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BACKGROUND: Existing neuroimaging techniques, including magnetic resonance imaging, computed tomography, and high-resolution ultrasound, lack the capability to provide real-time intraoperative positioning images to surgeons. However, the clinical implementation of near-infrared fluorescence imaging technology has made it possible to directly visualize surgical target areas, offering a novel solution for real-time nerve identification during surgery. OBJECTIVE: To provide a summary and overview of the research progress in near-infrared fluorescence imaging technology for intraoperative neuroimaging. METHODS: The first author used the computer to search for the documents published from January 2010 to July 2023 in WanFang, CNKI, and PubMed with the key words of “near-infrared fluorescence imaging, optical imaging, nerve imaging” in Chinese and English. A few classic old documents were also included. Initial screening was performed by reading the titles and abstracts; duplicate, low-quality, and irrelevant content documents were excluded. A total of 69 articles were finally included for review. RESULTS AND CONCLUSION: Near-infrared fluorescence imaging guided by indocyanine green has been clinically used to identify and locate tubular organs such as blood vessels, ureters, and bile ducts, as well as various tumors during surgery. This technique is currently considered a well-established imaging method in precision surgery. In the field of intraoperative neurofluorescence imaging, indocyanine green is currently the only near-infrared fluorescent dye used in clinical research. The ideal neuroimaging agent should possess certain characteristics, including easy administration in the perioperative period, logD between 0.5 and 3 at pH=7.4, molecular mass below 500 Da, excitation and emission wavelengths within the near-infrared window, long-term retention in nerve tissue, high signal-to-background ratio, and high safety. In the future, the development of near-infrared neurofluorescence imaging agents should focus on synthesizing complexes of indocyanine green and neural-specific targets. This technology not only enables intraoperative neurofluorescence imaging, but also holds promising prospects for in-situ monitoring of nerve regeneration and diagnosis of neurological diseases. [ABSTRACT FROM AUTHOR]

This review systematically analyzes potential biomarker candidates for post‐traumatic epilepsy (PTE) in humans who have experienced moderate to severe traumatic brain injury (TBI). Focusing on biomarkers across biofluid‐based protein, genetic, neuroimaging, and neurophysiological categories, this review distinguishes between TBI patients who develop PTE and those who do not. The review adheres to established methodologies outlined in the Cochrane Handbook for Systematic Reviews of Interventions. Data presentation follows the Meta‐analyses of Observational Studies (MOOSE) and Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines. Medline, Embase, and Web of Science were systematically searched and yielded 7538 records, of which 18 met inclusion criteria (moderate–severe TBI in humans, follow‐up of at least 6 months, and no prior history of epilepsy). The review aggregates data from 15 cohort and 3 case–control studies (risk of bias was assessed using the Newcastle‐Ottawa Scale). Statistically significant biomarkers were identified, with neurophysiological biomarkers showing the strongest effect size in a two‐study meta‐analysis. PTE, a severe long‐term outcome of TBI affecting 2% to 53% of individuals with TBI, lacks validated biomarkers for forecasting development, crucial for designing preventive clinical trials. A multimodal approach, integrating biofluid‐based protein, genetic, neuroimaging, and neurophysiological data, offers a promising strategy to enhance the predictability of PTE development and, potentially, its treatment. The study's protocol is registered in the International Prospective Register of Systematic Reviews PROSPERO (Registration ID: CRD42023470245). [ABSTRACT FROM AUTHOR]

Background Objective Methods Results Conclusion Understanding the neural mechanisms underlying migraine and other primary headache disorders is critical for the development of long‐term cures. Magnetoencephalography (MEG), an imaging modality that measures neuronal currents and cortical excitability with high temporal and superior spatial resolution, has been increasingly used in neurological research. Initial MEG studies showed promise in directly recording cortical spreading depression—a cortical correlate of migraine with aura. However, lately MEG technology has highly evolved with greater potential to reveal underlying pathophysiology of migraine and primary headache disorders, and aid in the identification of biomarkers.To systematically review the use of MEG in migraine and other primary headache disorders and summarize findings.We conducted a systematic search and selection of MEG studies in migraine and primary headache disorders from inception until June 8, 2023, in Medline, Embase, Cochrane, and Scopus databases. Peer‐reviewed English articles reporting the use of MEG for clinical or research purposes in migraine and primary headache disorders were selected.We found 560 articles and included 38 in this review after screening. Twelve studies investigated resting‐state, while others investigated a sensory modality using an evoked or event‐related paradigm with a total of 35 cohort and 3 case studies. Thirty‐two studies focused exclusively on migraine, while the rest reported other primary headache disorders.The findings show an evolution of MEG from a 7‐ to a 306‐channel system and analysis evolving from sensor‐level evoked responses to more advanced source‐level connectivity measures. A relatively few MEG studies portrayed migraine and primary headache disorders as a sensory abnormality, especially of the visual system. We found heterogeneity in the datasets, data reporting standards (due to constantly evolving MEG technology and analysis methods), and patient characteristics. Studies were inadequately powered and there was no evidence of blinding procedures to avoid selection bias in case–control studies, which could have led to false‐positive findings. More studies are needed to investigate the affective–cognitive aspects that exacerbate pain and disability in migraine and primary headache disorders. [ABSTRACT FROM AUTHOR]

The isocitrate dehydrogenase (IDH) mutation status is one of the most important markers according to the 2021 WHO classification of CNS tumors. Preoperatively, this information is usually obtained based on invasive biopsies, contrast-enhanced MR images or PET images generated using radioactive tracers. However, the completely non-invasive determination of IDH mutation status using routinely acquired preoperative native CT images has hardly been investigated to date. In our study, we show that radiomics-based machine learning allows to determine IDH mutation status based on preoperative native CT images both with very high accuracy and completely non-invasively. Based on independent test data, we are able to correctly identify 91.1% of cases with an IDH mutation. Our final model, containing only six features, exhibits a high area under the curve of 0.847 and an excellent area under the precision-recall curve of 0.945. In the future, such models may be used for a completely non-invasive prediction of important genetic markers, potentially allowing treating physicians to reduce the number of biopsies and speed up further treatment planning. [ABSTRACT FROM AUTHOR]

Aim Methods Results Conclusion Immune dysregulation and delayed onset of sleep wake cycling (SWC) are associated with worse outcome in neonatal encephalopathy (NE), however the association between sleep and immune dysfunction in NE remains unclear. Aimed to evaluate association of sleep and systemic inflammation with outcomes in NE.Amplitude‐integrated electroencephalography (aEEG) recordings were collected on infants undergoing therapeutic hypothermia (TH). Duration to onset of (SWC) and sleep quality (SQ) were examined. Blood samples collected during the first 2 days of life. Thirteen pro‐ and anti‐inflammatory serum cytokines were quantified. Adverse outcome defined as death or abnormal MRI brain.Earlier onset of SWC and better SQ had less adverse outcomes. SQ provided better prognostic value and showed better interobserver agreement compared to duration to SWC. Better SQ associated with lower cytokines EPO and interleukin (IL)‐1β. In infants with unfavourable outcome, shorter duration to SWC was associated with higher EPO and better SQ was associated with lower TNF‐α.Earlier onset of SWC or better SQ showed less systemic inflammation and fewer adverse outcomes. SQ during TH provided better prognostic information than time of onset of SWC. Modulation of circadian rhythm in infants with NE may have an immunomodulatory role, leading to improved outcomes. [ABSTRACT FROM AUTHOR]

Background: Freezing of gait is one of the most disturbing motor symptoms of Parkinson's disease (PD). However, the effective connectivity between key brain hubs that are associated with the pathophysiological mechanism of freezing of gait remains elusive. Objective: The aim of this study was to identify effective connectivity underlying freezing of gait. Methods: This study applied spectral dynamic causal modeling (DCM) of resting‐state functional magnetic resonance imaging in dedicated regions of interest determined using a data‐driven approach. Results: Abnormally increased functional connectivity between the bilateral dorsolateral prefrontal cortex (DLPFC) and the bilateral mesencephalic locomotor region (MLR) was identified in freezers compared with nonfreezers. Subsequently, spectral DCM analysis revealed that increased top‐down excitatory effective connectivity from the left DLPFC to bilateral MLR and an independent self‐inhibitory connectivity within the left DLPFC in freezers versus nonfreezers (>99% posterior probability) were inversely associated with the severity of freezing of gait. The lateralization of these effective connectivity patterns was not attributable to the initial dopaminergic deficit nor to structural changes in these regions. Conclusions: We have identified novel effective connectivity and an independent self‐inhibitory connectivity underlying freezing of gait. Our findings imply that modulating the effective connectivity between the left DLPFC and MLR through neurostimulation or other interventions could be a target for reducing freezing of gait in PD. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. [ABSTRACT FROM AUTHOR]

Declarative memory encompasses episodic and semantic divisions. Episodic memory captures singular events with specific spatiotemporal relationships, whereas semantic memory houses context-independent knowledge. Behavioural and functional neuroimaging studies have revealed common and distinct neural substrates of both memory systems, implicating mesiotemporal lobe (MTL) regions such as the hippocampus and distributed neocortices. Here, we explored declarative memory system reorganization in patients with unilateral temporal lobe epilepsy (TLE) as a human disease model to test the impact of variable degrees of MTL pathology on memory function. Our cohort included 31 patients with TLE and 60 age- and sex-matched healthy controls, and all participants underwent episodic and semantic retrieval tasks during a multimodal MRI session. The functional MRI tasks were closely matched in terms of stimuli and trial design. Capitalizing on non-linear connectome gradient-mapping techniques, we derived task-based functional topographies during episodic and semantic memory states, in both the MTL and neocortical networks. Comparing neocortical and hippocampal functional gradients between TLE patients and healthy controls, we observed a marked topographic reorganization of both neocortical and MTL systems during episodic memory states. Neocortical alterations were characterized by reduced functional differentiation in TLE across lateral temporal and midline parietal cortices in both hemispheres. In the MTL, in contrast, patients presented with a more marked functional differentiation of posterior and anterior hippocampal segments ipsilateral to the seizure focus and pathological core, indicating perturbed intrahippocampal connectivity. Semantic memory reorganization was also found in bilateral lateral temporal and ipsilateral angular regions, whereas hippocampal functional topographies were unaffected. Furthermore, leveraging MRI proxies of MTL pathology, we observed alterations in hippocampal microstructure and morphology that were associated with TLE-related functional reorganization during episodic memory. Moreover, correlation analysis and statistical mediation models revealed that these functional alterations contributed to behavioural deficits in episodic memory, but again not in semantic memory in patients. Altogether, our findings suggest that semantic processes rely on distributed neocortical networks, whereas episodic processes are supported by a network involving both the hippocampus and the neocortex. Alterations of such networks can provide a compact signature of state-dependent reorganization in conditions associated with MTL damage, such as TLE. [ABSTRACT FROM AUTHOR]

The neuroimaging basis of intelligence remains elusive; however, there is a growing body of research employing connectome‐based predictive modeling to estimate individual intelligence scores, aiming to identify the optimal set of neuroimaging features for accurately predicting an individual's cognitive abilities. Compared to adults, the disparities in cognitive performance among children and adolescents are more likely to captivate individuals' interest and attention. Limited research has been dedicated to exploring neuroimaging markers of intelligence specifically in the pediatric population. In this study, we utilized resting‐state functional magnetic resonance imaging (fMRI) and intelligence quotient (IQ) scores of 170 healthy children and adolescents obtained from a public database to identify brain functional connectivity markers associated with individual intellectual behavior. Initially, we extracted and summarized relevant resting‐state features from whole‐brain or functional network connectivity that were most pertinent to IQ scores. Subsequently, these features were employed to establish prediction models for both performance and verbal IQ scores. Within a 10‐fold cross‐validation framework, our findings revealed that prediction models based on whole‐brain functional connectivity effectively predicted performance IQ scores(R=0.35,P=2.2×10−4) but not verbal IQ scores(R=0.12,P=0.20). Results of prediction models based on brain functional network connectivity further demonstrated the exceptional predictive ability of the default mode network (DMN) and fronto‐parietal task control network (FTPN) for performance IQ scores (R=0.71,P=2.2×10−18). The above findings have also been validated using an independent dataset. Our findings suggest that the performance IQ of children and adolescents primarily relies on the connectivity of brain regions associated with DMN and FTPN. Moreover, variations in intellectual performance during childhood and adolescences are closely linked to alterations in brain functional network connectivity. Our findings suggest that the performance IQ of children and adolescents primarily relies on the connectivity of brain regions associated with DMN and FTPN. Moreover, variations in intellectual performance during childhood and adolescences are closely linked to alterations in brain functional network connectivity. [ABSTRACT FROM AUTHOR]

Background/Objectives: Huntington's disease (HD) is a fully penetrant neurodegenerative disease with a profound effect on quality of life. In recent years, there has been rapid growth in the description of its pathogenesis and diagnosis. Magnetic resonance spectroscopy (MRS) measurements can aid in the discrimination between premanifest Huntington's disease (Pre-HD) and healthy control (HC) subjects to establish early supportive and symptomatic management. Our objective was to evaluate metabolic changes using MRS to shed light on its potential as a biomarker through a systematic review. Methods: We followed the PRISMA guidelines, extracting articles from PubMed, Scopus, and the Virtual Health Library. We included patients with pre-HD, HD, and HC subjected to MRS, reporting the concentration of metabolites in at least one brain region. Results: In the putamen, N-acetyl Aspartate (NAA) was significantly decreased in 77.9% and total NAA (tNAA) was decreased in 72.4% of cases; no significant difference was found in 27.5% (n = 19) of cases. Furthermore, when looking into HD vs. pre-HD in the putamen, tNAA and NAA were decreased in 100% of participants. In the caudate nucleus, NAA and creatine were significantly decreased in 100% of HD in comparison to pre-HD participants, whereas tNAA showed a significant decrease in only 50%. Conclusions: MRS can be a relevant tool for the early diagnosis of HD; potential objective biomarkers related to its onset and pathogenesis exist and show differences between controls, pre-HD and HD patients. However, an effort should be made to standardize MRS methodology and reporting in subsequent studies. [ABSTRACT FROM AUTHOR]

Functional near-infrared spectroscopy (fNIRS) is beneficial for studying brain activity in naturalistic settings due to its tolerance for movement. However, residual motion artifacts still compromise fNIRS data quality and might lead to spurious results. Although some motion artifact correction algorithms have been proposed in the literature, their development and accurate evaluation have been challenged by the lack of ground truth information. This is because ground truth information is time- and labor-intensive to manually annotate. This work investigates the feasibility and reliability of a deep learning computer vision (CV) approach for automated detection and annotation of head movements from video recordings. Fifteen participants performed controlled head movements across three main rotational axes (head up/down, head left/right, bend left/right) at two speeds (fast and slow), and in different ways (half, complete, repeated movement). Sessions were video recorded and head movement information was obtained using a CV approach. A 1-dimensional UNet model (1D-UNet) that detects head movements from head orientation signals extracted via a pre-trained model (SynergyNet) was implemented. Movements were manually annotated as a ground truth for model evaluation. The model's performance was evaluated using the Jaccard index. The model showed comparable performance between the training and test sets (J train = 0.954; J test = 0.865). Moreover, it demonstrated good and consistent performance at annotating movement across movement axes and speeds. However, performance varied by movement type, with the best results being obtained for repeated (J test = 0.941), followed by complete (J test = 0.872), and then half movements (J test = 0.826). This study suggests that the proposed CV approach provides accurate ground truth movement information. Future research can rely on this CV approach to evaluate and improve fNIRS motion artifact correction algorithms. [ABSTRACT FROM AUTHOR]

Background: The aim of this review is to highlight the new advance of predictive and explainable artificial intelligence for neuroimaging applications. Methods: Data came from 30 original studies in PubMed with the following search terms: "neuroimaging" (title) together with "machine learning" (title) or "deep learning" (title). The 30 original studies were eligible according to the following criteria: the participants with the dependent variable of brain image or associated disease; the interventions/comparisons of artificial intelligence; the outcomes of accuracy, the area under the curve (AUC), and/or variable importance; the publication year of 2019 or later; and the publication language of English. Results: The performance outcomes reported were within 58–96 for accuracy (%), 66–97 for sensitivity (%), 76–98 for specificity (%), and 70–98 for the AUC (%). The support vector machine and the convolutional neural network registered the best performance (AUC 98%) for the classifications of low- vs. high-grade glioma and brain conditions, respectively. Likewise, the random forest delivered the best performance (root mean square error 1) for the regression of brain conditions. The following factors were discovered to be major predictors of brain image or associated disease: (demographic) age, education, sex; (health-related) alpha desynchronization, Alzheimer's disease stage, CD4, depression, distress, mild behavioral impairment, RNA sequencing; (neuroimaging) abnormal amyloid-β, amplitude of low-frequency fluctuation, cortical thickness, functional connectivity, fractal dimension measure, gray matter volume, left amygdala activity, left hippocampal volume, plasma neurofilament light, right cerebellum, regional homogeneity, right middle occipital gyrus, surface area, sub-cortical volume. Conclusion: Predictive and explainable artificial intelligence provide an effective, non-invasive decision support system for neuroimaging applications. [ABSTRACT FROM AUTHOR]

Cardiac arrest (CA) is one of the leading causes of death worldwide. Due to hypoxic ischemic brain injury, CA survivors may experience variable degrees of neurological dysfunction. This study, for the first time, describes the progression of CA-induced neuropathology in the rat. CA rats displayed neurological and exploratory deficits. Brain MRI revealed cortical and striatal edema at 3 days (d), white matter (WM) damage in corpus callosum (CC), external capsule (EC), internal capsule (IC) at d7 and d14. At d3 a brain edema significantly correlated with neurological score. Parallel neuropathological studies showed neurodegeneration, reduced neuronal density in CA1 and hilus of hippocampus at d7 and d14, with cells dying at d3 in hilus. Microgliosis increased in cortex (Cx), caudate putamen (Cpu), CA1, CC, and EC up to d14. Astrogliosis increased earlier (d3 to d7) in Cx, Cpu, CC and EC compared to CA1 (d7 to d14). Plasma levels of neurofilament light (NfL) increased at d3 and remained elevated up to d14. NfL levels at d7 correlated with WM damage. The study shows the consequences up to 14d after CA in rats, introducing clinically relevant parameters such as advanced neuroimaging and blood biomarker useful to test therapeutic interventions in this model. [ABSTRACT FROM AUTHOR]

Background: Progressive supranuclear palsy (PSP) is a progressive atypical parkinsonian condition that results in severe disability. There are few studies of PSP in patients of non‐white European ancestry. Objectives: We aim to perform deep phenotyping in a South Asian PSP cohort to uncover possible ethnic differences in disease characteristics. Methods: Consecutive PSP patients had their clinical records reviewed for clinical features operationalized in the Movement Disorder Society (MDS)‐PSP diagnostic criteria and relevant investigations, including imaging and genetic tests. Clinical variables were summarized by descriptive statistics and Kaplan–Meier curves were generated for survival analysis. Results: Twenty‐seven patients, comprising Indians (78%), Pakistanis (11%) and Sri Lankans (11%) were included. Mean age of symptom onset was 63.8 ± 7.0 years and 22% of patients had an early age of onset (<60 years). The most common presenting symptom was parkinsonism (56%), followed by cognitive dysfunction (37%), falls (33%) and dysarthria (26%). The predominance types at final review were distributed across PSP‐RS (67%), PSP‐PGF (15%), PSP‐P (15%) and PSP‐F (4%). Atypical clinical features like cerebellar signs (33%), REM‐sleep behavior disorder (RBD) (55%), visual hallucinations (22%), and a family history of parkinsonism (20%) were evident in a proportion of patients. Conclusions: We present a South Asian cohort of PSP patients with a higher than previously reported percentages of early‐onset disease, family history and atypical clinical manifestations. These patients do not fit easily into the PSP phenotypes defined by the current MDS criteria. Dedicated clinicopathological and genetic tests are needed in this population to dissect the pathogenesis of clinically‐defined PSP. [ABSTRACT FROM AUTHOR]

Background: Alcohol-related cues are known to influence craving levels, a hallmark of alcohol misuse. Binge drinking (BD), a pattern of heavy alcohol use, has been associated with cognitive and neurofunctional alterations, including alcohol attentional bias, memory impairments, as well as disrupted activity in prefrontal- and reward-related regions. However, literature is yet to explore how memories associated with alcohol-related cues are processed by BDs, and how the recall of this information may influence their reward processing. Aims: The present functional magnetic resonance imaging (fMRI) study aimed to investigate the neurofunctional signatures of BD during an associative memory task. Method: In all, 36 university students, 20 BDs and 16 alcohol abstainers, were asked to memorize neutral objects paired with either alcohol or non-alcohol-related contexts. Subsequently, neutral stimuli were presented, and participants were asked to classify them as being previously paired with alcohol- or non-alcohol-related contexts. Results: While behavioral performance was similar in both groups, during the recall of alcohol-related cues, BDs showed increased brain activation in two clusters including the thalamus, globus pallidus and dorsal striatum, and cerebellum and occipital fusiform gyrus, respectively. Conclusion: These findings suggest that BDs display augmented brain activity in areas responsible for mental imagery and reward processing when trying to recall alcohol-related cues, which might ultimately contribute to alcohol craving, even without being directly exposed to an alcohol-related context. These results highlight the importance of considering how alcohol-related contexts may influence alcohol-seeking behavior and, consequently, the maintenance or increase in alcohol use. [ABSTRACT FROM AUTHOR]

Background: Cannabidiol (CBD) impacts brain regions implicated in anxiety reactivity and stress reactivity (e.g., amygdala, anterior cingulate cortex (ACC), anterior insula (AI)); however, placebo-controlled studies are mixed regarding CBD's anxiolytic effects. We previously reported that CBD expectancy alone can alter subjective, physiological, and endocrine markers of stress/anxiety; however, it is unclear whether these findings reflect altered brain reactivity. This study evaluated whether CBD expectancy independently alters amygdala resting-state functional connectivity (rsFC) with the ACC and AI following acute stress. Method: Thirty-eight (20 females) healthy adults were randomly assigned to receive accurate or inaccurate information regarding the CBD content of a CBD-free oil administered during a single experimental session. Following a baseline resting state MRI scan, participants administered their assigned oil sublingually, engaged in a stress task (serial subtraction with negative feedback) inside the scanner, and underwent another resting state MRI scan. Amygdala rsFC with the ACC and AI was measured during each scan, and the subjective state was assessed at six time points. Outcomes were analyzed using ANCOVA. Results: CBD expectancy (vs CBD-free expectancy) was associated with significantly weaker rsFC between the left amygdala and right ACC (p = 0.042), but did not systematically alter amygdala-AI rsFC (p- values > 0.05). We also replicated our previously reported CBD expectancy effects on subjective stress/anxiety in the scanner context. Conclusion: CBD placebo effects may be sufficient to alter neural responses relevant to its purported anxiolytic and stress-relieving properties. Future work is needed to replicate these results and determine whether CBD expectancy and pharmacology interact to alter neural anxiety reactivity and stress reactivity. [ABSTRACT FROM AUTHOR]

Objective: Previous studies have reported that vitamin D deficiency increased the risk of Alzheimer's disease (AD) dementia in older adults. However, little is known about how vitamin D is involved in the pathophysiology of AD. Thus, this study aimed to examine the association and interaction of serum vitamin D levels with in vivo AD pathologies including cerebral beta-amyloid (Aβ) deposition and neurodegeneration in nondemented older adults. Methods: 428 Nondemented older adults were recruited from the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer's Disease, a prospective cohort that began in 2014. All participants underwent comprehensive clinical assessments, measurement of serum 25-hydroxyvitamin D (25[OH]D), and multimodal brain imaging including Pittsburgh compound B (PiB) positron emission tomography and magnetic resonance imaging. Global PiB deposition was measured for the Aβ biomarker. Intracranial volume-adjusted hippocampal volume (HVa) was used as a neurodegeneration biomarker. Results: Overall, serum 25(OH)D level was not associated with either Aβ deposition or HVa after controlling for age, sex, apolipoprotein E ε4 positivity, and vascular risk factors. However, serum 25(OH)D level had a significant moderating effect on the association between Aβ and neurodegeneration, with lower serum 25(OH)D level significantly exacerbating cerebral Aβ-associated hippocampal volume loss (B = 34.612, p = 0.008). Conclusion: Our findings indicate that lower serum vitamin D levels may contribute to AD by exacerbating Aβ-associated neurodegeneration in nondemented older adults. Further studies to explore the potential therapeutic effect of vitamin D supplementation on the progression of AD pathology will be necessary. [ABSTRACT FROM AUTHOR]

Assessing imaging biomarker in the prodromal and early phases of Parkinson's disease (PD) is of great importance to ensure an early and safe diagnosis. In the last decades, imaging modalities advanced and are now able to assess many different aspects of neurodegeneration in PD. MRI sequences can measure iron content or neuromelanin. Apart from SPECT imaging with Ioflupane, more specific PET tracers to assess degeneration of the dopaminergic system are available. Furthermore, metabolic PET patterns can be used to anticipate a phenoconversion from prodromal PD to manifest PD. In this regard, it is worth mentioning that PET imaging of inflammation will gain significance. Molecular imaging of neurotransmitters like serotonin, noradrenaline and acetylcholine shed more light on non-motor symptoms. Outside of the brain, molecular imaging of the heart and gut is used to measure PD-related degeneration of the autonomous nervous system. Moreover, optical coherence tomography can noninvasively detect degeneration of retinal fibers as a potential biomarker in PD. In this review, we describe these state-of-the-art imaging modalities in early and prodromal PD and point out in how far these techniques can and will be used in the future to pave the way towards a biomarker-based staging of PD. [ABSTRACT FROM AUTHOR]

Brain structure correlates of obsessive-compulsive personality disorder (OCPD) remain poorly understood as limited OCPD assessment has precluded well-powered studies. Here, we tested whether machine learning (ML; elastic net regression, gradient boosting machines, support vector regression with linear and radial kernels) could estimate OCPD scores from personality data and whether ML-predicted scores are associated with indices of brain structure (cortical thickness and surface area and subcortical volumes). Among older adults (ns = 898–1,606) who completed multiple OCPD assessments, ML elastic net regression with Revised NEO Personality Inventory personality items as features best predicted Five-Factor Obsessive-Compulsive Inventory—Short Form (FFOCI-SF) scores, root-mean-squared error (RMSE)/SD = 0.66; performance generalized to a sample of college students (n = 175; RMSE/SD = 0.51). Items from all five-factor model personality traits contributed to predicted FFOCI-SF (p-FFOCI-SF) scores; conscientiousness and openness items were the most influential. In college students (n = 1,253), univariate analyses of cortical thickness, surface area, and subcortical volumes revealed only a positive association between p-FFOCI-SF and right superior frontal gyrus cortical thickness after adjusting for multiple testing (b = 2.21, p =.0014; all other |b|s < 1.04; all other ps >.009). Multivariate ML models of brain features predicting FFOCI, conscientiousness, and neuroticism performed poorly (RMSE/SDs > 1.00). These data reveal that all five-factor model traits contribute to maladaptive OCPD traits and identify greater right superior frontal gyrus cortical thickness as a promising correlate of OCPD for future study. Broadly, this study highlights the utility of ML to estimate unmeasured psychopathology phenotypes in neuroimaging data sets but that our application of ML to neuroimaging may not resolve unreliable associations and small effects characteristic of univariate psychiatric neuroimaging research. General Scientific Summary: Indices of obsessive-compulsive personality disorder (OCPD) and its underlying dimensions can be predicted by applying machine learning to personality data. OCPD traits have small correlations with brain structure; only a positive association with superior frontal gyrus cortical thickness was significant. Multivariate machine learning models using brain features to predict OCPD traits were not robust. [ABSTRACT FROM AUTHOR]

A primary goal of clinical neuroscience is to identify associations between individual differences in psychopathology and the brain. However, despite a significant amount of resources invested in this endeavor, few reliable neural correlates of psychopathology have been identified. A common suspect for this lack of success is the significant heterogeneity in symptoms observed in psychiatric disorders. However, this is not the only potential source of heterogeneity, as substantial heterogeneity is also observed in brain structure and function. Thus, for clinical neuroscience to identify reliable neural correlates of psychopathology, it will be necessary to better understand heterogeneity in both psychopathology and the brain. In this commentary, we suggest four shared principles that can help disentangle heterogeneity in both of these domains: (a) the brain and behavior should both be treated as complex measures, (b) a priori assumptions should be viewed with caution unless they can be replicated robustly in individuals, (c) complex models of individual differences require appropriate data to estimate them, and (d) the field would benefit from an increased focus on extensively measuring individuals, such as through the use of personalized models of psychopathology and neuroimaging data. Together, these shared principles can aid in better characterizing—and separating relevant and irrelevant—heterogeneity in measures of psychopathology and neuroimaging. General Scientific Summary: A primary goal of clinical neuroscience is to identify associations between the brain and psychopathology. To accomplish this goal, the field will have to adopt more comprehensive measures that are reliable within individuals, such as personalized models. Otherwise, clinical neuroscience will continue to be plagued by small effect sizes with limited clinical utility. [ABSTRACT FROM AUTHOR]

Clinical heterogeneity is a significant factor to contend with when seeking to organize, understand, and treat psychopathology. In recent years, the field has prioritized efforts to minimize nonmeaningful heterogeneity and leverage meaningful heterogeneity to improve assessment and diagnostics, inform mechanistic understanding, and facilitate the development of novel treatments. Indeed, exciting developments such as the National Institute for Mental Health Research Domain Criteria and the Hierarchical Taxonomy of Psychopathology have provided powerful frameworks for facing clinical complexity. While these developments have spurred many advancements, the movement has yet to effectively harness the tremendous potential provided by the brain. Initial work incorporating brain data has focused on validating clinical observations with a biomarker rather than leveraging the brain to provide unique insight into meaningful clinical heterogeneity. To provide future guidance and examples of innovation in the area, we solicited articles from teams seeking to utilize brain research to manage clinical heterogeneity. The search resulted in a diverse illustration of how best to leverage brain data to greater mechanistic understanding and clinical utility. In this introduction, we consider this work and discuss strategies through which brain data can best be used to provide unique insight into clinical heterogeneity. As the science of psychopathology continues to grapple with the promise and costs inherent in utilizing this powerful and complex array of methodologies, it will be important to leverage unique insights from brain science. This special issue provides a useful guide for new and upcoming work and a catalyst for moving the field forward. General Scientific Summary: Clinical heterogeneity presents a significant scientific barrier to identifying mechanisms within disorders, within symptom dimensions across disorders, and in risk or etiology of a disorder, but advancements in brain research have significant potential to supplement and improve ongoing efforts to address heterogeneity. [ABSTRACT FROM AUTHOR]

Naltrexone, an opioid antagonist that blocks the reinforcing properties of opioid agonists, is often prescribed to preclude relapse to opioid use disorder (OUD) following detoxification. However, few laboratory studies have directly investigated the ability of naltrexone to alter relapse-inducing effects of opioid agonists, including their priming strength in reinstatement studies and their impact in brain regions known to be involved in drug-induced reinforcement in MRI studies. Here we directly address this issue by investigating the effects of continuous exposure to naltrexone on 1) fentanyl-induced reinstatement of drug-seeking behavior, 2) fentanyl-induced patterns of blood oxygenation level dependent (BOLD) activation in the nucleus accumbens (NAcc), and 3) fentanyl-induced changes in NAcc functional connectivity (FC) in awake non-human primates that are engaged in ongoing opioid self-administration studies. We found that naltrexone antagonizes the priming strength of fentanyl as shown by a rightward shift in its reinstatement dose–effect curve and that naltrexone surmountably antagonizes the BOLD response induced by fentanyl. However, while naltrexone also countered fentanyl's effects on NAcc FC, the effects were not surmounted by a higher dose of fentanyl. Together, these data suggest that, in contrast to naltrexone's modulation of fentanyl's effects on behavior and BOLD responses, their interactive effects on FC between multiple brain regions do not reflect their receptor-mediated activity. Additionally, we demonstrated opposing effects in the absence and presence of naltrexone on NAcc FC at baseline (i.e., in the absence of any fentanyl prime) suggesting that naltrexone alters FC at baseline, even though naltrexone appears behaviorally silent in the absence of an agonist prime. Together these data provide additional insight into ways in which naltrexone interacts with opioid agonists, both behaviorally and in the brain. Further understanding the effects of opioid agonists on patterns of FC could help elucidate our understanding of the neural processes that contribute to the initiation of and relapse to opioid-seeking behavior in OUD. [ABSTRACT FROM AUTHOR]

Psychological instruments that are employed to adequately explain treatment compliance and recidivism of intimate partner violence (IPV) perpetrators present a limited ability and certain biases. Therefore, it becomes necessary to incorporate new techniques, such as magnetic resonance imaging (MRI), to be able to surpass those limitations and measure central nervous system characteristics to explain dropout (premature abandonment of intervention) and recidivism. The main objectives of this study were: 1) to assess whether IPV perpetrators (n = 60) showed differences in terms of their brain's regional gray matter volume (GMV) when compared to a control group of non-violent men (n = 57); 2) to analyze whether the regional GMV of IPV perpetrators before starting a tailored intervention program explain treatment compliance (dropout) and recidivism rate. IPV perpetrators presented increased GMV in the cerebellum and the occipital, temporal, and subcortical brain regions compared to controls. There were also bilateral differences in the occipital pole and subcortical structures (thalamus, and putamen), with IPV perpetrators presenting reduced GMV in the above-mentioned brain regions compared to controls. Moreover, while a reduced GMV of the left pallidum explained dropout, a considerable number of frontal, temporal, parietal, occipital, subcortical and limbic regions added to dropout to explain recidivism. Our study found that certain brain structures not only distinguished IPV perpetrators from controls but also played a role in explaining dropout and recidivism. Given the multifactorial nature of IPV perpetration, it is crucial to combine neuroimaging techniques with other psychological instruments to effectively create risk profiles of IPV perpetrators. [ABSTRACT FROM AUTHOR]

Cases of isolated spinal cord ischemia resulting in symptoms in neonates are rare, and there are even fewer reported cases in atraumatic births. We present a case of a presumed isolated cervical cord ischemic injury, discuss differentials to consider when evaluating a neonatal spinal cord injury, and highlight the difficulties of diagnosing a spinal cord infarction. [ABSTRACT FROM AUTHOR]

Background and Aims: The brain age gap (BAG), calculated as the difference between a machine learning model‐based predicted brain age and chronological age, has been increasingly investigated in psychiatric disorders. Tobacco and alcohol use are associated with increased BAG; however, no studies have compared global and regional BAG across substances other than alcohol and tobacco. This study aimed to compare global and regional estimates of brain age in individuals with substance use disorders and healthy controls. Design: This was a cross‐sectional study. Setting: This is an Enhancing Neuro Imaging through Meta‐Analysis Consortium (ENIGMA) Addiction Working Group study including data from 38 global sites. Participants: This study included 2606 participants, of whom 1725 were cases with a substance use disorder and 881 healthy controls. Measurements: This study used the Kaufmann brain age prediction algorithms to generate global and regional brain age estimates using T1 weighted magnetic resonance imaging (MRI) scans. We used linear mixed effects models to compare global and regional (FreeSurfer lobestrict output) BAG (i.e. predicted minus chronological age) between individuals with one of five primary substance use disorders as well as healthy controls. Findings Alcohol use disorder (β = −5.49, t = −5.51, p < 0.001) was associated with higher global BAG, whereas amphetamine‐type stimulant use disorder (β = 3.44, t = 2.42, p = 0.02) was associated with lower global BAG in the separate substance‐specific models. Conclusions: People with alcohol use disorder appear to have a higher brain‐age gap than people without alcohol use disorder, which is consistent with other evidence of the negative impact of alcohol on the brain. [ABSTRACT FROM AUTHOR]

Objectives: The objectives of this narrative review are to summarize the current state of AI applications in neuroimaging for early Alzheimer's disease (AD) prediction and to highlight the potential of AI techniques in improving early AD diagnosis, prognosis, and management. Methods: We conducted a narrative review of studies using AI techniques applied to neuroimaging data for early AD prediction. We examined single-modality studies using structural MRI and PET imaging, as well as multi-modality studies integrating multiple neuroimaging techniques and biomarkers. Furthermore, they reviewed longitudinal studies that model AD progression and identify individuals at risk of rapid decline. Results: Single-modality studies using structural MRI and PET imaging have demonstrated high accuracy in classifying AD and predicting progression from mild cognitive impairment (MCI) to AD. Multi-modality studies, integrating multiple neuroimaging techniques and biomarkers, have shown improved performance and robustness compared to single-modality approaches. Longitudinal studies have highlighted the value of AI in modeling AD progression and identifying individuals at risk of rapid decline. However, challenges remain in data standardization, model interpretability, generalizability, clinical integration, and ethical considerations. Conclusion: AI techniques applied to neuroimaging data have the potential to improve early AD diagnosis, prognosis, and management. Addressing challenges related to data standardization, model interpretability, generalizability, clinical integration, and ethical considerations is crucial for realizing the full potential of AI in AD research and clinical practice. Collaborative efforts among researchers, clinicians, and regulatory agencies are needed to develop reliable, robust, and ethical AI tools that can benefit AD patients and society. [ABSTRACT FROM AUTHOR]

Introduction/Aims: Myotonia is a key symptom of myotonic dystrophies (DM), and its quantification is challenging. This exploratory study evaluated the utility of tissue Doppler ultrasound (TDU) to assess myotonia in DM. Methods: Twelve DM patients (seven type‐1 DM [DM1] and five type‐2 DM [DM2]) and 20 age‐matched healthy subjects were included in this cross‐sectional study. After measuring cross‐sectional areas of the flexor digitorum superficialis (FDS) and extensor digitorum communis (EDC) muscles in a resting state, muscle contraction/relaxation time, time to peak tissue velocity, peak tissue velocity and velocity gradients of these muscles were measured via TDU while performing forced fist unclenching after fist closure. Additionally, grip strength, Medical Research Council Sum score and patient‐reported myotonia severity scores were assessed. Results: DM1 and DM2 patients had a lower grip strength than healthy subjects (p =.0001/p =.002). Patient‐reported myotonia did not differ between DM1 and DM2 patients. DM1 patients revealed FDS and EDC atrophy compared to DM2 patients and healthy subjects (p =.003/p =.004). TDU revealed prolonged muscle contraction and relaxation times in both DM subtypes, with prolonged time to reach FDS peak relaxation velocity and altered peak FDS relaxation velocity only in DM1 patients (p =.03/p =.003). Peak FDS relaxation velocity correlated inversely with C(C)TG repeat numbers in DM patients. Sensitivity of TDU parameters to detect myotonic dystrophy varied between 50% and 75%, with a specificity of 95%. Discussion: Our exploratory study suggests that TDU could serve as a novel tool to quantify myotonia in DM patients, but larger follow‐up studies are warranted to validate its diagnostic accuracy. [ABSTRACT FROM AUTHOR]

Background: Due to the recent evolution of telecommunications, it is now acknowledged that digital communication provides essential services for remote areas. Teleradiology allows the ability to obtain images at one site, send them over a distance, and view them remotely for diagnostic or consultation purposes. Aim: The highlighted objectives include (a) the added value of the service, (b) user satisfaction, and (c) quality assurance according to global best practices and national quality standards. Methods: This study utilised an eight-part online self-report survey distributed among employees of the Ministry of Health (MOH) who use the national teleradiology platform. The survey sections were designed to gather comprehensive data, including participant demographics, levels of satisfaction with the service, awareness of security measures, communication effectiveness, perceived advantages and disadvantages, quality assurance, technical challenges, IT support, and future perceptions of teleradiology services. Additionally, a total of 212 MRI reports from patients who underwent brain and spine MRI examinations between 2018 and 2020 were collected from the platform to strengthen the analysis. Results: Most survey respondents (78%) were males, with a significant majority (96.2%) affirming that teleradiology sufficiently addresses clinical inquiries. Furthermore, 90% expressed satisfaction with the service, and 93% endorsed the standardization of MR imaging procedures across Ministry of Health (MOH) hospitals. Notably, 92.4% recognised teleradiology as a transformative strategy for healthcare facilities in Saudi Arabia, concurring with its benefits. The analysis of the MRI reports revealed structural inconsistencies; compared with structured templates, the average number of incorporated elements was reduced, and essential elements were frequently absent. Intriguingly, reports delineating normal cases included a higher incidence of clinical impressions relative to those describing abnormalities, yet the latter contained a more comprehensive array of elements. Variability in report composition was correlated with the years of experience of the reporters. Teleradiology users perceived enhancements in the quality of radiological reporting and the daily operational workflow. Nonetheless, certain limitations were identified, necessitating focused improvements by service providers. Conclusion: Despite teleradiology being a subspecialisation, it can reduce the role of local radiologists. Further research is needed on data security, confidentiality, and archiving options, as well as the cost-effectiveness of teleradiology services. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusions Adolescence is a critical developmental period for the study of anorexia nervosa (AN), an illness characterized by extreme restriction of food intake. The maturation of the reward system during adolescence combined with recent neurobiological models of AN led to the hypothesis that early on in illness, restrictive food choices would be associated with activity in nucleus accumbens reward regions, rather than caudate regions identified among adults with AN.Healthy adolescents (HC, n = 41) and adolescents with AN or atypical AN (atypAN, n = 76) completed a Food Choice Task during fMRI scanning. Selection of high‐fat foods and choice‐related activation in nucleus accumbens and anterior caudate regions‐of‐interest (ROIs) were compared between individuals with AN/atypAN and HC. Associations were examined between choice‐related activation and choice preferences among the AN group. Exploratory analyses examined associations between choice‐related activation and psychological assessments among the patient group.Adolescents with AN or atypAN selected fewer high‐fat foods than HC (t = −5.92, p < .001). Counter to predictions, there were no significant group differences in choice‐related activation in the ROIs. Among individuals with AN or atypAN, choice‐related neural activity in the anterior caudate was significantly negatively associated with high‐fat food selections in the task (r = −.32, p = .024). In exploratory analyses, choice‐related anterior caudate activation was positively associated with psychological measures of illness severity among patients (p's < .05, uncorrected).In this large cohort of adolescents with AN/atypAN, there was no evidence of altered reward system engagement during food choice. While there was no group difference in choice‐related caudate activation, the associations with choices and psychological measures continue to suggest that this neural region is implicated in illness. Longitudinal analyses will clarify whether neural variability relates to longer‐term course. [ABSTRACT FROM AUTHOR]

Purpose: The loss of olfactory function is known to occur in patients suffering from (behavioral variant) frontotemporal dementia ((bv)FTD) and Alzheimer's disease (AD), although different pathophysiological mechanisms underpin this clinical symptom in both disorders. This study assessed whether brain metabolism of the olfactory circuit as assessed by positron emission tomography (PET) imaging with 2-[fluorine-18]fluoro-2-deoxy-d-glucose ([18F]-FDG) can distinguish these entities in different subsets of patients. Methods: Patients presenting with cognitive decline were included from a prospectively kept database: (1) bvFTD patients, (2) AD patients and (3) patients with logopenic primary progressive aphasia (PPA). Metabolic rates were calculated for different regions of the olfactory circuit for each subgroup and compared with a cohort of subjects with normal brain metabolism. Additionally, in patients with a logopenic PPA pattern on PET-imaging, statistical parametric mapping (SPM) analysis was performed. Results: The metabolism of subdivisions of the olfactory circuit as assessed by [18F]-FDG PET brain imaging to bvFTD and AD from control subjects resulted in sensitivity/specificity rates of 95/87.5% and 80/83.3%, respectively. A sensitivity/specificity rate of 100/87.5% was achieved when used to differentiate AD from bvFTD. In patients with the PPA pattern on imaging, the underlying cause (either FTD or AD) could be determined with a sensitivity/specificity rate of 88/82%. SPM analysis concurred that different regions of the olfactory circuit were affected in patients suffering from AD PPA or bvFTD PPA. Conclusion: Metabolic dysfunction in the olfactory circuit is different in various neurodegenerative disorders. Further investigation of the correlations between the cerebral metabolism and the mechanisms which drive olfactory dysfunction is needed. [ABSTRACT FROM AUTHOR]

Objective Methods Results Significance High‐field magnetic resonance imaging (MRI) is a standard in the diagnosis of epilepsy. However, high costs and technical barriers have limited adoption in low‐ and middle‐income countries. Even in high‐income nations, many individuals with epilepsy face delays in undergoing MRI. Recent advancements in ultra‐low‐field (ULF) MRI technology, particularly the development of portable scanners, offer a promising solution to the limited accessibility of MRI. In this study, we present and evaluate the imaging capability of ULF MRI in detecting structural abnormalities typically associated with epilepsy and compare it to high‐field MRI at 3 T.Data collection was conducted within 3 consecutive weeks at the University Hospital Bonn. Inclusion criteria were a minimum age of 18 years, diagnosed epilepsy, and clinical high‐field MRI with abnormalities. We used a .064 T Swoop portable MR Imaging System. Both high‐field MRI and ULF MRI scans were evaluated independently by two experienced neuroradiologists as part of their clinical routine, comparing pathology detection and diagnosis completeness.Twenty‐three individuals with epilepsy were recruited. One subject presented with a dual pathology. Across the entire cohort, in 17 of 24 (71%) pathologies, an anomaly colocalizing with the actual lesion was observed on ULF MRI. For 11 of 24 (46%) pathologies, the full diagnosis could be made based on ULF MRI. Tumors and posttraumatic lesions could be diagnosed best on ULF MRI, whereas cortical dysplasia and other focal pathologies were the least well diagnosed.This single‐center series of individuals with epilepsy demonstrates the feasibility and utility of ULF MRI for the field of epileptology. Its integration into epilepsy care offers transformative potential, particularly in resource‐limited settings. Further research is needed to position ULF MRI within imaging modalities in the diagnosis of epilepsy. [ABSTRACT FROM AUTHOR]

Optoacoustic imaging offers label‐free multi‐parametric characterization of cerebrovascular morphology and hemodynamics at depths and spatiotemporal resolution unattainable with optical microscopy. Effective imaging depth can greatly be enhanced by employing photons in the second near‐infrared (NIR‐II) window. However, diminished absorption by hemoglobin along with a lack of suitable contrast agents hinder an efficient application of the technique in this spectral range. Herein, copper sulfide (CuS) micro‐ and nano‐formulations for multi‐scale optoacoustic imaging in the NIR‐II window are introduced. Dynamic contrast enhancement induced by intravenously administered CuS nanoparticles facilitated visualization of blood perfusion in murine cerebrovascular networks. The individual calcium carbonate microparticles carrying CuS are further shown to generate sufficient responses to enable super‐resolution microvascular imaging and blood flow velocity mapping with localization optoacoustic tomography. [ABSTRACT FROM AUTHOR]

We developed a computational pipeline (now provided as a resource) for measuring morphological similarity between cortical surface sulci to construct a sulcal phenotype network (SPN) from each magnetic resonance imaging (MRI) scan in an adult cohort (n = 34,725; 45–82 years). Networks estimated from pairwise similarities of 40 sulci on 5 morphological metrics comprised two clusters of sulci, represented also by the bimodal distribution of sulci on a linear-to-complex dimension. Linear sulci were more heritable and typically located in unimodal cortex, and complex sulci were less heritable and typically located in heteromodal cortex. Aligning these results with an independent fetal brain MRI cohort (n = 228; 21–36 gestational weeks), we found that linear sulci formed earlier, and the earliest and latest-forming sulci had the least between-adult variation. Using high-resolution maps of cortical gene expression, we found that linear sulcation is mechanistically underpinned by trans-sulcal gene expression gradients enriched for developmental processes. [Display omitted] • A pipeline is provided to extract 5 phenotypes for each of 40 sulci from brain MRI • Sulcal phenotype networks consistently find an axis of linear to complex shape • Adult sulcal complexity is linked to heritability, function, and fetal sulcation • Linear sulci demarcate zones of expression for developmentally enriched genes Snyder et al. develop measures of human cortical folding, revealing an axis of linear to complex sulcal shape across the adult cortex. This axis predicts the phased emergence of sulci in utero and boundaries of cortical gene expression, implicating these processes in the formation of mature cortical geometry. [ABSTRACT FROM AUTHOR]

Background: Although numerous studies have shown that bariatric surgery results in sustained weight loss and modifications in gut microbiota composition and cognitive function, the exact underlying mechanisms are unclear. This study aimed to investigate the effects of bariatric surgery on cognitive function through the microbiota-gut-brain axis (MGBA). Methods: Demographic data, serum samples, fecal samples, cognitive assessment scales, and resting-state functional connectivity magnetic resonance imaging (rs-fMRI) scans were obtained from 39 obese patients before and after (6 months) laparoscopic sleeve gastrectomy (LSG). PCA analysis, OPLS-DA analysis, and permutation tests were used to conduct fecal 16 S microbiota profiling, serum metabolomics, and neuroimaging analyses, and a bariatric surgery-specific rs-fMRI brain functional connectivity network was constructed. Spearman correlation analysis and Co-inertia analysis were employed to correlate significant alterations in cognitive assessment scales and resting-state functional connectivity difference networks with differential serum metabolites and 16 S microbiota data to identify key gut microbiota and serum metabolic factors. Results: LSG significantly reduced the weight of obese patients, with reductions of up to 28%. Furthermore, cognitive assessment scale measurements revealed that LSG enhanced cognitive functions, including memory (HVLT, p = 0.000) and executive function (SCWT, p = 0.008). Also, LSG significantly altered gut microbiota composition (p = 0.001), with increased microbial abundance and diversity (p < 0.05). Moreover, serum metabolite levels were significantly altered, revealing intergroup differences in 229 metabolites mapped to 72 metabolic pathways (p < 0.05, VIP > 1). Spearman correlation analysis among cognitive assessment scales, gut microbiota species, and serum metabolites revealed correlations with 68 gut microbiota species and 138 serum metabolites (p < 0.05). Furthermore, pairwise correlations were detected between gut microbiota and serum metabolites (p < 0.05). Functional neuroimaging analysis revealed that LSG increased functional connectivity in cognitive-related frontotemporal networks (FPN, p < 0.01). Additionally, normalization of the default mode network (DMN) and salience network (SN) connectivity was observed after LSG (p < 0.001). Further canonical correlation and correlation analysis suggested that the cognitive-related brain network changes induced by LSG were associated with key gut microbiota species (Akkermansia, Blautia, Collinsella, Phascolarctobacterium, and Ruminococcus, p < 0.05) and neuroactive metabolites (Glycine, L-Serine, DL-Dopa, SM (d18:1/24:1(15Z), p < 0.05). Conclusion: These findings indicate the pathophysiological role of the microbiota-gut-brain axis in enhancing cognitive function after bariatric surgery, and the study provides a basis for clinical dietary adjustments, probiotic supplementation, and guidance for bariatric surgery, but further research is still needed. Trial registration: Chinese Clinical Trial Registry, ChiCTR2100049403. Registered 02 August 2021, https://www.chictr.org.cn/. [ABSTRACT FROM AUTHOR]

Background: Analysis of selected research cohorts has highlighted an association between plasma neurofilament light (NfL) protein and cross-sectional cognitive impairment as well as longitudinal cognitive decline. However, the findings have yielded inconsistent results regarding its possible application in clinical practice. Despite its potential prognostic significance, the role of plasma NfL in daily clinical practice with unselected patients suffering from cognitive impairment remains largely unexplored. Methods: This retrospective, cross-sectional and longitudinal monocentric study enrolled 320 patients with Alzheimer's disease ([AD], n = 158), dementia with Lewy body ([DLB], n = 30), frontotemporal dementia ([FTD], n = 32), non-neurodegenerative diseases ([NND], n = 59) or subjective cognitive decline ([SCD], n = 41). Plasma NfL levels were measured at baseline on the Simoa platform. AD, DLB, and FTD patients were also analyzed altogether as a 'degenerative conditions' subgroup, whereas SCD and NND were grouped as a 'non-degenerative conditions' subgroup. We assessed the relationship between plasma NfL levels and cross-sectional cognitive performance, including global cognition and six specific cognitive domains. A subset of 239 patients had follow-up mini-mental state examinations (MMSE) up to 60 months. Models were adjusted on age, education level, glomerular filtration rate and body mass index. Results: In 320 patients, baseline plasma NfL levels were negatively associated with global cognition (β=-1.28 (-1.81 ; -0.75) P < 0.001), memory (β=-1.48 (-2.38 ; -0.59), P = 0.001), language (β=-1.72(-2.49 ; -0.95) P < 0.001), praxis (β=-2.02 (-2.91 ; -1.13) P < 0.001) and executive functions (β=-0.81, P < 0.001). Across diagnosis, plasma NfL levels were negatively associated with cross-sectional global cognition in all but the SCD subgroup, specifically with executive functions and memory in AD (respectively β=-0.71(-1.21 ; -0.211), P = 0.005 and β=-1.29 (-2.17 ; -0.42), P = 0.004), and with attention in LBD (β=-0.81(-1.16 ; -0.002), P = 0.03). Linear mixed-effects models showed that plasma NfL predicted MMSE decline in the global population (βPlasmaNfLxTime=-0.15 (-0.26 ; -0.04), P = 0.006), as in the neurodegenerative condition subgroup (βPlasmaNfLxTime=-0.21 (-0.37 ; − 0.06), P = 0.007), but not in non-neurodegenerative condition subgroup. Conclusion: In our clinical cohort, plasma NfL was associated with faster cognitive decline in neurodegenerative dementia, which corroborates data obtained in research cohorts. Yet, plasma NfL was not predictive of accelerated cognitive decline in individuals without neurodegeneration, suggesting its use as a neurodegeneration-specific predictive biomarker. [ABSTRACT FROM AUTHOR]

Conclusion As recently as the 1980s, it was not uncommon for paediatric surgeons to operate on infants without anaesthesia. Today, the same omission would be considered criminal malpractice, and there is an increased concern with the possibility of consciousness in the earliest stage of human infancy. This concern reflects a more general trend that has characterised science since the early 1990s of taking consciousness seriously. While this attitude shift has opened minds towards the possibility that our earliest experiences predate our first memories, convincing demonstrations of infant consciousness remain challenging given that infants cannot report on their experiences. Furthermore, while many behavioural and neural markers of consciousness that do not rely on language have been validated in adults, no one specific marker can be confidently translated to infancy. For this reason, we have proposed the ‘cluster‐based’ approach, in which a consensus of evidence across many markers, all pointing towards the same developmental period, could be used to argue convincingly for the presence of consciousness.We review the most promising markers for early consciousness, arguing that consciousness is likely to be in place by 5 months of age if not earlier. [ABSTRACT FROM AUTHOR]

Schizophrenia is a leading cause of global disease burden. Current drug treatments are associated with significant side effects and have limited efficacy for many patients, highlighting the need to develop new approaches that target other aspects of the neurobiology of schizophrenia. Preclinical, in vivo imaging, postmortem, genetic, and pharmacological studies have highlighted the key role of cortical GABAergic (gamma-aminobutyric acidergic)-glutamatergic microcircuits and their projections to subcortical dopaminergic circuits in the pathoetiology of negative, cognitive, and psychotic symptoms. Antipsychotics primarily act downstream of the dopaminergic component of this circuit. However, multiple drugs are currently in development that could target other elements of this circuit to treat schizophrenia. These include drugs for GABAergic or glutamatergic targets, including glycine transporters, D-amino acid oxidase, sodium channels, or potassium channels. Other drugs in development are likely to primarily act on pathways that regulate the dopaminergic system, such as muscarinic or trace amine receptors or 5-HT 2A receptors, while PDE10A inhibitors are being developed to modulate the downstream consequences of dopaminergic dysfunction. Our review considers where new drugs may act on this circuit and their latest clinical trial evidence in terms of indication, efficacy, and side effects. Limitations of the circuit model, including whether there are neurobiologically distinct subgroups of patients, and future directions are also considered. Several drugs based on the mechanisms reviewed have promising clinical data, with the muscarinic agonist KarXT most advanced. If these drugs are approved for clinical use, they have the potential to revolutionize understanding of the pathophysiology and treatment of schizophrenia. [ABSTRACT FROM AUTHOR]

Schizophrenia is a chronic mental illness that affects up to 1% of the population. While efficacious therapies are available for positive symptoms, effective treatment of cognitive and negative symptoms remains an unmet need after decades of research. New developments in the field of neuroimaging are accelerating our knowledge gain regarding the underlying pathophysiology of symptoms in schizophrenia and psychosis spectrum disorders, inspiring new targets for drug development. However, no validated and qualified biomarkers are currently available to support the development of new therapeutics. This review summarizes the current use of neuroimaging technology in clinical drug development for psychotic disorders. As exemplified by drug development programs that target NMDA receptor hypofunction, neuroimaging results play a critical role in target discovery and establishing target engagement and dose selection. Furthermore, pharmacological neuroimaging may provide response biomarkers that allow for early decision making in proof-of-concept studies that leverage pharmacological challenge models in healthy volunteers. That said, while response and predictive biomarkers are starting to be evaluated in patient populations, they continue to play a limited role. Novel approaches to neuroimaging data acquisition and analysis may aid the establishment of biomarkers that are predictive at the individual level in the future. Nevertheless, various gaps in knowledge need to be addressed and biomarkers need to be validated to establish them as "fit for purpose" in drug development. [ABSTRACT FROM AUTHOR]

During adolescence, cannabis experimentation is common, and its association with interindividual variations in brain maturation well studied. Cellular and molecular underpinnings of these system-level relationships are, however, unclear. We thus conducted a three-step study. First, we exposed adolescent male mice to Δ-9-tetrahydrocannabinol (THC) or a synthetic cannabinoid WIN 55,212-2 (WIN) and assessed differentially expressed genes (DEGs), spine numbers, and dendritic complexity in their frontal cortex. Second, in human (male) adolescents, we examined group differences in cortical thickness in 34 brain regions, using magnetic resonance imaging, between those who experimented with cannabis before age 16 (n = 140) and those who did not (n = 327). Finally, we correlated spatially these group differences with gene expression of human homologs of mouse-identified DEGs. The spatial expression of 13 THC-related human homologs of DEGs correlated with cannabis-related variations in cortical thickness, and virtual histology revealed coexpression patterns of these 13 genes with cell-specific markers of astrocytes, microglia, and a type of pyramidal cells enriched in dendrite-regulating genes. Similarly, the spatial expression of 18 WIN-related human homologs of DEGs correlated with group differences in cortical thickness and showed coexpression patterns with the same three cell types. Gene ontology analysis indicated that 37 THC-related human homologs are enriched in neuron projection development, while 33 WIN-related homologs are enriched in processes associated with learning and memory. In mice, we observed spine loss and lower dendritic complexity in pyramidal cells of THC-exposed animals (vs controls). Experimentation with cannabis during adolescence may influence cortical thickness by impacting glutamatergic synapses and dendritic arborization. [ABSTRACT FROM AUTHOR]

Cerebral amyloid angiopathy (CAA) is a common disorder of the elderly, a prominent comorbidity of Alzheimer's disease, and causes vascular cognitive impairment and dementia. Previously, we generated a novel transgenic rat model (rTg-D) that produces human familial CAA Dutch E22Q mutant amyloid β-protein (Aβ) in brain and develops arteriolar CAA type-2. Here, we show that deposition of fibrillar Aβ promotes arteriolar smooth muscle cell loss and cerebral microhemorrhages that can be detected by magnetic resonance imaging and confirmed by histopathology. Aged rTg-D rats also present with cognitive deficits. Cerebral proteomic analyses revealed 241 proteins that were significantly elevated with an increase of >50% in rTg-D rats presenting with CAA compared to wild-type rats. Fewer proteins were significantly decreased in rTg-D rats. Of note, high temperature requirement peptidase A (HTRA1), a proteinase linked to transforming growth factor beta 1 (TGF-β1) signaling, was elevated and found to accumulate in cerebral vessels harboring amyloid deposits. Pathway analysis indicated elevation of the TGF-β1 pathway and increased TGF-β1 levels were detected in rTg-D rats. In conclusion, the present findings provide new molecular insights into the pathogenesis of CAA and suggest a role for interactions between HTRA1 and TGF-β1 in the disease process. [ABSTRACT FROM AUTHOR]

Psychedelic therapies are an emerging class of treatments in psychiatry with great potential, however relatively little is known about their interactions with other commonly used psychiatric medications. As psychedelic therapies become more widespread and move closer to the clinic, they likely will need to be integrated into existing treatment models which may include one or more traditional pharmacological therapies, meaning an awareness of potential drug-drug interactions will become vital. This commentary outlines some of the issues surrounding the study of drug-drug interactions of this type, provides a summary of some of the relevant key results to date, and charts a way forward which relies crucially on multimodal neuroimaging investigations. Studies in humans which combine Positron Emission Tomography (PET) and functional Magnetic Resonance Imaging (fMRI), plus ancillary measures, are likely to provide the most comprehensive assessment of drug-drug interactions involving psychedelics and the relevant effects at multiple levels of the drug response (molecular, functional, and clinical). [ABSTRACT FROM AUTHOR]

Background: We present the NeuroimaGene resource as an R package designed to assist researchers in identifying genes and neurologic features relevant to psychiatric and neurological health. While recent studies have identified hundreds of genes as potential components of pathophysiology in neurologic and psychiatric disease, interpreting the physiological consequences of this variation is challenging. The integration of neuroimaging data with molecular findings is a step toward addressing this challenge. In addition to sharing associations with both molecular variation and clinical phenotypes, neuroimaging features are intrinsically informative of cognitive processes. NeuroimaGene provides a tool to understand how disease-associated genes relate to the intermediate structure of the brain. Results: We created NeuroimaGene, a user-friendly, open access R package now available for public use. Its primary function is to identify neuroimaging derived brain features that are impacted by genetically regulated expression of user-provided genes or gene sets. This resource can be used to (1) characterize individual genes or gene sets as relevant to the structure and function of the brain, (2) identify the region(s) of the brain or body in which expression of target gene(s) is neurologically relevant, (3) impute the brain features most impacted by user-defined gene sets such as those produced by cohort level gene association studies, and (4) generate publication level, modifiable visual plots of significant findings. We demonstrate the utility of the resource by identifying neurologic correlates of stroke-associated genes derived from pre-existing analyses. Conclusions: Integrating neurologic data as an intermediate phenotype in the pathway from genes to brain-based diagnostic phenotypes increases the interpretability of molecular studies and enriches our understanding of disease pathophysiology. The NeuroimaGene R package is designed to assist in this process and is publicly available for use. [ABSTRACT FROM AUTHOR]

Background The role of computed tomography (CT) before lumbar puncture (LP) is unclear, with limited evidence for a causal link between LP and cerebral herniation or for the ability of CT to identify individuals at risk of herniation. The risks of LP delay or deferral are potentially greater in high–HIV prevalence, resource-limited settings; we analyzed data from such a setting to determine the impact of CT on time to LP and treatment, as well as mortality. Methods Adults with suspected central nervous system (CNS) infection were enrolled prospectively into the Botswana National Meningitis Survey between 2016 and 2019. Inpatient mortality and clinical data including time of treatment initiation and CT were captured from medical records. Associations between preceding CT and outcomes were assessed using logistic regression. Results LPs were performed in 711 patients with suspected CNS infection; 27% had a CT before LP, and 73% were HIV positive. Time from admission to LP and time from admission to appropriate treatment were significantly longer in patients who had a CT before LP compared with those who did not (2.8 hours and 13.2 hours, respectively). There was some evidence for treatment delays being associated with increased mortality; however, there was no significant difference in mortality between those who had or did not have CT. Conclusions Patients who had a CT had delays to diagnostic LP and initiation of appropriate treatment; although treatment delays were associated with increased mortality, our observational study could not demonstrate a causal association between delays in diagnosis and treatment introduced by CT and mortality. [ABSTRACT FROM AUTHOR]

Purpose: Metachromatic leukodystrophy (MLD) is a lysosomal storage disease caused by arylsulfatase A deficiency, which leads to progressive demyelination in both the central and peripheral nervous systems, resulting in significant gross motor deterioration. This study aimed to analyze data concerning neuroimaging and clinical phenotypes of MLD patients, categorized by disease subtype. Methods: Patients diagnosed with MLD based on arylsulfatase A enzymatic activity, demyelination observed in brain magnetic resonance images, and/or pathogenic mutations were included in this study. The medical charts of 10 patients with confirmed MLD were retrospectively reviewed. We used a simplified magnetic resonance imaging (MRI) scoring system and clinical status, including survival. We analyzed the correlations between the scores of specific neuroimaging lesions and clinical status in two groups, categorized as late-infantile and juvenile types based on the age at symptom onset. Results: We detected a positive relationship between clinical function deterioration and MRI score (rho=0.59, P=0.002) in patients with MLD. This correlation was stronger in the late-infantile type (rho=0.700, P=0.003) than in the juvenile type (rho=0.513, P=0.029). A strong relationship was also noted in patients with high signal intensities in the pons and basal ganglia, and cerebellar atrophy, but not in those with lesions in the midbrain. MLD with a high MRI score was associated with poor clinical function. Conclusion: The identified correlations between modified MRI scores and clinical function scales may help predict the prognosis of patients with MLD, thereby aiding in the identification of treatment options and enhancing the quality of life for these patients. [ABSTRACT FROM AUTHOR]

Human–robot collaboration (HRC) has become increasingly prevalent due to innovative advancements in the automation industry, especially in manufacturing setups. Although HRC increases productivity and efficacy, it exposes human workers to psychological stress while interfacing with collaborative robotic systems as robots may not provide visual or auditory cues. It is crucial to comprehend how HRC impacts mental stress in order to enhance occupational safety and well-being. Though academics and industrial interest in HRC is expanding, safety and mental stress problems are still not adequately studied. In particular, human coworkers' cognitive strain during HRC has not been explored well, although being fundamental to sustaining a secure and constructive workplace environment. This study, therefore, aims to monitor the mental stress of factory workers during HRC using behavioural, physiological and subjective measures. Physiological measures, being objective and more authentic, have the potential to replace conventional measures i.e., behavioural and subjective measures, if they demonstrate a good correlation with traditional measures. Two neuroimaging modalities including electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have been used as physiological measures to track neuronal and hemodynamic activity of the brain, respectively. Here, the correlation between physiological data and behavioural and subjective measurements has been ascertained through the implementation of seven different machine learning algorithms. The results imply that the EEG and fNIRS features combined produced the best results for most of the targets. For subjective measures being the target, linear regression has outperformed all other models, whereas tree and ensemble performed the best for predicting the behavioural measures. The outcomes indicate that physiological measures have the potential to be more informative and often substitute other skewed metrics. [ABSTRACT FROM AUTHOR]

In 2010, the National Institute of Neurological Disorders and Stroke (NINDS) created a set of common data elements (CDEs) to help standardize the assessment and reporting of imaging findings in traumatic brain injury (TBI). However, as opposed to other standardized radiology reporting systems, a visual overview and data to support the proposed standardized lexicon are lacking. We used over 4000 admission computed tomography (CT) scans of patients with TBI from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study to develop an extensive pictorial overview of the NINDS TBI CDEs, with visual examples and background information on individual pathoanatomical lesion types, up to the level of supplemental and emerging information (e.g., location and estimated volumes). We documented the frequency of lesion occurrence, aiming to quantify the relative importance of different CDEs for characterizing TBI, and performed a critical appraisal of our experience with the intent to inform updating of the CDEs. In addition, we investigated the co-occurrence and clustering of lesion types and the distribution of six CT classification systems. The median age of the 4087 patients in our dataset was 50 years (interquartile range, 29-66; range, 0-96), including 238 patients under 18 years old (5.8%). Traumatic subarachnoid hemorrhage (45.3%), skull fractures (37.4%), contusions (31.3%), and acute subdural hematoma (28.9%) were the most frequently occurring CT findings in acute TBI. The ranking of these lesions was the same in patients with mild TBI (baseline Glasgow Coma Scale [GCS] score 13-15) compared with those with moderate-severe TBI (baseline GCS score 3-12), but the frequency of occurrence was up to three times higher in moderate-severe TBI. In most TBI patients with CT abnormalities, there was co-occurrence and clustering of different lesion types, with significant differences between mild and moderate-severe TBI patients. More specifically, lesion patterns were more complex in moderate-severe TBI patients, with more co-existing lesions and more frequent signs of mass effect. These patients also had higher and more heterogeneous CT score distributions, associated with worse predicted outcomes. The critical appraisal of the NINDS CDEs was highly positive, but revealed that full assessment can be time consuming, that some CDEs had very low frequencies, and identified a few redundancies and ambiguity in some definitions. Whilst primarily developed for research, implementation of CDE templates for use in clinical practice is advocated, but this will require development of an abbreviated version. In conclusion, with this study, we provide an educational resource for clinicians and researchers to help assess, characterize, and report the vast and complex spectrum of imaging findings in patients with TBI. Our data provides a comprehensive overview of the contemporary landscape of TBI imaging pathology in Europe, and the findings can serve as empirical evidence for updating the current NINDS radiologic CDEs to version 3.0. [ABSTRACT FROM AUTHOR]

Intimate partner violence (IPV) is a significant, global public health concern. Women, individuals with historically underrepresented identities, and disabilities are at high risk for IPV and tend to experience severe injuries. There has been growing concern about the risk of exposure to IPV-related head trauma, resulting in IPV-related brain injury (IPV-BI), and its health consequences. Past work suggests that a significant proportion of women exposed to IPV experience IPV-BI, likely representing a distinct phenotype compared with BI of other etiologies. An IPV-BI often co-occurs with psychological trauma and mental health complaints, leading to unique issues related to identifying, prognosticating, and managing IPV-BI outcomes. The goal of this review is to identify important gaps in research and clinical practice in IPV-BI and suggest potential solutions to address them. We summarize IPV research in five key priority areas: (1) unique considerations for IPV-BI study design; (2) understanding non-fatal strangulation as a form of BI; (3) identifying objective biomarkers of IPV-BI; (4) consideration of the chronicity, cumulative and late effects of IPV-BI; and (5) BI as a risk factor for IPV engagement. Our review concludes with a call to action to help investigators develop ecologically valid research studies addressing the identified clinical-research knowledge gaps and strategies to improve care in individuals exposed to IPV-BI. By reducing the current gaps and answering these calls to action, we will approach IPV-BI in a trauma-informed manner, ultimately improving outcomes and quality of life for those impacted by IPV-BI. [ABSTRACT FROM AUTHOR]

The complexity in diagnosing hereditary degenerative ataxias lies not only in their rarity, but also in the variety of different genetic conditions that can determine sometimes similar and overlapping clinical findings. In this light, Magnetic Resonance Imaging (MRI) plays a key role in the evaluation of these conditions, being a fundamental diagnostic tool needed not only to exclude other causes determining the observed clinical phenotype, but also to proper guide to an adequate genetic testing. Here, we propose an MRI-based diagnostic algorithm named CHARON (Characterization of Hereditary Ataxias Relying On Neuroimaging), to help in disentangling among the numerous, and apparently very similar, hereditary degenerative ataxias. Being conceived from a neuroradiological standpoint, it is based primarily on an accurate evaluation of the observed MRI findings, with the first and most important being the pattern of cerebellar atrophy. Along with the evaluation of the presence, or absence, of additional signal changes and/or supratentorial involvement, CHARON allows for the identification of a small groups of ataxias sharing similar imaging features. The integration of additional MRI findings, demographic, clinical and laboratory data allow then for the identification of typical, and in some cases pathognomonic, phenotypes of hereditary ataxias. [ABSTRACT FROM AUTHOR]

The cerebellum is recognized as being important for optimal behavioral performance across task domains, including motor function, cognition, and affect. Decades of work have highlighted cerebello-thalamo-cortical circuits, from both structural and functional perspectives. However, these circuits of interest have been primarily (though not exclusively) focused on targets in the cerebral cortex. In addition to these cortical connections, the circuit linking the cerebellum and hippocampus is of particular interest. Recently, there has been an increased interest in this circuit, thanks in large part to novel findings in the animal literature demonstrating that neuronal firing in the cerebellum impacts that in the hippocampus. Work in the human brain has provided evidence for interactions between the cerebellum and hippocampus, though primarily this has been in the context of spatial navigation. Given the role of both regions in cognition and aging, and emerging evidence indicating that the cerebellum is impacted in age-related neurodegenerative disease such as Alzheimer's, I propose that further attention to this circuit is warranted. Here, I provide an overview of cerebello-hippocampal interactions in animal models and from human imaging and outline the possible utility of further investigations to improve our understanding of aging and age-related cognitive decline. [ABSTRACT FROM AUTHOR]

Neuroimaging studies in rapid eye movement sleep behavior disorder (RBD) can inform fundamental questions about the pathogenesis of Parkinson's disease (PD). Across modalities, functional magnetic resonance imaging (fMRI) may be better suited to identify changes between neural networks in the earliest stages of Lewy body diseases when structural changes may be subtle or absent. This review synthesizes the findings from all fMRI studies of RBD to gain further insight into the pathophysiology and progression of Lewy body diseases. A total of 32 studies were identified using a systematic review conducted according to PRISMA guidelines between January 2000 to February 2024 for original fMRI studies in patients with either isolated RBD (iRBD) or RBD secondary to PD. Common functional alterations were detectable in iRBD patients compared with healthy controls across brainstem nuclei, basal ganglia, frontal and occipital lobes, and whole brain network measures. Patients with established PD and RBD demonstrated decreased functional connectivity across the whole brain and brainstem nuclei, but increased functional connectivity in the cerebellum and frontal lobe compared with those PD patients without RBD. Finally, longitudinal changes in resting state functional connectivity were found to track with disease progression. Currently, fMRI studies in RBD have demonstrated early signatures of neurodegeneration across both motor and non‐motor pathways. Although more work is needed, such findings have the potential to inform our understanding of disease, help to distinguish between prodromal PD and prodromal dementia with Lewy bodies, and support the development of fMRI‐based outcome measures of phenoconversion and progression in future disease modifying trials. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. [ABSTRACT FROM AUTHOR]

Behcet disease (BD) is a systemic vasculitis which can involve many different organ systems. Neurological involvement (NBD) occurs in 5.3% to 59% of BD patients. The diagnosis is challenging especially in case of inaugural neurological presentation, and is based on a constellation of clinical, laboratory, and neuroimaging findings. NBD can be subdivided into parenchymal NBD through an immune mediated meningoencephalitis with a predilection to the brainstem, basal ganglia, thalamus, cranial nerves, and spinal cord involvement, and extraparenchymal NBD encompassing cerebral veinous thrombosis and intracranial arterial involvement. Brain magnetic resonance shows ill-defined areas of oedema with high signal intensity on T2-FLAIR images, isointense or hypointense in T1-weighted images in the basal ganglia area or in the brainstem, which may extend to the diencephalic structures. Swelling might be noticed. Hemorrhages can be seen, such as contrast enhancement (blood brain barrier disruption). Magnetic resonance venography and computerized tomographic angiography can be used to diagnose extraparenchymal NBD. Treatment of parenchymatous forms is based on glucocorticoids associated with oral immunosuppressants (azathioprine, mycophenolate mofetil or methotrexate) in mild forms, and intravenous cyclophosphamide or infliximab in severe forms. The management of cerebral thrombosis consists of steroids course associated with an oral anticoagulation. An early recognition of this condition is mandatory to initiate adequate therapies in order to improve outcomes and limit the risk of sequelae, relapses, or death. The aim of this review is to summarize a comprehensive review on the various neurological presentations of BD with emphasizes on diagnostic tools, prognosis, and therapeutic issues. [ABSTRACT FROM AUTHOR]