Emerging frontiers in Chronic Traumatic Encephalopathy: early diagnosis and implications for neurotherapeutic interventions.

INTRODUCTION: Chronic Traumatic Encephalopathy (CTE) is a neurodegenerative disorder associated with repetitive head trauma. Historically, the diagnosis has been primarily clinical, which has hindered definitive early diagnosis and proactive intervention. AREAS COVERED: The authors analyze the recent advancements in early diagnosis of CTE by examining biomarkers, imaging, and clinical decision tools. They discuss the identification of neuropathologies - such as tau aggregates - through novel techniques ranging from blood sampling and to brain density scanning. The reader will walk away with a better understanding of current advancements in early detection and be better equipped to deal with encephalopathies secondary to trauma in clinical practice. EXPERT OPINION: Tremendous progress has been made in understanding the pathophysiology of CTE. Despite these advancements, CTE treatment is still primarily symptomatic rather than underlying disease. Future research should focus on integrating current understanding of CTE pathophysiology with treatment modalities.

An end-to-end deep learning pipeline to derive blood input with partial volume corrections for automated parametric brain PET mapping.

Dynamic 2-[18F] fluoro-2-deoxy-D-glucose positron emission tomography (dFDG-PET) for human brain imaging has considerable clinical potential, yet its utilization remains limited. A key challenge in the quantitative analysis of dFDG-PET is characterizing a patient-specific blood input function, traditionally reliant on invasive arterial blood sampling. This research introduces a novel approach employing non-invasive deep learning model-based computations from the internal carotid arteries (ICA) with partial volume (PV) corrections, thereby eliminating the need for invasive arterial sampling. We present an end-to-end pipeline incorporating a 3D U-Net based ICA-net for ICA segmentation, alongside a Recurrent Neural Network (RNN) based MCIF-net for the derivation of a model-corrected blood input function (MCIF) with PV corrections. The developed 3D U-Net and RNN was trained and validated using a 5-fold cross-validation approach on 50 human brain FDG PET datasets. The ICA-net achieved an average Dice score of 82.18% and an Intersection over Union of 68.54% across all tested scans. Furthermore, the MCIF-net exhibited a minimal root mean squared error of 0.0052. The application of this pipeline to ground truth data for dFDG-PET brain scans resulted in the precise localization of seizure onset regions, which contributed to a successful clinical outcome, with the patient achieving a seizure-free state after treatment. These results underscore the efficacy of the ICA-net and MCIF-net deep learning pipeline in learning the ICA structure's distribution and automating MCIF computation with PV corrections. This advancement marks a significant leap in non-invasive neuroimaging.

Diaminonaphthalene Boronic Acid (DANBA): New Approach for Peroxynitrite Sensing Site.

Selective detection of reactive oxygen species (ROS) is vital for studying their role in brain diseases. Fluorescence probes can distinguish ONOO- species from other ROS; however, their selectivity toward ONOO- species depends on the ONOO- recognition group. Aryl-boronic acids and esters, which are common ONOO- recognition groups, are not selective for ONOO- over H2O2. In this study, we developed a diaminonaphthalene (DAN)-protected boronic acid as a new ONOO- recognition group that selectively reacts with ONOO- over H2O2 and other ROS. Three DAN-protected boronic acid (DANBA)-based fluorophores that emit fluorescence over visible to near-infrared (NIR) regions, Cou-BN, BVP-BN, and HDM-BN, and their aryl-boronic acid-based counterparts (Cou-BO, BVP-BO, and HDM-BO), were developed. The DANBA-based probes exhibited enhanced selectivity toward ONOO- over that of their control group, as well as universality in MTT assays and in vitro experiments with PC12 cells. The NIR-emissive HDM-BN was optimized to delineate in vivo ONOO- levels in mouse brains with Parkinson's disease. This DAN-protected boronic acid belongs to a new generation of recognition groups for developing ONOO- probes, and this strategy could be extended to other common hydroxyl-containing dyes to detect ONOO- levels in complex biological systems and processes.

Bayesian Lesion Estimation with a Structured Spike-and-Slab Prior.

Neural demyelination and brain damage accumulated in white matter appear as hyperintense areas on T2-weighted MRI scans in the form of lesions. Modeling binary images at the population level, where each voxel represents the existence of a lesion, plays an important role in understanding aging and inflammatory diseases. We propose a scalable hierarchical Bayesian spatial model, called BLESS, capable of handling binary responses by placing continuous spike-and-slab mixture priors on spatially-varying parameters and enforcing spatial dependency on the parameter dictating the amount of sparsity within the probability of inclusion. The use of mean-field variational inference with dynamic posterior exploration, which is an annealing-like strategy that improves optimization, allows our method to scale to large sample sizes. Our method also accounts for underestimation of posterior variance due to variational inference by providing an approximate posterior sampling approach based on Bayesian bootstrap ideas and spike-and-slab priors with random shrinkage targets. Besides accurate uncertainty quantification, this approach is capable of producing novel cluster size based imaging statistics, such as credible intervals of cluster size, and measures of reliability of cluster occurrence. Lastly, we validate our results via simulation studies and an application to the UK Biobank, a large-scale lesion mapping study with a sample size of 40,000 subjects.

Shifting concepts of autoregulation: Commentary to 'static autoregulation in humans'.

Advances in imaging techniques have transformed our understanding of cerebral autoregulation. Older imaging techniques provided measurements of cerebral blood flow (CBF) that reflected the average CBF over a window of 10-20 minutes. A key finding, dating back to 1959, was that CBF remained more or less stable over a remarkably wide range of changes in blood pressure. Modern techniques can measure changes in CBF within the time frame of a heartbeat. They have revealed, paradoxically, a remarkable instability of CBF. This commentary attempts to reconcile these seemingly contradictory observations.

Using mechanistic knowledge to appraise contemporary approaches to the rehabilitation of upper limb function following stroke.

It is a paradox of neurological rehabilitation that, in an era in which preclinical models have produced significant advances in our mechanistic understanding of neural plasticity, there is inadequate support for many therapies recommended for use in clinical practice. When the goal is to estimate the probability that a specific form of therapy will have a positive clinical effect, the integration of mechanistic knowledge (concerning 'the structure or way of working of the parts in a natural system') may improve the quality of inference. This is illustrated by analysis of three contemporary approaches to the rehabilitation of lateralized dysfunction affecting people living with stroke: constraint-induced movement therapy; mental practice; and mirror therapy. Damage to 'cross-road' regions of the structural (white matter) brain connectome generates deficits that span multiple domains (motor, language, attention and verbal/spatial memory). The structural integrity of these regions determines not only the initial functional status, but also the response to therapy. As structural disconnection constrains the recovery of functional capability, 'disconnectome' modelling provides a basis for personalized prognosis and precision rehabilitation. It is now feasible to refer a lesion delineated using a standard clinical scan to a (dis)connectivity atlas derived from the brains of other stroke survivors. As the individual disconnection pattern thus obtained suggests the functional domains most likely be compromised, a therapeutic regimen can be tailored accordingly. Stroke is a complex disorder that burdens individuals with distinct constellations of brain damage. Mechanistic knowledge is indispensable when seeking to ameliorate the behavioural impairments to which such damage gives rise.

Review of Microwave Near-Field Sensing and Imaging Devices in Medical Applications.

Microwaves can safely and non-destructively illuminate and penetrate dielectric materials, making them an attractive solution for various medical tasks, including detection, diagnosis, classification, and monitoring. Their inherent electromagnetic properties, portability, cost-effectiveness, and the growth in computing capabilities have encouraged the development of numerous microwave sensing and imaging systems in the medical field, with the potential to complement or even replace current gold-standard methods. This review aims to provide a comprehensive update on the latest advances in medical applications of microwaves, particularly focusing on the near-field ones working within the 1-15 GHz frequency range. It specifically examines significant strides in the development of clinical devices for brain stroke diagnosis and classification, breast cancer screening, and continuous blood glucose monitoring. The technical implementation and algorithmic aspects of prototypes and devices are discussed in detail, including the transceiver systems, radiating elements (such as antennas and sensors), and the imaging algorithms. Additionally, it provides an overview of other promising cutting-edge microwave medical applications, such as knee injuries and colon polyps detection, torso scanning and image-based monitoring of thermal therapy intervention. Finally, the review discusses the challenges of achieving clinical engagement with microwave-based technologies and explores future perspectives.

Yearly change in air pollution and brain aging among older adults: A community-based study in Taiwan.

BACKGROUND: Air pollution is recognized as a modifiable risk factor for dementia, and recent evidence suggests that improving air quality could attenuate cognitive decline and reduce dementia risk. However, studies have yet to explore the effects of improved air quality on brain structures. This study aims to investigate the impact of air pollution reduction on cognitive functions and structural brain differences among cognitively normal older adults. METHODS: Four hundred and thirty-one cognitively normal older adults were from the Epidemiology of Mild Cognitive Impairment study in Taiwan (EMCIT), a community-based cohort of adults aged 60 and older, between year 2017- 2021. Annual concentrations of PM2.5, NO2, O3, and PM10 at participants' residential addresses during the 10 years before enrollment were estimated using ensemble mixed spatial models. The yearly rate of change (slope) in air pollutants was estimated for each participant. Cognitive functions and structural brain images were collected during enrollment. The relationships between the rate of air pollution change and cognitive functions were examined using linear regression models. For air pollutants with significant findings in relation to cognitive function, we further explored the association with brain structure. RESULTS: Overall, all pollutant concentrations, except O3, decreased over the 10-year period. The yearly rates of change (slopes) in PM2.5 and NO2 were correlated with better attention (PM2.5: r = -0.1, p = 0.047; NO2: r = -0.1, p = 0.03) and higher white matter integrity in several brain regions. These regions included anterior thalamic radiation, superior longitudinal fasciculus, inferior longitudinal fasciculus, corticospinal tract, and inferior fronto-occipital fasciculus. CONCLUSIONS: Greater rate of reduction in air pollution was associated with better attention and attention-related white matter integrity. These results provide insight into the mechanism underlying the relationship between air pollution, brain health, and cognitive aging among older adults.

Associations between brain imaging-derived phenotypes and cognitive functions.

We aimed to evaluate the potential causal relationship between brain imaging-derived phenotypes and cognitive functions via Mendelian randomization analyses. Genetic instruments for 470 brain imaging-derived phenotypes were selected from a genome-wide association study based on the UK Biobank (n = 33,224). Statistics for cognitive functions were obtained from the genome-wide association study based on the UK Biobank. We used the inverse variance weighted Mendelian randomization method to investigate the associations between brain imaging-derived phenotypes and cognitive functions, and reverse Mendelian randomization analyses were performed for significant brain imaging-derived phenotypes to examine the reverse causation for the identified associations. We identified three brain imaging-derived phenotypes to be associated with verbal-numerical reasoning, including cortical surface area of the left fusiform gyrus (beta, 0.18 [95% confidence interval, 0.11 to 0.25], P = 4.74 × 10-7), cortical surface area of the right superior temporal gyrus (beta, 0.25 [95% confidence interval, 0.15 to 0.35], P = 6.30 × 10-7), and orientation dispersion in the left superior longitudinal fasciculus (beta, 0.14 [95% confidence interval, 0.09 to 0.20], P = 8.37 × 10-7). The reverse Mendelian randomization analysis indicated that verbal-numerical reasoning had no effect on these three brain imaging-derived phenotypes. This Mendelian randomization study identified cortical surface area of the left fusiform gyrus, cortical surface area of the right superior temporal gyrus, and orientation dispersion in the left superior longitudinal fasciculus as predictors of verbal-numerical reasoning.

Compressed representation of brain genetic transcription.

The architecture of the brain is too complex to be intuitively surveyable without the use of compressed representations that project its variation into a compact, navigable space. The task is especially challenging with high-dimensional data, such as gene expression, where the joint complexity of anatomical and transcriptional patterns demands maximum compression. The established practice is to use standard principal component analysis (PCA), whose computational felicity is offset by limited expressivity, especially at great compression ratios. Employing whole-brain, voxel-wise Allen Brain Atlas transcription data, here we systematically compare compressed representations based on the most widely supported linear and non-linear methods-PCA, kernel PCA, non-negative matrix factorisation (NMF), t-stochastic neighbour embedding (t-SNE), uniform manifold approximation and projection (UMAP), and deep auto-encoding-quantifying reconstruction fidelity, anatomical coherence, and predictive utility across signalling, microstructural, and metabolic targets, drawn from large-scale open-source MRI and PET data. We show that deep auto-encoders yield superior representations across all metrics of performance and target domains, supporting their use as the reference standard for representing transcription patterns in the human brain.

Dynamic Ultrasound Localization Microscopy Without ECG-Gating.

OBJECTIVE: Dynamic Ultrasound Localization Microscopy (DULM) has first been developed for non-invasive Pulsatility measurements in the rodent brain. DULM relies on the localization and tracking of microbubbles (MBs) injected into the bloodstream, to obtain highly resolved velocity and density cine-loops. Previous DULM techniques required ECG-gating, limiting its application to specific datasets, and increasing acquisition time. The objective of this study is to eliminate the need for ECG-gating in DULM experiments by introducing a motion-matching method for time registration. METHODS: We developed a motion-matching algorithm based on tissue Doppler that leverages the cyclic tissue motion within the brain. Tissue Doppler was estimated for each group of frames in the acquisitions, at multiple locations identified as local maxima in the skin above the skull. Subsequently, each group of frames was time-registered to a reference group by delaying it based on the maximum correlation value between their respective tissue Doppler signals. This synchronization ensured that each group of frames aligned with the brain tissue motion of the reference group, and consequently, with its cardiac cycle. As a result, velocities of MBs could be averaged to retrieve flow velocity variations over time. RESULTS: Initially validated in ECG-gated acquisitions in a rat model (n = 1), the proposed method was successfully applied in a mice model in 2D (n = 3) and in a feline model in 3D (n = 1). Performing time-registration with the proposed motion-matching method or by using ECG-gating leads to similar results. For the first time, dynamic velocity and density cine-loops were extracted without the need for any information on the animal ECG, and complex dynamic markers such as the Pulsatility index were estimated. CONCLUSION: Results suggest that DULM can be performed without external gating, enabling the use of DULM on any ULM dataset where enough MBs are detectable. Time registration by motion-matching represents a significant advancement in DULM techniques, making DULM more accessible by simplifying its experimental complexity.

Flexible circuit-based spatially aware modular optical brain imaging system for high-density measurements in natural settings.

Significance: Functional near-infrared spectroscopy (fNIRS) presents an opportunity to study human brains in everyday activities and environments. However, achieving robust measurements under such dynamic conditions remains a significant challenge. Aim: The modular optical brain imaging (MOBI) system is designed to enhance optode-to-scalp coupling and provide a real-time probe three-dimensional (3D) shape estimation to improve the use of fNIRS in everyday conditions. Approach: The MOBI system utilizes a bendable and lightweight modular circuit-board design to enhance probe conformity to head surfaces and comfort for long-term wearability. Combined with automatic module connection recognition, the built-in orientation sensors on each module can be used to estimate optode 3D positions in real time to enable advanced tomographic data analysis and motion tracking. Results: Optical characterization of the MOBI detector reports a noise equivalence power of 8.9 and 7.3 pW / Hz at 735 and 850 nm, respectively, with a dynamic range of 88 dB. The 3D optode shape acquisition yields an average error of 4.2 mm across 25 optodes in a phantom test compared with positions acquired from a digitizer. Results for initial in vivo validations, including a cuff occlusion and a finger-tapping test, are also provided. Conclusions: To the best of our knowledge, the MOBI system is the first modular fNIRS system featuring fully flexible circuit boards. The self-organizing module sensor network and automatic 3D optode position acquisition, combined with lightweight modules ( 18 g / module ) and ergonomic designs, would greatly aid emerging explorations of brain function in naturalistic settings.

Association between irritable temperament and glucose metabolism in the left insula and the right cerebellum.

INTRODUCTION: Affective temperaments are assumed to have biological and neural bases. In the present study, we analyzed 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) images of healthy participants to explore the neural basis of affective temperaments. METHOD: We utilized data of affective temperament measured by the Temperament Evaluation of Memphis, Pisa, Paris, and San Diego-Autoquestionnaire and 18F-FDG PET images of healthy participants from two of our previous studies. A multiple regression analysis was performed to assess the association between 18F-FDG uptake and temperament scores using Statistical Parametric Mapping 12. RESULTS: The final sample included 62 healthy participants. Whole-brain analysis revealed a cluster of 18F-FDG uptake that was significantly and positively associated with irritable temperament scores in the right cerebellum (Crus II, VIII, and IX). After further adjustment for the other four temperament scores, whole-brain analysis revealed a cluster of 18F-FDG uptake significantly and positively associated with irritable temperament scores in the left insula and right cerebellum (Crus II, VIII, and IX). However, no significant association was found between 18F-FDG uptake and the other four temperaments (depressive, cyclothymic, hyperthymic, and anxious). CONCLUSIONS: The left insula and right cerebellum of the cerebrocerebellar circuit may be one of the neural bases of irritable temperament.

Hemiplegic (unilateral) cerebral palsy in northern Stockholm: Intellectual disability and epilepsy.

PURPOSE: The purpose of this study was to describe intellectual disability and its association with epilepsy and brain imaging, in a population-based group of children with hemiplegic (unilateral) cerebral palsy, previously investigated and published in 2020. MATERIALS AND METHODS: Forty-seven children of school age in northern Stockholm, fulfilling the Surveillance of Cerebral Palsy in Europe-criteria of hemiplegic (unilateral spastic) cerebral palsy, were invited to participate in the study. Twenty-one children consented to participate. A WISC (Wechsler Intelligence Scale for Children)-test was performed by an experienced psychologist. RESULTS: In the study population of twenty-one children, 57 % (n 12) displayed uneven cognitive profiles, 38 % (n 8) intellectual disability and 62 % (n 13) had a normal IQ. 43 % (n 9) developed epilepsy. Children with extensive brain lesions had more severe intellectual disability. CONCLUSIONS: In this study intellectual disability and/or epilepsy were associated with the type and extent of the underlying brain lesion. Intellectual disability and uneven cognitive profiles were common. We therefore recommend individual cognitive assessment to ensure an optimal school start.

Cognitive driven gait freezing phase detection and classification for neuro-rehabilitated patients using machine learning algorithms.

BACKGROUND: The significance of diagnosing illnesses associated with brain cognitive and gait freezing phase patterns has led to a recent surge in interest in the study of gait for mental disorders. A more precise and effective way to characterize and classify many common gait problems, such as foot and brain pulse disorders, can improve prognosis evaluation and treatment options for Parkinson patients. Nonetheless, the primary clinical technique for assessing gait abnormalities at the moment is visual inspection, which depends on the subjectivity of the observer and can be inaccurate. RESEARCH QUESTION: This study investigates whether it is possible to differentiate between gait brain disorder and the typical walking pattern using machine learning driven supervised learning techniques and data obtained from inertial measurement unit sensors for brain, hip and leg rehabilitation. METHOD: The proposed method makes use of the Daphnet freezing of Gait Data Set, consisted of 237 instances with 9 attributes. The method utilizes machine learning and feature reduction approaches in leg and hip gait recognition. RESULTS: From the obtained results, it is concluded that among all classifiers RF achieved highest accuracy as 98.9 % and Perceptron achieved lowest i.e. 70.4 % accuracy. While utilizing LDA as feature reduction approach, KNN, RF and NB also achieved promising accuracy and F1-score in comparison with SVM and LR classifiers. SIGNIFICANCE: In order to distinguish between the different gait disorders associated with brain tissues freezing/non-freezing and normal walking gait patterns, it is shown that the integration of different machine learning algorithms offers a viable and prospective solution. This research implies the need for an impartial approach to support clinical judgment.

Acetylcholine Neurons Become Cholinergic during Three Time Windows in the Developing Mouse Brain.

Acetylcholine (ACh) neurons in the central nervous system are required for the coordination of neural network activity during higher brain functions, such as attention, learning, and memory, as well as locomotion. Disturbed cholinergic signaling has been described in many neurodevelopmental and neurodegenerative disorders. Furthermore, cotransmission of other signaling molecules, such as glutamate and GABA, with ACh has been associated with essential roles in brain function or disease. However, it is unknown when ACh neurons become cholinergic during development. Thus, understanding the timeline of how the cholinergic system develops and becomes active in the healthy brain is a crucial part of understanding brain development. To study this, we used transgenic mice to selectively label ACh neurons with tdTomato. We imaged serial sectioned brains and generated whole-brain reconstructions at different time points during pre- and postnatal development. We found three crucial time windows-two in the prenatal and one in the postnatal brain-during which most ACh neuron populations become cholinergic in the brain. We also found that cholinergic gene expression is initiated in cortical ACh interneurons, while the cerebral cortex is innervated by cholinergic projection neurons from the basal forebrain. Taken together, we show that ACh neuron populations are present and become cholinergic before postnatal day 12, which is the onset of major sensory processes, such as hearing and vision. We conclude that the birth of ACh neurons and initiation of cholinergic gene expression are temporally separated during development but highly coordinated by brain anatomical structure.

Alice Rosenstein (1898-1991) First German Female Neurosurgeon.

BACKGROUND/OBJECTIVE: Gender inequality has been a long-standing issue throughout history, with limited progress despite the rise of women in the workforce. Historically, women were deemed inferior to men, including within the medical profession, due to perceived bodily differences. METHOD: This perception was reinforced in religious texts, depicting women as bearing the burden of the first woman's transgressions. Such attitudes also influenced the treatment of women's health, with menstruation viewed as a natural source of suffering. Nevertheless, a thorough examination of medical history unveils a deep-rooted bias against women. RESULTS: This antiquated and discriminatory notion lacks any foundation in scientific truth. Indeed, an examination of the contributions made by female physicians reveals that they deliver equivalent levels of care, attentiveness, preventive measures, and therapeutic efficacy as their male counterparts. The narratives of female trailblazers in the medical field, like Alice Rosenstein, the first female neurosurgeon in Germany, provide compelling evidence of this phenomenon. CONCLUSIONS: This paper delves into her professional journey and the significant influence she has had on the field of neurosurgery.

Cognitive and brain morphological deviations in middle-to-old aged autistic adults: A systematic review and meta-analysis.

Cognitive challenges and brain structure variations are common in autism spectrum disorder (ASD) but are rarely explored in middle-to-old aged autistic adults. Cognitive deficits that overlap between young autistic individuals and elderlies with dementia raise an important question: does compromised cognitive ability and brain structure during early development drive autistic adults to be more vulnerable to pathological aging conditions, or does it protect them from further decline? To answer this question, we have synthesized current theoretical models of aging in ASD and conducted a systematic literature review (Jan 1, 1980 - Feb 29, 2024) and meta-analysis to summarize empirical studies on cognitive and brain deviations in middle-to-old aged autistic adults. We explored findings that support different aging theories in ASD and addressed study limitations and future directions. This review sheds light on the poorly understood consequences of aging question raised by the autism community to pave the way for future studies to identify sensitive and reliable measures that best predict the onset, progression, and prognosis of pathological aging in ASD.

Imaging features for the identification of atrial fibrillation in cryptogenic stroke patients.

BACKGROUND: Whether specific imaging aspects can be used to identify cryptogenic stroke (CS) patients with high risk of underlying atrial fibrillation (AF) remains unclear. The purpose of this study was to evaluate brain-imaging features in CS patients and their utility as AF predictors. METHODS: The Nordic Atrial Fibrillation and Stroke study was a prospective observational study of CS and transient ischemic attack patients undergoing 12-month cardiac-rhythm monitoring, biomarker and clinical assessments. In this imaging sub-study, brain magnetic resonance imaging and computed tomography scans from 106 patients were assessed for acute and chronic ischemic lesions in relation to AF occurrence and included in a score to predict AF. Receiver operating characteristics (ROC) curve was used to evaluate the discriminative ability of the score and for its dichotomization for predictive model. RESULTS: Age, periventricular white-matter hyperintensities (PVWMH), acute lesion size, and vessel occlusion were significantly associated with AF. Acute and chronic cortical infarcts as well as chronic cerebellar infarcts were numerically more frequent in the AF group than the non-AF group. A score consisting of six features (0-6 points) was proposed (age ≥ 65 years, chronic cortical or cerebellar lesions, acute cortical lesions, PVWMH ≥ 2 in Fazekas scale, vessel occlusion, and acute lesion size ≥ 10 mm). Area under ROC curve was 0.735 and a score of ≥ 3 points was a predictor of AF. CONCLUSIONS: The suggested score was shown to identify CS patients with an increased risk of underlying AF.

A survey of brain functional network extraction methods using fMRI data.

Functional network (FN) analyses play a pivotal role in uncovering insights into brain function and understanding the pathophysiology of various brain disorders. This paper focuses on classical and advanced methods for deriving brain FNs from functional magnetic resonance imaging (fMRI) data. We systematically review their foundational principles, advantages, shortcomings, and interrelations, encompassing both static and dynamic FN extraction approaches. In the context of static FN extraction, we present hypothesis-driven methods such as region of interest (ROI)-based approaches as well as data-driven methods including matrix decomposition, clustering, and deep learning. For dynamic FN extraction, both window-based and windowless methods are surveyed with respect to the estimation of time-varying FN and the subsequent computation of FN states. We also discuss the scope of application of the various methods and avenues for future improvements.