Topographical metal burden correlates with brain atrophy and clinical severity in Wilson's disease.

BACKGROUND: Quantitative susceptibility mapping (QSM) is a post-processing technique that creates brain susceptibility maps reflecting metal burden through tissue magnetic susceptibility. We assessed topographic differences in magnetic susceptibility between participants with and without Wilson's disease (WD), correlating these findings with clinical severity, brain volume, and biofluid copper and iron indices. METHODS: A total of 43 patients with WD and 20 unaffected controls, were recruited. QSM images were derived from a 3T MRI scanner. Clinical severity was defined using the minimal Unified Wilson's Disease Rating Scale (M-UWDRS) and Montreal Cognitive Assessment scoring. Differences in magnetic susceptibilities between groups were evaluated using general linear regression models, adjusting for age and sex. Correlations between the susceptibilities and clinical scores were analyzed using Spearman's method. RESULTS: In age- and sex-adjusted analyses, magnetic susceptibility values were increased in WD patients compared with controls, including caudate nucleus, putamen, globus pallidus, and substantia nigra (all p < 0.01). Putaminal susceptibility was greater with an initial neuropsychiatric presentation (n = 25) than with initial hepatic dysfunction (n = 18; p = 0.04). Susceptibility changes correlated negatively with regional brain volume in almost all topographic regions. Serum ferritin, but not serum copper or ceruloplasmin, correlated positively with magnetic susceptibility level in the caudate nucleus (p = 0.04), putamen (p = 0.04) and the hippocampus (p = 0.03). The dominance of magnetic susceptibility in cortical over subcortical regions correlated with M-UWDRS scores (p < 0.01). CONCLUSION: The magnetic susceptibility changes could serve as a surrogate marker for patients with WD.

Wireless optically pumped magnetometer MEG.

The current magnetoencephalography (MEG) systems, which rely on cables for control and signal transmission, do not fully realize the potential of wearable optically pumped magnetometers (OPM). This study presents a significant advancement in wireless OPM-MEG by reducing magnetization in the electronics and developing a tailored wireless communication protocol. Our protocol effectively eliminates electromagnetic interference, particularly in the critical frequency bands of MEG signals, and accurately synchronizes the acquisition and stimulation channels with the host computer's clock. We have successfully achieved single-channel wireless OPM-MEG measurement and demonstrated its reliability by replicating three well-established experiments: The alpha rhythm, auditory evoked field, and steady-state visual evoked field in the human brain. Our prototype wireless OPM-MEG system not only streamlines the measurement process but also represents a major step forward in the development of wearable OPM-MEG applications in both neuroscience and clinical research.

Taxonomy of Acute Stroke: Imaging, Processing, and Treatment.

Stroke management employs a variety of diagnostic imaging modalities, image processing and analysis methods, and treatment procedures. This work categorizes methods for stroke imaging, image processing and analysis, and treatment, and provides their taxonomies illustrated by a state-of-the-art review. Imaging plays a critical role in stroke management, and the most frequently employed modalities are computed tomography (CT) and magnetic resonance (MR). CT includes unenhanced non-contrast CT as the first-line diagnosis, CT angiography, and CT perfusion. MR is the most complete method to examine stroke patients. MR angiography is useful to evaluate the severity of artery stenosis, vascular occlusion, and collateral flow. Diffusion-weighted imaging is the gold standard for evaluating ischemia. MR perfusion-weighted imaging assesses the penumbra. The stroke image processing methods are divided into non-atlas/template-based and atlas/template-based. The non-atlas/template-based methods are subdivided into intensity and contrast transformations, local segmentation-related, anatomy-guided, global density-guided, and artificial intelligence/deep learning-based. The atlas/template-based methods are subdivided into intensity templates and atlases with three atlas types: anatomy atlases, vascular atlases, and lesion-derived atlases. The treatment procedures for arterial and venous strokes include intravenous and intraarterial thrombolysis and mechanical thrombectomy. This work captures the state-of-the-art in stroke management summarized in the form of comprehensive and straightforward taxonomy diagrams. All three introduced taxonomies in diagnostic imaging, image processing and analysis, and treatment are widely illustrated and compared against other state-of-the-art classifications.

Major depression mistaken as frontotemporal dementia due to PET scan.

Clinicians should be aware that the hypometabolism associated with depression can mimic frontotemporal dementia on PET.

Between neurons and networks: investigating mesoscale brain connectivity in neurological and psychiatric disorders.

The study of brain connectivity has been a cornerstone in understanding the complexities of neurological and psychiatric disorders. It has provided invaluable insights into the functional architecture of the brain and how it is perturbed in disorders. However, a persistent challenge has been achieving the proper spatial resolution, and developing computational algorithms to address biological questions at the multi-cellular level, a scale often referred to as the mesoscale. Historically, neuroimaging studies of brain connectivity have predominantly focused on the macroscale, providing insights into inter-regional brain connections but often falling short of resolving the intricacies of neural circuitry at the cellular or mesoscale level. This limitation has hindered our ability to fully comprehend the underlying mechanisms of neurological and psychiatric disorders and to develop targeted interventions. In light of this issue, our review manuscript seeks to bridge this critical gap by delving into the domain of mesoscale neuroimaging. We aim to provide a comprehensive overview of conditions affected by aberrant neural connections, image acquisition techniques, feature extraction, and data analysis methods that are specifically tailored to the mesoscale. We further delineate the potential of brain connectivity research to elucidate complex biological questions, with a particular focus on schizophrenia and epilepsy. This review encompasses topics such as dendritic spine quantification, single neuron morphology, and brain region connectivity. We aim to showcase the applicability and significance of mesoscale neuroimaging techniques in the field of neuroscience, highlighting their potential for gaining insights into the complexities of neurological and psychiatric disorders.

Multi-feature concatenation and multi-classifier stacking: An interpretable and generalizable machine learning method for MDD discrimination with rsfMRI.

Major depressive disorder (MDD) is a serious and heterogeneous psychiatric disorder that needs accurate diagnosis. Resting-state functional MRI (rsfMRI), which captures multiple perspectives on brain structure, function, and connectivity, is increasingly applied in the diagnosis and pathological research of MDD. Different machine learning algorithms are then developed to exploit the rich information in rsfMRI and discriminate MDD patients from normal controls. Despite recent advances reported, the MDD discrimination accuracy has room for further improvement. The generalizability and interpretability of the discrimination method are not sufficiently addressed either. Here, we propose a machine learning method (MFMC) for MDD discrimination by concatenating multiple features and stacking multiple classifiers. MFMC is tested on the REST-meta-MDD data set that contains 2428 subjects collected from 25 different sites. MFMC yields 96.9% MDD discrimination accuracy, demonstrating a significant improvement over existing methods. In addition, the generalizability of MFMC is validated by the good performance when the training and testing subjects are from independent sites. The use of XGBoost as the meta classifier allows us to probe the decision process of MFMC. We identify 13 feature values related to 9 brain regions including the posterior cingulate gyrus, superior frontal gyrus orbital part, and angular gyrus, which contribute most to the classification and also demonstrate significant differences at the group level. The use of these 13 feature values alone can reach 87% of MFMC's full performance when taking all feature values. These features may serve as clinically useful diagnostic and prognostic biomarkers for MDD in the future.

Dysfunction of the glymphatic system in childhood absence epilepsy.

Objective: This study aimed to evaluate the glymphatic system in childhood absence epilepsy (CAE) using diffusion tensor image analysis along the paravascular space (DTI-ALPS) index. Methods: Forty-two CAE patients and 50 age- and gender-matched healthy controls (HC) were included in this study. All participants underwent scanning using a Siemens 3.0 T magnetic resonance scanner, and the DTI-ALPS index was calculated. The study compared the differences of DTI-ALPS index between CAE patients and the healthy controls. Additionally, this study also assessed the relationship between the DTI-ALPS index and clinical characteristics such as age, seizure frequency, and duration of epilepsy. Results: The DTI-ALPS index was lower in CAE patients compared to the healthy controls (1.45 ± 0.36 vs. 1.66 ± 0.30, p < 0.01). The DTI-ALPS index showed a negative correlation with the duration of epilepsy (r = -0.48, p < 0.01) and a positive correlation with age (r = 0.766, p < 0.01) in CAE patients. However, no significant correlation was observed between the DTI-ALPS index and seizure frequency. Conclusion: The results of this study indicate that children with CAE exhibit dysfunction in the glymphatic system of the brain, which might contribute to understanding the pathophysiological mechanism of CAE. The DTI-ALPS, as a non-invasive diagnostic marker, can be used to assess the function of the glymphatic system in CAE patients, providing promising applications in the diagnosis and research of CAE.

Exploring the links among peripheral immunity, biomarkers, cognition, and neuroimaging in Alzheimer's disease.

INTRODUCTION: We analyzed relationships among peripheral immunity markers, cognition, Alzheimer's disease (AD)-related biomarkers, and neuroimaging to understand peripheral immunity involvement in AD. METHODS: Peripheral immunity markers were assessed in AD, non-AD neurodegenerative disorders, and controls, examining their connections with cognition, AD-related biomarkers, and neuroimaging using multiple regression models. RESULTS: The study included 1579 participants. Higher levels of white blood cell, neutrophil, monocyte, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and lower lymphocyte-to-monocyte ratio (LMR) were associated with cognitive decline and more severe anxiety and depression. The impact of lower LMR, lymphocyte count, and higher NLR on cognitive decline is mediated through cerebrospinal fluid amyloid beta (Aß) levels. Additionally, increased PLR, NLR, and SII were associated with brain atrophy and hippocampal Aß deposition (amyloid positron emission tomography). DISCUSSION: Peripheral immunity markers offer a non-invasive and cost-effective means of studying AD-related pathophysiological changes, providing valuable insights into its pathogenesis and treatment. Highlights: Peripheral immunity markers linked to cognitive decline and anxiety/depression.Low LMR, LYM, and high NLR linked to reduced CSF Aß, impacting cognition.High PLR, NLR, SII associated with brain atrophy and hippocampal Aß deposition.

Brain-Inspired Spatio-Temporal Associative Memories for Neuroimaging Data Classification: EEG and fMRI.

Humans learn from a lot of information sources to make decisions. Once this information is learned in the brain, spatio-temporal associations are made, connecting all these sources (variables) in space and time represented as brain connectivity. In reality, to make a decision, we usually have only part of the information, either as a limited number of variables, limited time to make the decision, or both. The brain functions as a spatio-temporal associative memory. Inspired by the ability of the human brain, a brain-inspired spatio-temporal associative memory was proposed earlier that utilized the NeuCube brain-inspired spiking neural network framework. Here we applied the STAM framework to develop STAM for neuroimaging data, on the cases of EEG and fMRI, resulting in STAM-EEG and STAM-fMRI. This paper showed that once a NeuCube STAM classification model was trained on a complete spatio-temporal EEG or fMRI data, it could be recalled using only part of the time series, or/and only part of the used variables. We evaluated both temporal and spatial association and generalization accuracy accordingly. This was a pilot study that opens the field for the development of classification systems on other neuroimaging data, such as longitudinal MRI data, trained on complete data but recalled on partial data. Future research includes STAM that will work on data, collected across different settings, in different labs and clinics, that may vary in terms of the variables and time of data collection, along with other parameters. The proposed STAM will be further investigated for early diagnosis and prognosis of brain conditions and for diagnostic/prognostic marker discovery.

Alzheimer's disease cortical morphological phenotypes are associated with TOMM40'523-APOE haplotypes.

Both the APOE ε4 and TOMM40 rs10524523 ("523") genes have been associated with risk for Alzheimer's disease (AD) and neuroimaging biomarkers of AD. No studies have investigated the relationship of TOMM40'523-APOE ε4 on the structural complexity of the brain in AD individuals. We quantified brain morphology and multiple cortical attributes in individuals with mild cognitive impairment (MCI) and AD, then tested whether APOE ε4 or TOMM40 poly-T genotypes were related to AD morphological biomarkers in cognitively unimpaired (CU) and MCI/AD individuals. We identified several AD-specific phenotypes in brain morphology and found that TOMM40 poly-T short alleles are associated with early, AD-specific brain morphological differences in healthy aging. We observed decreased cortical thickness, sulcal depth, and fractal dimension in CU individuals with the poly-T short alleles. Moreover, in MCI/AD participants, the APOE ε4 (TOMM40 L) individuals had a higher rate of gene-related morphological markers indicative of AD. Our data suggest that TOMM40'523 is associated with early brain structure variations in the precuneus, temporal, and limbic cortices.

Methotrexate-Induced Stroke-Like Syndrome: A Typical Presentation of a Rare Complication.

Methotrexate (MTX) is one of the mainstay drugs used in acute lymphoblastic leukemia (ALL) management; however, it can cause damage to the central nervous system (CNS), typically to the subcortical white matter. Stroke-like syndrome is one particular form of MTX-related neurotoxicity that occurs within 21 days of methotrexate administration (intrathecal or high-dose intravenous treatment). The clinical picture comprehends fluctuating neurological symptoms evoking acute cerebral ischemia or hemorrhage (paresis or paralysis, speech disorders - aphasia and/or dysarthria, altered mental status, and occasionally seizures), with spontaneous resolution in the majority of cases, without other identifiable cause. The typical neuroimage includes areas of restricted diffusion on diffusion-weighted imaging and non-enhancing T2 hyper-intense lesions in the white matter, on brain MRI. We report a 12-year-old boy with low-risk B-ALL without CNS involvement, who presented to the emergency department with complaints of sudden paresis of the four limbs (more severe on the right side), aphasia, and confusion. He had received one dose of intrathecal MTX 11 days prior to this episode. An angio-MRI of the brain revealed bilateral restricted diffusion areas in the centrum semiovale, and symptoms fluctuated until complete neurological recovery without any medical intervention, which is very suggestive of MTX-related neurotoxicity. This case illustrates a rare complication of MTX administration that presented with typical clinical and radiological characteristics, in an adolescent with hematological malignancy who experienced swift and full neurological recovery.

Trait and state-related characteristics of thalamo-cortical circuit disruption in bipolar disorder: a prospective cross-sectional study.

Objective: The purpose of this study is to investigate the shared and distinct thalamic-cortical circuit between bipolar depression and remission, as well as to investigate the trait and state-related characteristics of the abnormal thalamic-cortical circuit in bipolar disorder. Methods: Resting-state functional magnetic resonance imaging was performed on 38 bipolar depression patients, 40 bipolar remission patients, and 39 gender-matched healthy controls (rsfMRI). The thalamic subregions were used as seed points to draw the functional connectivity of the entire brain, and then the shared and distinct thalamic-cortical circuits between bipolar depression and remission were compared. Results: When compared to the healthy group, both groups of patients had significantly lower functional connectivity between the rostral temporal thalamus and the lingual gyrus, the posterior parietal thalamus, the precuneus/cerebellum, and the occipital thalamus and the precuneus; however, functional connectivity between the premotor thalamus and the superior medial frontal was significantly lower in depression. Conclusion: This study discovered that both bipolar depression and remission had abnormal sensorimotor-thalamic functional connectivity, implying that it is a trait-related characteristic of bipolar disorder; however, the decline in prefrontal-thalamic connectivity exists specifically in bipolar depression, implying that it is a state-related characteristic of bipolar disorder.

Rheumatoid meningitis: a rare neurological complication of rheumatoid arthritis.

Objective: To describe the clinical and neuroimaging characteristics of rheumatoid meningitis (RM) in Chinese patients. Methods: The patients admitted to our hospital with the diagnosis of RM in the past 8 years were retrospectively analyzed. Results: Six patients with RM were identified among 933 patients admitted with rheumatoid arthritis (RA). The symptoms of meningitis occurred after onset of arthritis in five patients and before onset in one. Headache (n=6), hyperacute focal neurological deficits (n=4) and seizures (n=3) were the most prevalent symptoms. The nadir modified Rankin Scale score was ≥3 in five patients. Rheumatoid factor was elevated in all patients, and interleukin-6 levels in cerebrospinal fluid were dramatically elevated in three of four tested patients. Magnetic resonance imaging of the brain revealed that the meninges were affected in all patients and the cerebral parenchyma was affected in one patient. The lesions were generally located in the frontoparietal region and showed restricted diffusion along the adjacent subarachnoid space. RM occurred during disease-modifying therapy in four patients. In the acute episode, three patients improved on tocilizumab and the other three improved on pulse corticosteroids. For maintenance therapy, two patients received combined therapy of tocilizumab and other immunosuppressive agents, one received adalimumab and methotrexate, and two received low-dose oral corticosteroids with an immunosuppressive agent. Five patients had a good outcome, and one died of Pneumocystis jirovecii pneumonia after stabilization of his neurologic conditions. No relapse of RM occurred on immunotherapy during follow-up. Conclusions: Chinese patients with RM share some remarkable clinical and neuroimaging features and respond well to appropriate immunotherapy. Tocilizumab could be a treatment option for this severe complication of RA.

Graphic Intelligent Diagnosis of Hypoxic-Ischemic Encephalopathy Using MRI-Based Deep Learning Model.

INTRODUCTION: Heterogeneous MRI manifestations restrict the efficiency and consistency of neuroradiologists in diagnosing hypoxic-ischemic encephalopathy (HIE) due to complex injury patterns. This study aimed to develop and validate an intelligent HIE identification model (termed as DLCRN, deep learning clinical-radiomics nomogram) based on conventional structural MRI and clinical characteristics. METHODS: In this retrospective case-control study, full-term neonates with HIE and healthy controls were collected in two different medical centers from January 2015 to December 2020. Multivariable logistic regression analysis was implemented to establish the DLCRN model based on conventional MRI sequences and clinical characteristics. Discrimination, calibration, and clinical applicability were used to evaluate the model in the training and validation cohorts. Grad-class activation map algorithm was implemented to visualize the DLCRN. RESULTS: 186 HIE patients and 219 healthy controls were assigned to the training, internal validation, and independent validation cohorts. Birthweight was incorporated with deep radiomics signatures to create the final DLCRN model. The DLCRN model achieved better discriminatory power than simple radiomics models, with an area under the curve (AUC) of 0.868, 0.813, and 0.798 in the training, internal validation, and independent validation cohorts, respectively. The DLCRN model was well calibrated and has clinical potential. Visualization of the DLCRN highlighted the lesion areas that conformed to radiological identification. CONCLUSION: Visualized DLCRN may be a useful tool in the objective and quantitative identification of HIE. Scientific application of the optimized DLCRN model may save time for screening early mild HIE, improve the consistency of HIE diagnosis, and guide timely clinical management.

Eight-week antidepressant treatment changes intrinsic functional brain topology in first-episode drug-naïve patients with major depressive disorder.

BACKGROUND: A recent study revealed disrupted topological organization of whole-brain networks in patients with major depressive disorder (MDD); however, these results were mostly driven by recurrent MDD patients, rather than first-episode drug-naïve (FEDN) patients. Furthermore, few longitudinal studies have explored the effects of antidepressant therapy on the topological organization of whole-brain networks. METHODS: We collected clinical and neuroimaging data from 159 FEDN MDD patients and 152 normal controls (NCs). A total of 115 MDD patients completed an eight-week antidepressant treatment procedure. Topological features of brain networks were calculated using graph theory-based methods and compared between FEDN MDD patients and NCs, as well as before and after treatment. RESULTS: Decreased global efficiency, local efficiency, small-worldness, and modularity were found in pretreatment FEDN MDD patients compared with NCs. Nodal degrees, betweenness, and efficiency decreased in several networks compared with NCs. After antidepressant treatment, the global efficiency increased, while the local efficiency, the clustering coefficient of the network, the path length, and the normalized characteristic path length decreased. Moreover, the reduction rate of the normalized characteristic path length was positively correlated with the reduction rate of retardation factor scores. LIMITATIONS: The interaction effects of groups and time on the topological features were not explored because of absence of the eighth-week data of NC group. CONCLUSIONS: The topological architecture of functional brain networks is disrupted in FEDN MDD patients. After antidepressant therapy, the global efficiency shifted toward recovery, but the local efficiency deteriorated, suggesting a correlation between recovery of retardation symptoms and global efficiency.

Roadmap for an imaging and modelling paediatric study in rural NZ.

Our study methodology is motivated from three disparate needs: one, imaging studies have existed in silo and study organs but not across organ systems; two, there are gaps in our understanding of paediatric structure and function; three, lack of representative data in New Zealand. Our research aims to address these issues in part, through the combination of magnetic resonance imaging, advanced image processing algorithms and computational modelling. Our study demonstrated the need to take an organ-system approach and scan multiple organs on the same child. We have pilot tested an imaging protocol to be minimally disruptive to the children and demonstrated state-of-the-art image processing and personalized computational models using the imaging data. Our imaging protocol spans brain, lungs, heart, muscle, bones, abdominal and vascular systems. Our initial set of results demonstrated child-specific measurements on one dataset. This work is novel and interesting as we have run multiple computational physiology workflows to generate personalized computational models. Our proposed work is the first step towards achieving the integration of imaging and modelling improving our understanding of the human body in paediatric health and disease.

Medication overuse headache and substance use disorder: A comparison based on basic research and neuroimaging.

It has yet to be determined whether medication overuse headache (MOH) is an independent disorder or a combination of primary headache and substance addiction. To further explore the causes of MOH, we compared MOH with substance use disorder (SUD) in terms of the brain regions involved to draw more targeted conclusions. In this review, we selected alcohol use disorder (AUD) as a representative SUD and compared MOH and AUD from two aspects of neuroimaging and basic research. We found that in neuroimaging studies, there were many overlaps between AUD and MOH in the reward circuit, but the extensive cerebral cortex damage in AUD was more serious than that in MOH. This difference was considered to reflect the sensitivity of the cortex structure to alcohol damage. In future research, we will focus on the central amygdala (CeA), prefrontal cortex (PFC), orbital-frontal cortex (OFC), hippocampus, and other brain regions for interventions, which may have unexpected benefits for addiction and headache symptoms in MOH patients.