the prefrontal cortex (PFC) was negatively correlated with the TSK scores (r = -0.531). During intense exercise, the mean GM density of the periaqueductal gray (PAG) was negatively correlated with the VAS scores (r = -0.484). Additionally, TSK scores were positively correlated with VAS scores (r = 0.455). Our exploratory findings suggest that, in participants with CAI, the atrophy of the PFC and PAG may be associated with pain-related fear. Future clinical diagnosis and treatment for CAI should consider the impact of psychological barriers on functional recovery.

Immunomodulatory treatment may change functional and structural brain imaging in severe mental disorders

Neuroinflammation has been implicated in the pathophysiology of schizophrenia and obsessive-compulsive disorder (OCD) and deviations in brain structure and connectivity are seen in these disorders. Here, we explore the effects of a potent immunomodulatory treatment on neuroimaging. In a pilot study of rituximab treatment in schizophrenia and OCD, a subgroup (n = 13) underwent structural and functional magnetic resonance imaging before and 5 months after treatment, to study longitudinal changes in resting-state functional connectivity (rsFC) and voxel-based morphometry (VBM).A hypothesis-free exploratory whole-brain analysis was performed twice to assess changes in rsFC, using anterior cingulate cortex, anterior insula, posterior insula and nucleus accumbens as seed regions. There were significant interactions (diagnosis x time) in connectivity between right posterior insula and two clusters encompassing basal ganglia and anterior frontal pole, and between left anterior insula and a cluster in basal ganglia, where connectivity decreased in OCD and increased in schizophrenia. The increase of connectivity after rituximab, between left anterior insula and parts of cerebellum and lingual gyrus and between left posterior insula and parts of cerebellum, correlated with improved global psychosocial functioning according to the Personal and Social Performance Scale, especially in schizophrenia. VBM analysis identified two clusters with increased grey matter volumes (GMV) after rituximab, one in right insula overlapping one of the seed regions with significant rsFC changes. This pilot study implies that rituximab may influence both brain structure and connectivity and that GMV changes and rsFC changes are regionally associated.

Electrical Impedance Tomography-Based Electronic Skin for Multi-Touch Tactile Sensing Using Hydrogel Material and FISTA Algorithm.

Flexible electronic skin (e-skin) can enable robots to have sensory forms similar to human skin, enhancing their ability to obtain more information from touch. The non-invasive nature of electrical impedance tomography (EIT) technology allows electrodes to be arranged only at the edges of the skin, ensuring the stretchability and elasticity of the skin's interior. However, the image quality reconstructed by EIT technology has deteriorated in multi-touch identification, where it is challenging to clearly reflect the number of touchpoints and accurately size the touch areas. This paper proposed an EIT-based flexible tactile sensor that employs self-made hydrogel material as the primary sensing medium. The sensor's structure, fabrication process, and tactile imaging principle were elaborated. To improve the quality of image reconstruction, the fast iterative shrinkage-thresholding algorithm (FISTA) was embedded into the EIDORS toolkit. The performances of the e-skin in aspects of assessing the touching area, quantitative force sensing and multi-touch identification were examined. Results showed that the mean intersection over union (MIoU) of the reconstructed images was improved up to 0.84, and the tactile position can be accurately imaged in the case of the number of the touchpoints up to seven (larger than two to four touchpoints in existing studies), proving that the combination of the proposed sensor and imaging algorithm has high sensitivity and accuracy in multi-touch tactile sensing. The presented e-skin shows potential promise for the application in complex human-robot interaction (HRI) environments, such as prosthetics and wearable devices.

Machine learning with multiple modalities of brain magnetic resonance imaging data to identify the presence of bipolar disorder

BackgroundBipolar disorder (BD) is a chronic psychiatric mood disorder that is solely diagnosed based on clinical symptoms. These symptoms often overlap with other psychiatric disorders. Efforts to use machine learning (ML) to create predictive models for BD based on data from brain imaging are expanding but have often been limited using only a single modality and the exclusion of the cerebellum, which may be relevant in BD. MethodsIn this study, we sought to improve ML classification of BD by combining information from structural, functional, and diffusion-weighted imaging. Participants (108 BD I, 78 control) with BD type I and matched controls were recruited into an imaging study. This dataset was randomly divided into training and testing sets. For each of the three modalities, a separate ML model was selected, trained, and then used to generate a prediction of the class of each test subject. Majority voting was used to combine results from the three models to make a final prediction of whether a subject had BD. An independent replication sample was used to evaluate the ability of the ML classification to generalize to data collected at other sites. ResultsCombining the three machine learning models through majority voting resulted in an accuracy of 89.5 % for classification of the test subjects as being in the BD or control group. Bootstrapping resulted in a 95 % confidence interval of 78.9 %–97.4 % for test accuracy. Performance was reduced when only using 2 of the 3 modalities. Analysis of feature importance revealed that the cerebellum and nodes of the emotional control network were among the most important regions for classification. The machine learning model performed at chance on the independent replication sample. ConclusionBD I could be identified with high accuracy in our relatively small sample by combining structural, functional, and diffusion-weighted imaging data within a single site but not generalize well to an independent replication sample. Future studies using harmonized imaging protocols may facilitate generalization of ML models.

Evaluating Prostate Cancer: The Diagnostic Impact of MRI and Its Relationship With Transrectal Ultrasound (TRUS)-Guided Biopsy.

Background Prostate disorders, including benign enlargement and malignancy, are commonly evaluated through imaging techniques. Historically, transrectal ultrasound (TRUS) has been used for prostate imaging and biopsy. However, multiparametric MRI (mpMRI), which integrates structural and functional imaging methods, offers enhanced diagnostic capabilities. This study evaluates the effectiveness of mpMRI, including its grading via Prostate Imaging - Reporting and Data System (PI-RADS) or Likert scoring, in distinguishing between benign and malignant prostatic conditionsand compares these findings with TRUS outcomes. Methodology This prospective study enrolled 30 male patients aged 45 to 75 years (mean age 60 years), selected based on prostatic abnormalities, elevated prostate-specific antigen (PSA) levels (>4 ng/dL), or palpable nodules detected via digital rectal examination. MRI, including PI-RADS or Likert scoring, was utilized to assess prostatic lesions, and results were compared with histopathological data obtained from TRUS-guided biopsies. Results Among the 30 patients, common symptoms included urinary retention (60%) and painful urination (53.3%). Malignant tumors were diagnosed in 12 patients (40%). MRI identified eight cases with enlarged transitional zones and irregular signals in peripheral zones (benign prostatic hyperplasia with tumor)and four cases with irregular signals in both zones (sarcoma). Concordance between MRI T2-weighted (T2W) observations and biopsy results showed 60% malignancy detection. Sensitivity assessments revealed MRI detected 15 true-positives (50%), TRUS detected six true positives (20%), and multivoxel spectroscopic analysis (MVS) identified 14 true-positives (46.7%). PI-RADS or Likert scoring of mpMRI was correlated with TRUS outcomes, highlighting its enhanced diagnostic accuracy compared to TRUS alone. Conclusion While TRUS remains a standard diagnostic tool, it is limited by significant sampling errors and complications. The integration of mpMRI, with its grading system, significantly improves diagnostic accuracy and treatment planning. Although mpMRI alone has limitations, its combination with contrast-enhanced MRI, diffusion-weighted imaging, and MR spectroscopy offers a comprehensive approach to enhanced prostate cancer detection.

Optimization of parameters for image denoising algorithm pertaining to generalized Caputo-Fabrizio fractional operator

The aim of the present paper is to optimize the values of different parameters related to the image denoising algorithm involving Caputo Fabrizio fractional integral operator of non-singular type with the Mittag-Leffler function in generalized form. The algorithm aims to find the coefficients of a kernel to remove the noise from images. The optimization of kernel coefficients are done on the basis of different numerical parameters like Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structure Similarity Index measure (SSIM) and Image Enhancement Factor (IEF). The performance of the proposed algorithm is investigated through above-mentioned numeric parameters and visual perception with the other prevailed algorithms. Experimental results demonstrate that the proposed optimized kernel based on generalized fractional operator performs favorably compared to state of the art methods. The uniqueness of the paper is to highlight the optimized values of performance parameters for different values of fractional order. The novelty of the presented work lies in the development of a kernel utilizing coefficients from a fractional integral operator, specifically involving the Mittag-Leffler function in a more generalized form.

TRanscranial AlterNating current stimulation FOR patients with mild Alzheimer’s Disease (TRANSFORM-AD): a randomized controlled clinical trial

BackgroundThe mechanistic effects of gamma transcranial alternating current stimulation (tACS) on hippocampal gamma oscillation activity in Alzheimer’s Disease (AD) remains unclear. This study aimed to clarify beneficial effects of gamma tACS on cognitive functioning in AD and to elucidate effects on hippocampal gamma oscillation activity.MethodsThis is a double-blind, randomized controlled single-center trial. Participants with mild AD were randomized to tACS group or sham group, and underwent 30 one-hour sessions of either 40 Hz tACS or sham stimulation over consecutive 15 days. Cognitive functioning, structural magnetic resonance imaging (MRI), and simultaneous electroencephalography–functional MRI (EEG-fMRI) were evaluated at baseline, the end of the intervention and at 3-month follow-up from the randomization.ResultsA total of 46 patients were enrolled (23 in the tACS group, 23 in the sham group). There were no group differences in the change of the primary outcome, 11-item cognitive subscale of the Alzheimer’s Disease Assessment Scale (ADAS-Cog) score after intervention (group*time, p = 0.449). For secondary outcomes, compared to the control group, the intervention group showed significant improvement in MMSE (group*time, p = 0.041) and MoCA scores (non-parametric test, p = 0.025), which were not sustained at 3-month follow-up. We found an enhancement of theta-gamma coupling in the hippocampus, which was positively correlated with improvements of MMSE score and delayed recall. Additionally, fMRI revealed increase of the local neural activity in the hippocampus.ConclusionEffects on the enhancement of theta-gamma coupling and neural activity within the hippocampus suggest mechanistic models for potential therapeutic mechanisms of tACS.Trial registrationClinicalTrials.gov, NCT 03920826; Registration Date: 2019-04-19.

Molecular and micro-architectural mapping of gray matter alterations in psychosis.

The psychosis spectrum encompasses a heterogeneous range of clinical conditions associated with abnormal brain development. Detecting patterns of atypical neuroanatomical maturation across psychiatric disorders requires an interpretable metric standardized by age-, sex- and site-effect. The molecular and micro-architectural attributes that account for these deviations in brain structure from typical neurodevelopment are still unknown. Here, we aggregate structural magnetic resonance imaging data from 38,696 healthy controls (HC) and 1256 psychosis-related conditions, including first-degree relatives of schizophrenia (SCZ) and schizoaffective disorder (SAD) patients (n = 160), individuals who had psychotic experiences (n = 157), patients who experienced a first episode of psychosis (FEP, n = 352), and individuals with chronic SCZ or SAD (n = 587). Using a normative modeling approach, we generated centile scores for cortical gray matter (GM) phenotypes, identifying deviations in regional volumes below the expected trajectory for all conditions, with a greater impact on the clinically diagnosed ones, FEP and chronic. Additionally, we mapped 46 neurobiological features from healthy individuals (including neurotransmitters, cell types, layer thickness, microstructure, cortical expansion, and metabolism) to these abnormal centiles using a multivariate approach. Results revealed that neurobiological features were highly co-localized with centile deviations, where metabolism (e.g., cerebral metabolic rate of oxygen (CMRGlu) and cerebral blood flow (CBF)) and neurotransmitter concentrations (e.g., serotonin (5-HT) and acetylcholine (α4β2) receptors) showed the most consistent spatial overlap with abnormal GM trajectories. Taken together these findings shed light on the vulnerability factors that may underlie atypical brain maturation during different stages of psychosis.

BIN1 deficiency enhances ULK3-dependent autophagic flux and reduces dendritic size in mouse hippocampal neurons

ABSTRACT Genome-wide association studies identified variants around the BIN1 (bridging integrator 1) gene locus as prominent risk factors for late-onset Alzheimer disease. In the present study, we decreased the expression of BIN1 in mouse hippocampal neurons to investigate its neuronal function. Bin1 knockdown via RNAi reduced the dendritic arbor size in primary cultured hippocampal neurons as well as in mature Cornu Ammonis 1 excitatory neurons. The AAV-mediated Bin1 RNAi knockdown also generated a significant regional volume loss around the injection sites at the organ level, as revealed by 7-Tesla structural magnetic resonance imaging, and an impaired spatial reference memory performance in the Barnes maze test. Unexpectedly, Bin1 knockdown led to concurrent activation of both macroautophagy/autophagy and MTOR (mechanistic target of rapamycin kinase) complex 1 (MTORC1). Autophagy inhibition with the lysosome inhibitor chloroquine effectively mitigated the Bin1 knockdown-induced dendritic regression. The subsequent molecular studydemonstrated that increased expression of ULK3 (unc-51 like kinase 3), which is MTOR-insensitive, supported autophagosome formation in BIN1 deficiency. Reducing ULK3 activity with SU6668, a receptor tyrosine kinase inhibitor, or decreasing neuronal ULK3 expression through AAV-mediated RNAi, significantly attenuated Bin1 knockdown-induced hippocampal volume loss and spatial memory decline. In Alzheimer disease patients, the major neuronal isoform of BIN1 is specifically reduced. Our work suggests this reduction is probably an important molecular event that increases the autophagy level, which might subsequently promote brain atrophy and cognitive impairment through reducing dendritic structures, and ULK3 is a potential interventional target for relieving these detrimental effects.Abbreviations: AV: adeno-associated virus; Aβ: amyloid-β; ACTB: actin, beta; AD: Alzheimer disease; Aduk: Another Drosophila Unc-51-like kinase; AKT1: thymoma viral proto-oncogene 1; AMPK: AMP-activated protein kinase; AP: autophagosome; BafA1: bafilomycin A1; BDNF: brain derived neurotrophic factor; BIN1: bridging integrator 1; BIN1-iso1: BIN1, isoform 1; CA1: cornu Ammonis 1; CA3: cornu Ammonis 3; CLAP: clathrin and adapter binding; CQ: chloroquine; DMEM: Dulbecco’s modified Eagle medium; EGFP: enhanced green fluorescent protein; GWAS: genome-wide association study; MAP1LC3B/LC3B: microtubule-associated protein 1 light chain 3 beta; MRI: magnetic resonance imaging; MTOR; mechanistic target of rapamycin kinase; MTORC1: MTOR complex 1; PET: positron emission tomography; qRT-PCR: real-time quantitative reverse transcription PCR; ROS: reactive oxygen species; RPS6KB1: ribosomal protein S6 kinase B1; TFEB: transcription factor EB; ULK1: unc-51 like kinase 1; ULK3: unc-51 like kinase 3.

Multiple-ResNet GAN: An enhanced high-resolution image generation method for translation from fundus structure image to fluorescein angiography.

Fluorescein angiography (FA) is a diagnostic method for observing the vascular circulation in the eye. However, it poses a risk to patients. Therefore, generative adversarial networks have been used to convert retinal fundus structure images into FA images. Existing high-resolution image generation methods employ complex deep network models that are challenging to optimize, which leads to issues such as blurred lesion boundaries and poor capture of microleakage and microvessels. In this study, we propose a multiple-ResNet generative adversarial network (GAN) to improve model training, thereby enhancing the ability to generate high-resolution FA images. First, the structure of the multiple-ResNet generator is designed to enhance detail generation in high-resolution images. Second, the Gaussian error linear unit (GELU) activation function is used to help the model converge rapidly. The effectiveness of the multiple-ResNet is verified using the publicly available Isfahan MISP dataset. Experimental results show that our method outperforms other methods, achieving better quantitative results with a mean structural similarity of 0.641, peak signal-to-noise ratio of 18.25, and learned perceptual image patch similarity of 0.272. Compared with state-of-the-art methods, the results showed that using the multiple-ResNet framework and GELU activation function can improve the generation of detailed regions in high-resolution FA images.

Association of birth weight with neuropsychological functioning in early adolescence: A retrospective cohort study

ObjectiveThis study aimed to compare the neuropsychological function in early adolescence between children born small for gestational age (SGA) or large for gestational age (LGA) and those born appropriate for gestational age (AGA). MethodsThis retrospective cohort study utilized data from the Adolescent Brain Cognitive Development study in 2016–18. Children born of singleton pregnancy with complete information of birth weight and delivery week were enrolled. Their neuropsychological functioning were assessed by the brain structural magnetic resonance imaging (MRI), combined with cognitive and behavioral measurements. Linear mixed-effects models and subgroup analyses were performed. ResultsAmong 5,922 children aged 9–11, children born SGA and LGA demonstrated similar cognitive and behavioral performances as children born AGA (P > 0.05). In the MRI measurement, brain area and volume were lower among SGA children compared to AGA children (t=-5.626, Cohen's d = 0.448, P < 0.001; t=-6.071, Cohen's d = 0.427, P < 0.001); brain area and volume were higher among LGA children compared to AGA children (t = 8.562, Cohen's d = 0.470, P < 0.001; t = 8.562, Cohen's d = 0.470, P < 0.001). Cortical thickness was of no statistical difference (P > 0.05). These associations were confirmed by sensitivity analyses and propensity score matching. ConclusionChildren born of SGA and LGA status were associated with altered brain area and volume structure in early adolescence.

Magnetic Resonance Imaging to Detect Structural Brain Changes in Huntington's Disease: A Review of Data from Mouse Models.

Structural magnetic resonance imaging (MRI) is a powerful tool to visualize 3D neuroanatomy and assess pathology and disease progression in neurodegenerative disorders such as Huntington's disease (HD). The development of mouse models of HD that reproduce many of the psychiatric, motor and cognitive impairments observed in human HD has improved our understanding of the disease and provided opportunities for testing novel therapies. Similar to the clinical scenario, MRI of mouse models of HD demonstrates onset and progression of brain pathology. Here, we provided an overview of the articles that used structural MRI in mouse models of HD to date, highlighting the differences between studies and models and describing gaps in the current state of knowledge and recommendations for future studies.

High-Frequency Hearing Loss, Hippocampal Volume, and Motoric Cognitive Risk Syndrome in Older Adults in China: A Population-Based Study.

Little is known about the associations of hearing loss, hippocampal volume, and motoric cognitive risk syndrome (MCR) in older adults. We aimed to investigate the associations of hearing loss with MCR and hippocampal volume; and the interaction of hearing loss with hippocampal volume on MCR. This population-based cross-sectional study included 2,540 dementia-free participants (age≥60 years; 56.5% women) in the baseline examination of the Multimodal Interventions to Delay Dementia and Disability in rural China. Data were collected through face-to-face interviews, clinical examination, and laboratory tests. Hearing function was assessed using pure tone audiometry test. In the subsample (n = 661), hippocampal volume was assessed on structural magnetic resonance images. Data were analyzed with logistic regression models. In the total sample, MCR was diagnosed in 246 persons (9.7%). High-frequency hearing loss was significantly associated with an increased likelihood of MCR and slow gait. In the subsample, the restricted cubic spline plots indicated an inverted U-shaped nonlinear relationship between high-frequency hearing performance and hippocampal volume. Moreover, greater hippocampal volume was significantly associated with a deduced likelihood of MCR and subjective cognitive decline (SCD). In addition, there were statistical interactions of high-frequency hearing loss with hippocampal volume on MCR and slow gait (p for interaction < 0.05), such that the associations were statistically significant only among participants free of high-frequency hearing loss. High-frequency hearing loss was associated with an increased likelihood of MCR in older adults. The hippocampus might play a part in the relationship of high-frequency hearing loss and MCR.

Plastic reorganization of the topological asymmetry of hemispheric white matter networks induced by congenital visual experience deprivation

Congenital blindness offers a unique opportunity to investigate human brain plasticity. The influence of congenital visual loss on the asymmetry of the structural network remains poorly understood. To address this question, we recruited 21 participants with congenital blindness (CB) and 21 age-matched sighted controls (SCs). Employing diffusion and structural magnetic resonance imaging, we constructed hemispheric white matter (WM) networks using deterministic fiber tractography and applied graph theory methodologies to assess topological efficiency (i.e., network global efficiency, network local efficiency, and nodal local efficiency) within these networks. Statistical analyses revealed a consistent leftward asymmetry in global efficiency across both groups. However, a different pattern emerged in network local efficiency, with the CB group exhibiting a symmetric state, while the SC group showed a leftward asymmetry. Specifically, compared to the SC group, the CB group exhibited a decrease in local efficiency in the left hemisphere, which was caused by a reduction in the nodal properties of some key regions mainly distributed in the left occipital lobe. Furthermore, interhemispheric tracts connecting these key regions exhibited significant structural changes primarily in the splenium of the corpus callosum. This result confirms the initial observation that the reorganization in asymmetry of the WM network following congenital visual loss is associated with structural changes in the corpus callosum. These findings provide novel insights into the neuroplasticity and adaptability of the brain, particularly at the network level.

Leveraging ultra-high field (7T) MRI in psychiatric research.

Non-invasive brain imaging has played a critical role in establishing our understanding of the neural properties that contribute to the emergence of psychiatric disorders. However, characterizing core neurobiological mechanisms of psychiatric symptomatology requires greater structural, functional, and neurochemical specificity than is typically obtainable with standard field strength MRI acquisitions (e.g., 3T). Ultra-high field (UHF) imaging at 7 Tesla (7T) provides the opportunity to identify neurobiological systems that confer risk, determine etiology, and characterize disease progression and treatment outcomes of major mental illnesses. Increases in scanner availability, regulatory approval, and sequence availability have made the application of UHF to clinical cohorts more feasible than ever before, yet the application of UHF approaches to the study of mental health remains nascent. In this technical review, we describe core neuroimaging methodologies which benefit from UHF acquisition, including high resolution structural and functional imaging, single (1H) and multi-nuclear (e.g., 31P) MR spectroscopy, and quantitative MR techniques for assessing brain tissue iron and myelin. We discuss advantages provided by 7T MRI, including higher signal- and contrast-to-noise ratio, enhanced spatial resolution, increased test-retest reliability, and molecular and neurochemical specificity, and how these have begun to uncover mechanisms of psychiatric disorders. Finally, we consider current limitations of UHF in its application to clinical cohorts, and point to ongoing work that aims to overcome technical hurdles through the continued development of UHF hardware, software, and protocols.

Combined Whole Eye and Face Transplant: Microsurgical Strategy and 1-Year Clinical Course.

Catastrophic facial injury with globe loss remains a formidable clinical problem with no previous reports of reconstruction by whole eye or combined whole eye and facial transplant. To develop a microsurgical strategy for combined whole eye and facial transplant and describe the clinical findings during the first year following transplant. A 46-year-old man who sustained a high-voltage electrical injury with catastrophic tissue loss to his face and left globe underwent combined whole eye and face transplant using personalized surgical devices and a novel microsurgical strategy at a specialized center for vascularized composite allotransplantation. Reperfusion and viability of the whole eye and facial allografts, retinal function, and incidence of acute rejection. The patient underwent a combined whole eye and face transplant from an immunologically compatible donor with primary optic nerve coaptation and conventional postoperative immunosuppression. Globe and retinal perfusion were maintained throughout the immediate postoperative period, evidenced by fluorescein angiography. Optical coherence tomography demonstrated atrophy of inner retinal layers and attenuation and disruption of the ellipsoid zone. Serial electroretinography confirmed retinal responses to light in the transplanted eye. Using structural and functional magnetic resonance imaging, the integrity of the transplanted visual pathways and potential occipital cortical response to light stimulation of the transplanted eye was demonstrated. At 1 year post transplant (postoperative day 366), there was no perception of light in the transplanted eye. This is the first report of whole eye transplant combined with facial transplant, demonstrating allograft survival including rejection-free graft survival and electroretinographic measurements indicating retinal response to light stimuli. These data highlight the potential for clinical allotransplantation for globe loss.