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Environmental hypoxia, resulting from reduced oxygen supply, poses a significant risk of dysfunctioning and damaging the neurocognitive system, particularly in relation to anxiety and stress. Inadequate oxygenation can lead to acute and chronic brain damage. Scholars used behavioral, hemodynamic, and electromagnetic neurofunctional techniques to investigate the effects of normobaric and hypobaric hypoxia on neurocognitive systems. They found a correlation between hypoxia, altered psychomotor responses, and changes in EEG alpha, theta, beta, and gamma rhythms, which affect spatial attention and memory. Hypoxia affects event related potential (ERP) components differently depending on latency. Perceptual responses N1 and P2 remain largely unaffected, while the amplitudes of preattentive MMN, vMMN, and P3a are significantly altered. Late latency components related to attention, particularly P3b, are also altered. These changes illustrate the spectrum from sensory detection to more complex cognitive processing, highlighting the brain's efficiency in managing information. Interestingly, the amplitudes of P3b, ADAN and CNV can increase with increased cognitive demands in hypoxia. This suggests a compensatory response. Prolonged exposure exacerbates these effects, resulting in compensatory delayed behavioral responses and alterations in behavioral monitoring and conflict inhibitory control, as reflected by reduced amplitudes in some attention related ERP components, including N2, N2pc, and ERN. Thus, neurocognitive function and integrity are under stress. ERP sources and hemodynamic images reveal that vulnerable brain regions include the frontal prefrontal cortices, hippocampus, basal ganglia, and parietal and visual cortices, which are essential for attention related processes like decision making and spatial memory. The auditory system appears less affected. [ABSTRACT FROM AUTHOR]

Introduction: Depression is prevalent after traumatic brain injury (TBI). However, there is a lack of understanding of the brain-based correlates of depression post-TBI. This systematic review aimed to synthesize findings of structural and functional magnetic resonance imaging (MRI) studies to identify consistently reported neural correlates of depression post-TBI. Methods: A search for relevant published studies was conducted through OVID (MEDLINE, APA PsycINFO, and Embase), with an end date of August 3rd, 2023. Fourteen published studies were included in this review. Results: TBI patients with depression exhibited distinct changes in diffusion- based white matter fractional anisotropy, with the direction of change depending on the acuteness or chronicity of TBI. Decreased functional connectivity (FC) of the salience and default mode networks was prominent alongside the decreased volume of gray matter within the insular, dorsomedial prefrontal, and ventromedial prefrontal cortices. Seven studies reported the correlation between observed neuroimaging and depression outcomes. Of these studies, 42% indicated that FC of the bilateral medial temporal lobe subregions was correlated with depression outcomes in TBI. Discussion: This systematic review summarizes existing neuroimaging evidence and reports brain regions that can be leveraged as potential treatment targets in future studies examining depression post-TBI. [ABSTRACT FROM AUTHOR]

Objective: Long COVID is a condition characterised by persistent symptoms after a SARS-CoV-2 infection, with neurological manifestations being particularly frequent. Existing research suggests that long COVID patients not only report cognitive symptoms but also exhibit measurable cognitive impairment. Neuroimaging studies have identified structural alterations in brain regions linked to cognitive functions. However, most of these studies have focused on patients within months of their initial infection. This study aims to explore the longer-term cognitive effects and brain structural changes in long COVID patients, approximately two years post-infection, in a cohort from San Martín, Buenos Aires, Argentina. Methods: We conducted a cross-sectional study involving 137 participants: 109 with long COVID symptoms and 28 healthy controls. The participants underwent an initial clinical assessment, completed a structured questionnaire and standardised scales, underwent a cognitive assessment, and had a brain MRI scan. Structural MRI images were processed via FreeSurfer and FSL to obtain volumetric measures for subcortical and cortical regions, along with regional cortical thickness. Differences between groups for these variables were analysed using ANCOVA, with permutation tests applied to correct for multiple comparisons. Results: Long COVID patients reported persistent cognitive symptoms such as memory problems and brain fog, with higher levels of fatigue and reduced quality of life compared to controls. Despite subjective cognitive complaints, cognitive tests did not reveal significant differences between groups, except for the TMT-A (p = 0.05). MRI analysis revealed decreased volume in the cerebellum (p = 0.03), lingual gyrus (p = 0.04), and inferior parietal regions (p = 0.03), and reduced cortical thickness in several areas, including the left and right postcentral gyri (p = 0.02, p = 0.03) and precuneus (p = 0.01, p = 0.02). Conclusions: This study highlights the enduring impact of long COVID on quality of life and physical activity, with specific brain structural changes identified two years post-infection. Although cognitive tests did not show clear impairment, the observed brain atrophy and significant reduction in quality of life emphasize the need for comprehensive interventions and further longitudinal studies to understand the long-term effects of long COVID on cognition and brain health. [ABSTRACT FROM AUTHOR]

Background: Infectious brain abscesses and granulomas, characterized by localized collections of pus or inflammatory tissue within the brain parenchyma, pose significant clinical challenges due to their potentially life-threatening nature and complex management requirements. Methods: This cross-sectional study investigated patients diagnosed with infectious brain abscesses and granulomas from March 1, 2012, to October 22, 2021, in Mashhad, Iran. Data were collected from adult patients admitted to the two primary referral centers for community-acquired neuroinfections and neuroinflammations. Demographic information, clinical features, laboratory and neuroimaging characteristics, and clinical outcomes were analyzed. Results: A total of 110 episodes were identified in 106 patients, with a median age of 45 years (IQR 30-56.3) and 62.7% male. Predisposing conditions included immunocompromised states (27.5%), preceding otitis/mastoiditis (16.2%), sinusitis (13.3%), and pulmonary infections (17.2%). The most common clinical manifestations were headache (57.3%), fever (49.1%), altered consciousness (44.4%), and seizures (31.8%). Neuroimaging revealed that brain lesions were solitary in 51% and multiple in 48% of episodes. Surgical intervention was performed in 46.4% of cases. The in-hospital mortality rate was 24.5%, with significant associations found between mortality and factors such as age, altered consciousness, multiple brain lesions, and cerebellum and brainstem involvement. The median length of hospital stay was 28 days (IQR 16-46.5). Conclusion: Our study underscores challenges in diagnosing and treating brain abscesses and granulomas, with high mortality rates (24.5%) despite advanced techniques. Age, altered consciousness, and lesion characteristics predict death. Addressing changing microbial patterns and improving diagnostics are vital for better outcomes, especially in low- and middle-income countries. [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]

Video presentation has become ubiquitous in paradigms investigating the neural and behavioral responses to observed actions. In spite of the great interest in uncovering the processing of observed bodily movements and actions in neuroscience and cognitive science, at present, no standardized set of video stimuli for action observation research in neuroimaging settings exists. To facilitate future action observation research, we developed an open-access database of 135 high-definition videos of a male actor performing object-oriented actions. Actions from 3 categories: kinematically natural and goal-intact (Normal), kinematically unnatural and goal-intact (How), or kinematically natural and goal-violating (What), directed toward 15 different objects were filmed from 3 angles. Psychometric evaluation of the database revealed high video recognition accuracy (Mean accuracy = 88.61 %) and substantial inter-rater agreement (Fleiss' Kappa = 0.702), establishing excellent validity and reliability. Videos' exact timing of motion onset was identified using a custom motion detection frame-differencing procedure. Based on its outcome, the videos were edited to assure that motion begins at the second frame of each video. The videos' timing of category recognition was also identified using a novel behavioral up-down staircase procedure. The identified timings can be incorporated in future experimental designs to counteract jittered stimulus onsets, thus vastly improving the sensitivity of neuroimaging experiments. All videos, their psychometric evaluations, and the timing of their frame of category recognition, as well as our custom programs for performing these evaluations on our, or on other similar video databases, are available at the Open Science Framework (https://osf.io/zexc4/). [ABSTRACT FROM AUTHOR]

Background: Resting-state functional magnetic resonance imaging (rs-fMRI) reveals diverse neural activity patterns in cervical spondylosis (CS) patients. However, the reported results are inconsistent. Therefore, our objective was to conduct a meta-analysis to synthesize the findings from existing rs-fMRI studies and identify consistent patterns of neural brain activity alterations in patients with CS. Materials and methods: A systematic search was conducted across PubMed, Web of Knowledge, Embase, Google Scholar, and CNKI for rs-fMRI studies that compared CS patients with healthy controls (HCs), up to January 28, 2024. Significant cluster coordinates were extracted for comprehensive analysis. Results: We included 16 studies involving 554 CS patients and 488 HCs. CS patients demonstrated decreased brain function in the right superior temporal gyrus and left postcentral gyrus, and increased function in the left superior frontal gyrus. Jackknife sensitivity analysis validated the robustness of these findings, and Egger's test confirmed the absence of significant publication bias (p > 0.05). Meta-regression showed no significant impact of age or disease duration differences on the results. Conclusion: This meta-analysis confirms consistent alterations in specific brain regions in CS patients, highlighting the potential of rs-fMRI to refine diagnostic and therapeutic strategies. Systematic review registration: https://www.crd.york.ac.uk/prospero/ , identifier CRD42024496263. [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: Previous studies have evaluated the safety and efficacy of transcutaneous auricular vagus nerve stimulation (taVNS) for the treatment of Parkinson's disease (PD). However, the mechanism underlying the effect of taVNS on PD remains to be elucidated. This study aimed to investigate the immediate effects of taVNS in PD patients. Methods: This crossover self-controlled study included 50 PD patients. Each patient underwent three sessions of resting-state functional magnetic resonance imaging (rs-fMRI) under three conditions: real taVNS, sham taVNS, and no taVNS intervention. We analyzed whole-brain amplitude of low-frequency fluctuations (ALFF) from preprocessed fMRI data across different intervention conditions. ALFF values in altered brain regions were correlated with clinical symptoms in PD patients. Results: Forty-seven participants completed the study and were included in the final analysis. Real taVNS was associated with a widespread decrease in ALFF in the right hemisphere, including the superior parietal lobule, precentral gyrus, postcentral gyrus, middle occipital gyrus, and cuneus (voxel p < 0.001, GRF corrected). The ALFF value in the right superior parietal lobule during real taVNS was negatively correlated with the Unified Parkinson's Disease Rating Scale Part III (r = −0.417, p = 0.004, Bonferroni corrected). Conclusion: TaVNS could immediately modulate the functional activity of brain regions involved in superior parietal lobule, precentral gyrus, postcentral gyrus, middle occipital gyrus, and cuneus. These findings offer preliminary insights into the mechanism of taVNS in treating PD and bolster confidence in its long-term therapeutic potential. TaVNS appears to reduce ALFF values in specific brain regions, suggesting a potential modulation mechanism for treating PD. [ABSTRACT FROM AUTHOR]

Bruxism is a complex orofacial behavior that can occur during sleep or wakefulness, characterized by the involuntary grinding or clenching of teeth, involving repetitive activity of the jaw muscles. Its etiology is multifactorial, influenced by genetic, psychological, physiological, and lifestyle factors. While the mild bruxism may not necessitate treatment, severe bruxism can lead to significant consequences, including tooth damage, jaw pain, fatigue, and headaches. The bruxism has been associated with medical conditions, such as stress, anxiety, sleep disorders, and various neurological disorders; however, the exact pathophysiology remains elusive. Although the central nervous system is strongly implicated in the development of bruxism, specific neural substrates have not yet been conclusively established. Furthermore, there is evidence to suggest that individuals with bruxism may exhibit neural plasticity, resulting in the establishment of distinct neural circuitry that control the jaw movements. The application of various neurophysiological techniques in both clinical and pre-clinical studies provides valuable insights into the neural mechanisms underlying bruxism. This review aims to comprehensively examine the current literature on the neural pathways involved in bruxism, with the goal of improving the clinical approach and therapeutics for this condition. A deeper understanding of the neural circuitry controlling bruxism holds the potential to advance future treatment approaches and improve the management of patients with bruxism. [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]

Altered mental status (AMS) and coma are terms used to describe disorders of arousal and content of consciousness. AMS may account for up to 4% to 10% of chief complaints in the emergency department setting and is a common accompanying symptom for other presentations. AMS is not a diagnosis, but rather a term for symptoms of acute or chronic disordered mentation, including confusion, disorientation, lethargy, drowsiness, somnolence, unresponsiveness, agitation, altered behavior, inattention, hallucinations, delusions, and psychosis. Some of the most common disorders associated with AMS are underlying medical conditions, substance use, and mental disorders. This document focuses on the appropriateness of neuroimaging in adult patients presenting with AMS changes including new onset delirium or new onset psychosis. In these cases, imaging is often expedited for initial stabilization and to exclude an intracranial process requiring intervention. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation. [ABSTRACT FROM AUTHOR]

Purpose of review: This review aims to rediscuss the leading theories concerning the role of basal ganglia and the thalamus in the genesis of aphasic symptoms in the absence of gross anatomical lesions in cortical language areas as assessed by conventional neuroimaging studies. Recent findings: New concepts in language processing and modern neuroimaging techniques have enabled some progress in resolving the impasse between the current dominant theories: (a) direct and specific linguistic processing and (b) subcortical structures as processing relays in domain-general functions. Of particular interest are studies of connectivity based on functional magnetic resonance imaging (MRI) and tractography that highlight the impact of white matter pathway lesions on aphasia development and recovery. Summary: Connectivity studies have put into evidence the central role of the arcuate fasciculus (AF), inferior frontal occipital fasciculus (IFOF), and uncinate fasciculus (UF) in the genesis of aphasia. Regarding the thalamus, its involvement in lexical-semantic processing through modulation of the frontal cortex is becoming increasingly apparent. [ABSTRACT FROM AUTHOR]

Cognitive impairments, which can be caused by neurodegenerative and cerebrovascular disease, represent a growing global health crisis with far‐reaching implications for individuals, families, healthcare systems, and economies worldwide. Notably, neurodegenerative‐induced cognitive impairment often presents a different pattern and severity compared to cerebrovascular‐induced cognitive impairment. With the development of computational technology, machine learning techniques have developed rapidly, which offers a powerful tool in radiomic analysis, allowing a more comprehensive model that can handle high‐dimensional, multivariate data compared to the traditional approach. Such models allow the prediction of the disease development, as well as accurately classify disease from overlapping symptoms, therefore facilitating clinical decision making. This review will focus on the application of machine learning‐based radiomics on cognitive impairment caused by neurogenerative and cerebrovascular disease. Within the neurodegenerative category, this review primarily focuses on Alzheimer's disease, while also covering other conditions such as Parkinson's disease, Lewy body dementia, and Huntington's disease. In the cerebrovascular category, we concentrate on poststroke cognitive impairment, including ischemic and hemorrhagic stroke, with additional attention given to small vessel disease and moyamoya disease. We also review the specific challenges and limitations when applying machine learning radiomics, and provide our suggestion to overcome those limitations towards the end, and discuss what could be done for future clinical use. [ABSTRACT FROM AUTHOR]

Background and Hypothesis Identifying biomarkers that predict treatment response in early psychosis (EP) is a priority for psychiatry research. Previous work suggests that resting-state connectivity biomarkers may have promise as predictive measures, although prior results vary considerably in direction and magnitude. Here, we evaluated the relationship between intrinsic functional connectivity of the attention, default mode, and salience resting-state networks and 12-month clinical improvement in EP. Study Design Fifty-eight individuals with EP (less than 2 years from illness onset, 35 males, average age 20 years) had baseline and follow-up clinical data and were included in the final sample. Of these, 30 EPs showed greater than 20% improvement in Brief Psychiatric Rating Scale (BPRS) total score at follow-up and were classified as "Improvers." Study Results The overall logistic regression predicting Improver status was significant (χ2 = 23.66, Nagelkerke's R 2 = 0.45, P  < .001, with 85% concordance). Significant individual predictors of Improver status included higher default mode within-network connectivity, higher attention-default mode between-network connectivity, and higher attention-salience between-network connectivity. Including baseline BPRS as a predictor increased model significance and concordance to 92%, and the model was not significantly influenced by the dose of antipsychotic medication (chlorpromazine equivalents). Linear regression models predicting percent change in BPRS were also significant. Conclusions Overall, these results suggest that resting-state functional magnetic resonance imaging connectivity may serve as a useful biomarker of clinical outcomes in recent-onset psychosis. [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]

In the clinical diagnosis and treatment of pituitary adenomas, MRI plays a crucial role. However, traditional manual interpretations are plagued by inter-observer variability and limitations in recognizing details. Radiomics, based on MRI, facilitates quantitative analysis by extracting high-throughput data from images. This approach elucidates correlations between imaging features and pituitary tumor characteristics, thereby establishing imaging biomarkers. Recent studies have demonstrated the extensive application of radiomics in differential diagnosis, subtype identification, consistency evaluation, invasiveness assessment, and treatment response in pituitary adenomas. This review succinctly presents the general workflow of radiomics, reviews pertinent literature with a summary table, and provides a comparative analysis with traditional methods. We further elucidate the connections between radiological features and biological findings in the field of pituitary adenoma. While promising, the clinical application of radiomics still has a considerable distance to traverse, considering the issues with reproducibility of imaging features and the significant heterogeneity in pituitary adenoma patients. [ABSTRACT FROM AUTHOR]

Neurons are interactive cells that connect via ions to develop electromagnetic fields in the brain. This structure functions directly in the brain. Connectome is the data obtained from neuronal connections. Since neural circuits change in the brain in various diseases, studying connectome sheds light on the clinical changes in special diseases. The ability to explore this data and its relation to the disorders leads us to find new therapeutic methods. Artificial intelligence (AI) is a collection of powerful algorithms used for finding the relationship between input data and the outcome. AI is used for extraction of valuable features from connectome data and in turn uses them for development of prognostic and diagnostic models in neurological diseases. Studying the changes of brain circuits in neurodegenerative diseases and behavioral disorders makes it possible to provide early diagnosis and development of efficient treatment strategies. Considering the difficulties in studying brain diseases, the use of connectome data is one of the beneficial methods for improvement of knowledge of this organ. In the present study, we provide a systematic review on the studies published using connectome data and AI for studying various diseases and we focus on the strength and weaknesses of studies aiming to provide a viewpoint for the future studies. Throughout, AI is very useful for development of diagnostic and prognostic tools using neuroimaging data, while bias in data collection and decay in addition to using small datasets restricts applications of AI-based tools using connectome data which should be covered in the future studies. [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]

Background: Extensive glioblastoma infiltration justifies a 15-mm margin around the gross tumor volume (GTV) to define the radiotherapy clinical target volume (CTV). Amide proton transfer (APT)-weighted imaging could enable visualization of tumor infiltration, allowing more accurate GTV delineation. We quantified the impact of integrating APT-weighted imaging into GTV delineation of glioblastoma and compared two APT-weighted quantification methods—magnetization transfer ratio asymmetry (MTRasym) and Lorentzian difference (LD) analysis—for target delineation. Methods: Nine glioblastoma patients underwent an extended imaging protocol prior to radiotherapy, yielding APT-weighted MTRasym and LD maps. From both maps, biological tumor volumes were generated (BTVMTRasym and BTVLD) and added to the conventional GTV to generate biological GTVs (GTVbio,MTRasym and GTVbio,LD). Wilcoxon signed-rank tests were performed for comparisons. Results: The GTVbio,MTRasym and GTVbio,LD were significantly larger than the conventional GTV (p ≤ 0.022), with a median volume increase of 9.3% and 2.1%, respectively. The GTVbio,MTRasym and GTVbio,LD were significantly smaller than the CTV (p = 0.004), with a median volume reduction of 72.1% and 70.9%, respectively. There was no significant volume difference between the BTVMTRasym and BTVLD (p = 0.074). In three patients, BTVMTRasym delineation was affected by elevated signals at the brain periphery due to residual motion artifacts; this elevation was absent on the APT-weighted LD maps. Conclusion: Larger biological GTVs compared to the conventional GTV highlight the potential of APT-weighted imaging for radiotherapy target delineation of glioblastoma. APT-weighted LD mapping may be advantageous for target delineation as it may be more robust against motion artifacts. Relevance statement: The introduction of APT-weighted imaging may, ultimately, enhance visualization of tumor infiltration and eliminate the need for the substantial 15-mm safety margin for target delineation of glioblastoma. This could reduce the risk of radiation toxicity while still effectively irradiating the tumor. Trial registration: NCT05970757 (ClinicalTrials.gov). Key Points: Integration of APT-weighted imaging into target delineation for radiotherapy is feasible. The integration of APT-weighted imaging yields larger GTVs in glioblastoma. APT-weighted LD mapping may be more robust against motion artifacts than APT-weighted MTRasym. [ABSTRACT FROM AUTHOR]

Background: A growing body of literature classifies autism spectrum disorder (ASD) as a heterogeneous, complex neurodevelopmental disorder that often is identified prior to three years of age. We aim to provide a narrative review of key structural and functional properties that differentiate the neuroimaging profile of autistic youth from their typically developing (TD) peers across different neuroimaging modalities. Methods: Relevant studies were identified by searching for key terms in PubMed, with the most recent search conducted on September 1, 2023. Original research papers were included if they applied at least one of seven neuroimaging modalities (structural MRI, functional MRI, DTI, MRS, fNIRS, MEG, EEG) to compare autistic children or those with a family history of ASD to TD youth or those without ASD family history; included only participants <18 years; and were published from 2013 to 2023. Results: In total, 172 papers were considered for qualitative synthesis. When comparing ASD to TD groups, structural MRI-based papers (n = 26) indicated larger subcortical gray matter volume in ASD groups. DTI-based papers (n = 14) reported higher mean and radial diffusivity in ASD participants. Functional MRIbased papers (n = 41) reported a substantial number of between-network functional connectivity findings in both directions. MRS-based papers (n = 19) demonstrated higher metabolite markers of excitatory neurotransmission and lower inhibitory markers in ASD groups. fNIRS-based papers (n = 20) reported lower oxygenated hemoglobin signals in ASD. Converging findings in MEG- (n = 20) and EEG-based (n = 32) papers indicated lower event-related potential and field amplitudes in ASD groups. Findings in the anterior cingulate cortex, insula, prefrontal cortex, amygdala, thalamus, cerebellum, corpus callosum, and default mode network appeared numerous times across modalities and provided opportunities for multimodal qualitative analysis. Conclusions: Comparing across neuroimaging modalities, we found significant differences between the ASD and TD neuroimaging profile in addition to substantial heterogeneity. Inconsistent results are frequently seen within imaging modalities, comparable study populations and research designs. Still, converging patterns across imaging modalities support various existing theories on ASD. [ABSTRACT FROM AUTHOR]

Introduction: Adverse childhood experiences (ACEs) are linked to higher rates of psychiatric disorders in adults. Previous neuroimaging studies with small samples have shown associations between ACEs and alterations in brain volume, connectivity, and blood flow. However, no study has explored these associations in a large clinical population to identify brain regions that may mediate the relationship between ACEs and psychiatric diagnoses. This study aims to evaluate how patient-reported ACEs are associated with brain function in adults, across diagnoses. Methods: We analyzed 7,275 adults using HMPAO SPECT scans at rest and during a continuous performance task (CPT). We assessed the impact of ACEs on brain function across psychiatric diagnoses and performed mediation analyses where brain functional regions of interest acted as mediators between patient-reported ACEs and specific psychiatric diagnoses. We further evaluated the risk of being diagnosed with specific classes of mental illnesses as a function of increasing ACEs and identified which specific ACE questions were statistically related to each diagnosis in this cohort. Results: Increased ACEs were associated with higher activity in cognitive control and default mode networks and decreased activity in the dorsal striatum and cerebellum. Higher ACEs increased the risk of anxiety-related disorders, substance abuse, and depression. Several brain regions were identified as potential mediators between ACEs and adult psychiatric diagnoses. Discussion: This study, utilizing a large clinical cohort, provides new insights into the neurobiological mechanisms linking ACEs to adult psychiatric conditions. The findings suggest that specific brain regions mediate the effects of ACEs on the risk of developing mental health disorders, highlighting potential targets for therapeutic interventions. [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]