Neuroimaging Techniques Research Articles

Neuroprotective effects of naltrexone in a mouse model of post-traumatic seizures

Traumatic Brain Injury (TBI) induces neuroinflammatory response that can initiate epileptogenesis, which develops into epilepsy. Recently, we identified anti-convulsive effects of naltrexone, a mu-opioid receptor (MOR) antagonist, used to treat drug addiction. While blocking opioid receptors can reduce inflammation, it is unclear if post-TBI seizures can be prevented by blocking MORs. Here, we tested if naltrexone prevents neuroinflammation and/or seizures post-TBI. TBI was induced by a modified Marmarou Weight-Drop (WD) method on 4-week-old C57BL/6J male mice. Mice were placed in two groups: non-telemetry assessing the acute effects or in telemetry monitoring for interictal events and spontaneous seizures both following TBI and naltrexone. Molecular, histological and neuroimaging techniques were used to evaluate neuroinflammation, neurodegeneration and fiber track integrity at 8 days and 3 months post-TBI. Peripheral immune responses were assessed through serum chemokine/cytokine measurements. Our results show an increase in MOR expression, nitro-oxidative stress, mRNA expression of inflammatory cytokines, microgliosis, neurodegeneration, and white matter damage in the neocortex of TBI mice. Video-EEG revealed increased interictal events in TBI mice, with 71% mice developing post-traumatic seizures (PTS). Naltrexone treatment ameliorated neuroinflammation, neurodegeneration, reduced interictal events and prevented seizures in all TBI mice, which makes naltrexone a promising candidate against PTS, TBI-associated neuroinflammation and epileptogenesis in a WD model of TBI.

Maximizing the Effectiveness of Anti-Smoking Campaigns With Targeted Framing Strategies: Evidence From Behavioral and Neurological Studies

Background The global prevalence of smoking remains a significant public health concern, necessitating the development of effective anti-smoking campaigns to promote smoking cessation and discourage smoking initiation. Understanding the factors that influence individual responses to anti-smoking messages is critical for tailoring campaigns to different populations. Focus of the Article This study substantially exhibits the concepts of audience segmentation and targeting, informed by theory and formative research, and monitoring and evaluation research in the context of anti-smoking campaigns. Research Question The article aims to answer how smoking behavior and message framing influence individuals’ counterarguing responses to anti-smoking advertisements and the implications for ad effectiveness. Approach The research investigates the role of smoking behavior (smokers vs. non-smokers) and message framing (positive vs. negative) in shaping individuals’ counterarguing responses to anti-smoking advertisements, using two studies involving behavioral and neuroscientific methods. Methods Study 1 employed an online video task with 1920 participants, examining the effects of smoking behavior and message framing on ad effectiveness. Study 2 used electroencephalography (EEG) to record brain activity from 31 participants while they viewed anti-smoking video advertisements, focusing on the neural correlates of counterarguing and their effect on anti-smoking ad effectiveness. Results Study 1 found that smokers report lower ad effectiveness compared to non-smokers, and observed an interaction between smoking behavior and message framing. Study 2 found that smoking behavior significantly influenced brain activity in the dorsolateral prefrontal cortex (dlPFC), ventrolateral prefrontal cortex (VLPFC), and anterior cingulate cortex (ACC) regions in response to anti-smoking ads. Additionally, a significant interaction was observed between smoking behavior and ad framing on brain activity in all three regions. Recommendations for Research or Practice The findings suggest that anti-smoking campaigns should be tailored to the specific needs and attitudes of smokers and non-smokers, with separate campaigns for each group. Future research could explore the role of other individual differences in shaping counterarguing responses, the long-term effects of exposure to different types of anti-smoking messages on smoking behavior, and using more advanced neuroimaging techniques to investigate the neural processes that underlie counterarguing responses to anti-smoking messages for better flow.

Modern methods of neuroimaging in the diagnosis of acute ischemic stroke

Ischemic stroke is one of the leading causes of morbidity and mortality worldwide, resulting in significant social and economic consequences for the healthcare system. Moreover, ischemic stroke has a devastating psycho-emotional impact on both the patient and their family, causing anxiety and depressive disorders. To reduce the severity of neurological deficit, increase the percentage of patients returning to their previous level of social and daily activity, and decrease the rates of disability after a cerebrovascular catastrophe, it is necessary to provide the full spectrum of therapeutic and diagnostic assistance in the shortest possible time. Developing neuroimaging techniques help to timely and accurately diagnose cerebral stroke, differentiate ischemic damage from hemorrhagic, and also establish the time of onset of acute cerebrovascular accident.

Hyperscanning literature after two decades of neuroscientific research: A scientometric review

Hyperscanning, a neuroimaging approach introduced in 2002 for simultaneously recording the brain activity of multiple participants, has significantly contributed to our understanding of social interactions. Nevertheless, the existing literature requires systematic organization to advance our knowledge. This study, after two decades of hyperscanning research, aims to identify the primary thematic domains and the most influential documents in the field. We conducted a scientometric analysis to examine co-citation patterns quantitatively, using a sample of 548 documents retrieved from Scopus and their 32,022 cited references. Our analysis revealed ten major thematic domains in hyperscanning research, with the most impactful document authored by Czeszumski and colleagues in 2020. Notably, while hyperscanning was initially developed for functional magnetic resonance imaging (fMRI), our findings indicate a substantial influence of research conducted using electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). The introduction of fNIRS and advancements in EEG methods have enabled the implementation of more ecologically valid experiments for investigating social interactions. The study also highlights the need for more research that combines multi-brain neural stimulation with neuroimaging techniques to understand the causal role played by interpersonal neural synchrony in social interactions.

Disparate nonlinear neural dynamics measured with different techniques in macaque and human V1

Diverse neuro-imaging techniques measure different aspects of neural responses with distinct spatial and temporal resolutions. Relating measured neural responses across different methods has been challenging. Here, we take a step towards overcoming this challenge, by comparing the nonlinearity of neural dynamics measured across methods. We used widefield voltage-sensitive dye imaging (VSDI) to measure neural population responses in macaque V1 to visual stimuli with a wide range of temporal waveforms. We found that stimulus-evoked VSDI responses are surprisingly near-additive in time. These results are qualitatively different from the strong sub-additive dynamics previously measured using fMRI and electrocorticography (ECoG) in human visual cortex with a similar set of stimuli. To test whether this discrepancy is specific to VSDI—a signal dominated by subthreshold neural activity, we repeated our measurements using widefield imaging of a genetically encoded calcium indicator (GcaMP6f)—a signal dominated by spiking activity, and found that GCaMP signals in macaque V1 are also near-additive. Therefore, the discrepancies in the extent of sub-additivity between the macaque and the human measurements are unlikely due to differences between sub- and supra-threshold neural responses. Finally, we use a simple yet flexible delayed normalization model to capture these different dynamics across measurements (with different model parameters). The model can potentially generalize to a broader set of stimuli, which aligns with previous suggestion that dynamic gain-control is a canonical computation contributing to neural processing in the brain.

An Experiment Using Functional Near-Infrared Spectroscopy and Robot-Assisted Multi-Joint Pointing Movements of the Lower Limb.

Stroke affects approximately 17 million individuals worldwide each year and is a leading cause of long-term disability. Robotic therapy has shown promise in helping stroke patients regain lost motor functions. One potential avenue for increasing the understanding of how motor recovery occurs is to study brain activation during the movements that are targeted by therapy in healthy individuals. Functional Near-Infrared Spectroscopy (fNIRS) has emerged as a promising neuroimaging technique for examining neural underpinnings of motor function. This study aimed to investigate fNIRS neural correlates of complex lower limb movements in healthy subjects. Participants were asked to perform cycles of rest and movement for 6 min using a robotic device for motor rehabilitation. The task required coordinated knee and ankle joint movements to point to targets displayed on a computer screen. Two experimental conditions with different levels of movement assistance provided by the robot were explored. The results showed that the fNIRS protocol effectively detected brain regions associated with motor control during the task. Notably, all subjects exhibited greater activation in the contralateral premotor area during the no-assistance condition compared to the assisted condition. In conclusion, fNIRS appears to be a valuable approach for detecting changes in oxyhemoglobin concentration associated with multi-joint pointing movements of the lower limb. This research might contribute to the understanding of stroke motor recovery mechanisms and might pave the way for improved rehabilitation treatments for stroke patients. However, further research is needed to fully elucidate the potential of fNIRS in studying motor function and its applications in clinical settings.

Mapping Thyroid Hormone Action in the Human Brain.

Background: Normal brain development, mood, and cognitive functions depend on thyroid hormone (TH) action. However, little is known about how TH mediates its actions in the human brain. This is due to limited access to human brains deprived of TH during fetal and early postnatal life, as well as from adults with altered thyroid status. One way to partially bypass these limitations is by using magnetic resonance imaging and spectroscopy, two neuroimaging techniques that provide detailed, noninvasive information on human brain structure and function. Another way is using human-induced pluripotent stem cell (hiPSCs)-derived three-dimensional in vitro systems, known as brain organoids, which allow for the study of fundamental aspects of the early stages of human brain development. Summary: This narrative review focuses on neuroimaging and brain organoid studies. Neuroimaging of human brains performed in individuals with different thyroid conditions provides information on the volume, myelination, blood flow, neural activity, and connectivity of different areas. Such studies show that suboptimal thyroid status can impact human brain development and its normal function throughout life. This is true not only for patients with sporadic congenital hypothyroidism, during pregnancy or early after birth, but also for adult patients with hypo- or hyperthyroidism, patients carrying mutations that manifest as impaired sensitivity to TH, and even for normal individuals during aging. Studies using brain organoids generated from hiPSCs of healthy individuals or patients with thyroid genetic conditions provide insights into how TH can impact the early development of the human cerebral cortex. Conclusions: The developmental alterations in children born to mothers with different degrees of gestational hypothyroidism or who developed hypothyroidism early in life are remarkable, affecting multiple brain regions and pathways, including the cerebral cortex, hippocampus, cerebellum, interhemispheric and corticospinal tracts, and associative nuclei. The data connecting such changes to poor neurological outcomes in adult patients with hypothyroidism represent an objective link between thyroid-specific functional brain alterations and behavior. Growing brain organoids require TH, which is critical for human neurogenesis and oligodendrogenesis. These models have proven useful in screening drugs with potential therapeutic effects for patients with genetic thyroid diseases.

The neural and genetic underpinnings of different developmental trajectories of Attention-Deficit/Hyperactivity Symptoms in children and adolescents

BackgroundThe trajectory of attention-deficit hyperactivity disorder (ADHD) symptoms in children and adolescents, encompassing descending, stable, and ascending patterns, delineates their ADHD status as remission, persistence or late onset. However, the neural and genetic underpinnings governing the trajectory of ADHD remain inadequately elucidated.MethodsIn this study, we employed neuroimaging techniques, behavioral assessments, and genetic analyses on a cohort of 487 children aged 6–15 from the Children School Functions and Brain Development project at baseline and two follow-up tests for 1 year each (interval 1: 1.14 ± 0.32 years; interval 2: 1.14 ± 0.30 years). We applied a Latent class mixed model (LCMM) to identify the developmental trajectory of ADHD symptoms in children and adolescents, while investigating the neural correlates through gray matter volume (GMV) analysis and exploring the genetic underpinnings using polygenic risk scores (PRS).ResultsThis study identified three distinct trajectories (ascending-high, stable-low, and descending-medium) of ADHD symptoms from childhood through adolescence. Utilizing the linear mixed-effects (LME) model, we discovered that attention hub regions served as the neural basis for these three developmental trajectories. These regions encompassed the left anterior cingulate cortex/medial prefrontal cortex (ACC/mPFC), responsible for inhibitory control; the right inferior parietal lobule (IPL), which facilitated conscious focus on exogenous stimuli; and the bilateral middle frontal gyrus/precentral gyrus (MFG/PCG), accountable for regulating both dorsal and ventral attention networks while playing a crucial role in flexible modulation of endogenous and extrinsic attention. Furthermore, our findings revealed that individuals in the ascending-high group exhibited the highest PRS for ADHD, followed by those in the descending-medium group, with individuals in the stable-low group displaying the lowest PRS. Notably, both ascending-high and descending-medium groups had significantly higher PRS compared to the stable-low group.ConclusionsThe developmental trajectory of ADHD symptoms in the general population throughout childhood and adolescence can be reliably classified into ascending-high, stable-low, and descending-medium groups. The bilateral MFG/PCG, left ACC/mPFC, and right IPL may serve as crucial brain regions involved in attention processing, potentially determining these trajectories. Furthermore, the ascending-high pattern of ADHD symptoms exhibited the highest PRS for ADHD.

P-glycoprotein activity in vivo in older adults: physiological, pathophysiological and pharmacokinetic interplay at the blood-brain barrier

p-glycoprotein (P-gp) is an efflux transporter of xenobiotic and endogenous compounds across the blood-brain barrier (BBB). P-gp plays an essential role by limiting passage of these compounds into the brain tissue. It is susceptible to drug-drug interactions when interactors drugs are co-administrated. The efficiency of P-gp may be affected by the aging process and the development of neurodegenerative diseases. Studying this protein in older adults is therefore highly relevant for all these reasons. Understanding P-gp activity in vivo is essential when considering the physiological, pathophysiological, and pharmacokinetic perspectives, as these aspects seem to be interconnected to some extent. In vivo exploration in humans is based on neuroimaging techniques, which have been improving over the last years. The advancement of exploration and diagnostic tools is opening up new prospects for understanding P-gp activity at the BBB.

Multimodal dementia identification using lifestyle and brain lesions, a machine learning approach

Dementia diagnosis often relies on expensive and invasive neuroimaging techniques that limit access to early screening. This study proposes an innovative approach for facilitating early dementia screening by estimating diffusion tensor imaging (DTI) measures using accessible lifestyle and brain imaging factors. Conventional DTI analysis, though effective, is often hindered by high costs and limited accessibility. To address this challenge, fuzzy subtractive clustering identified 14 influential variables from the Lifestyle for Brain Health and Brain Atrophy and Lesion Index frameworks, encompassing demographics, medical conditions, lifestyle factors, and structural brain markers. A multilayer perceptron (MLP) neural network was developed using these selected variables to predict fractional anisotropy (FA), a DTI metric reflecting white matter integrity and cognitive function. The MLP model achieved promising results, with a mean squared error of 0.000 878 on the test set for FA prediction, demonstrating its potential for accurate DTI estimation without costly neuroimaging techniques. The FA values in the dataset ranged from 0 to 1, with higher values indicating greater white matter integrity. Thus, a mean squared error of 0.000 878 suggests that the model’s predictions were highly accurate compared to the observed FA values. This multifactorial approach aligns with the current understanding of dementia’s complex etiology influenced by various biological, environmental, and lifestyle factors. By integrating readily available data into a predictive model, this method enables widespread, cost-effective screening for early dementia risk assessment. The proposed accessible screening tool could facilitate timely interventions, preventive strategies, and efficient resource allocation in public health programs, ultimately improving patient outcomes and caregiver burden.

Neuroimaging for Early Diagnosis of Alzheimer's Disease: a Review.

Alzheimer's disease (AD) is a progressive neurodegenerative disease that primarily affects people above the age of 60 all around the world. As of now, the cause is unknown and there is no effective cure. The pathological changes of AD have occurred many years before the onset of the disease, and current treatment techniques can only delay the progression of the disease. Because disease-modifying therapies may be most beneficial in the early stages of AD, the clinical significance of an early diagnosis is emphasized. So far, a variety of imaging technologies and related biomarkers have been used to identify and monitor AD, but there are many imaging technologies; finding the most effective imaging technology can assist medical personnel in interpreting the early stages of AD and can also improve patient treatment opportunities. This is, therefore, the main purpose and back-ground of this study. PubMed and other repositories were used in this study to conduct a literature search with various keywords, and relevant articles were reviewed. In this review, different neuroimaging techniques are reviewed which are considered advanced tools to help establish the diagnosis, and in addition, the diagnostic utility, advantages, and limitations of contemporary AD imaging techniques are discussed. The results of the literature review and synthesis show that the prevalence of several in vivo biomarkers helps distinguish affected individuals from healthy controls in the early stages of the disease. Additionally, each current imaging method has its advantages and disadvantages, so no single imaging method is the best diagnostic modality. This article also reviews and draws conclusions on better ways to use the imaging techniques to improve the likelihood of an early diagnosis of AD. It is suggested that future research could focus on expanding the use of imaging technologies and on identifying novel biomarkers manifesting the earliest stages of AD pathology.

Is the Caudate, Putamen, and Globus Pallidus the Delusional Disorder's Trio? A Texture Analysis Study.

The neurobiological basis of delusional disorder is less explored through neuroimaging techniques than in other psychotic disorders. This study aims to provide information about the neural origins of delusional disorder (DD) by examining the neuroanatomical features of some basal nuclei with magnetic resonance imaging (MRI) texture analysis. Twenty DD patients and 20 healthy individuals were included in the study. Globus pallidus, putamen, and caudate nuclei were selected individually with a region of interest (ROI) on the axial MRI images. The entire texture analysis algorithm applied to all selected ROIs was done with an in-house software. Nuclei on both sides were taken as separate samples. There were no significant differences between groups in terms of age and gender. The average "mean, median and maximum" values of all three nuclei were decreased in DD patients. The small putamen area and the differences detected in different tissue parameters for all three nuclei in delusional disorder patients indicate that they differ in delusional disorder from normal controls (p < 0.05). The differences detected in the texture parameters for all three nuclei indicate that there is something different in the DD from in the normal controls. Neuroimaging studies with larger samples and different techniques in the future may shed light on the etiology of delusional disorder.

P-glycoprotein activity in vivo in older adults: physiological, -pathophysiological and pharmacokinetic interplay at the blood-brain barrier

p-glycoprotein (P-gp) is an efflux transporter of xenobiotic and endogenous compounds across the blood-brain barrier (BBB). P-gp plays an essential role by limiting passage of these compounds into the brain tissue. It is susceptible to drug-drug interactions when interactors drugs are co-administrated. The efficiency of P-gp may be affected by the aging process and the development of neurodegenerative diseases. Studying this protein in older adults is therefore highly relevant for all these reasons. Understanding P-gp activity in vivo is essential when considering the physiological, pathophysiological, and pharmacokinetic perspectives, as these aspects seem to be interconnected to some extent. In vivo exploration in humans is based on neuroimaging techniques, which have been improving over the last years. The advancement of exploration and diagnostic tools is opening up new prospects for understanding P-gp activity at the BBB.

Unraveling emotional regulation through multimodal neuroimaging techniques

Neuroimaging techniques have significantly advanced our understanding of emotional regulation by elucidating the neural mechanisms involved. This review synthesizes findings across multiple modalities—functional Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG), Magnetoencephalography (MEG), Positron Emission Tomography (PET), and Magnetic Resonance Spectroscopy (MRS)—to highlight key insights into emotional processing. The prefrontal cortex (PFC), amygdala, hippocampus, and insula emerge as critical brain regions in regulating emotions. fMRI studies demonstrate increased PFC activity and decreased amygdala responses during effective emotion regulation, indicating top-down control mechanisms. EEG and MEG provide insights into the temporal dynamics of emotional responses, capturing rapid changes in neural activity during emotional tasks. PET and MRS studies reveal the neurochemical basis of emotional regulation, emphasizing the roles of neurotransmitters like serotonin and dopamine. Integration of multimodal approaches, such as fMRI-EEG and fMRI-PET, enhances our understanding by combining spatial, temporal, and neurochemical specificity. Challenges include methodological limitations and the need for diverse participant samples to improve generalizability. Future research should focus on improving spatial and temporal resolution, adopting longitudinal and ecologically valid designs, and fostering interdisciplinary collaborations. These advancements hold promise for developing personalized interventions and treatments for emotional disorders by leveraging neuroimaging biomarkers and understanding the neural underpinnings of emotional regulation.

A predictive study of the efficacy of transcutaneous auricular vagus nerve stimulation in the treatment of major depressive disorder: An fMRI-based machine learning analysis

BackgroundIn order to improve taVNS efficacy, the usage of fMRI to explore the predictive neuroimaging markers would be beneficial for screening the appropriate MDD population before treatment. MethodsA total of 86 MDD patients were recruited in this study, and all subjects were conducted with the clinical scales and resting-state functional magnetic resonance imaging (fMRI) scan before and after 8 weeks’ taVNS treatment. A two-stage feature selection strategy combining Machine Learning and Statistical was used to screen out the critical brain functional connections (FC) that were significantly associated with efficacy prediction, then the efficacy prediction model was constructed for taVNS treating MDD. Finally, the model was validated by separated the responding and non-responding patients. ResultsThis study showed that taVNS produced promising clinical efficacy in the treatment of mild and moderate MDD. Eleven FCs were selected out and were found to be associated with the cortico-striatal-pallidum-thalamic loop, the hippocampus and cerebellum and the HAMD-17 scores. The prediction model was created based on these FCs for the efficacy prediction of taVNS treatment. The R-square of the conducted regression model for predicting HAMD-17 reduction rate is 0.44, and the AUC for classifying the responding and non-responding patients is 0.856. ConclusionThe study demonstrates the validity and feasibility of combining neuroimaging and machine learning techniques to predict the efficacy of taVNS on MDD, and provides an effective solution for personalized and precise treatment for MDD.

Current implications of EEG and fNIRS as functional neuroimaging techniques for motor recovery after stroke

Abstract Persistent motor deficits are highly prevalent among post-stroke survivors, contributing significantly to disability. Despite the prevalence of these deficits, the precise mechanisms underlying motor recovery after stroke remain largely elusive. The exploration of motor system reorganization using functional neuroimaging techniques represents a compelling yet challenging avenue of research. Quantitative electroencephalography (qEEG) parameters, including the power ratio index, brain symmetry index, and phase synchrony index, have emerged as potential prognostic markers for overall motor recovery post-stroke. Current evidence suggests a correlation between qEEG parameters and functional motor outcomes in stroke recovery. However, accurately identifying the source activity poses a challenge, prompting the integration of EEG with other neuroimaging modalities, such as functional near-infrared spectroscopy (fNIRS). fNIRS is nowadays widely employed to investigate brain function, revealing disruptions in the functional motor network induced by stroke. Combining these two methods, referred to as integrated fNIRS-EEG, neural activity and hemodynamics signals can be pooled out and offer new types of neurovascular coupling-related features, which may be more accurate than the individual modality alone. By harnessing integrated fNIRS-EEG source localization, brain connectivity analysis could be applied to characterize cortical reorganization associated with stroke, providing valuable insights into the assessment and treatment of post-stroke motor recovery.

Peaglet: A user-friendly probabilistic Kernel density estimation of intracranial cortical and subcortical stimulation sites

BackgroundData on human brain function obtained with direct electrical stimulation (DES) in neurosurgical patients have been recently integrated and combined with modern neuroimaging techniques, allowing a connectome-based approach fed by intraoperative DES data. Within this framework is crucial to develop reliable methods for spatial localization of DES-derived information to be integrated within the neuroimaging workflow. New methodTo this aim, we applied the Kernel Density Estimation for modelling the distribution of DES sites from different patients into the MNI space. The algorithm has been embedded in a MATLAB-based User Interface, Peaglet. It allows an accurate probabilistic weighted and unweighted estimation of DES sites location both at cortical level, by using shortest path calculation along the brain 3D geometric topology, and subcortical level, by using a volume-based approach. ResultsWe applied Peaglet to investigate spatial estimation of cortical and subcortical stimulation sites provided by recent brain tumour studies. The resulting NIfTI maps have been anatomically investigated with neuroimaging open-source tools. Comparison with existing methodsPeaglet processes differently cortical and subcortical data following their distinguishing geometrical features, increasing anatomical specificity of DES-related results and their reliability within neuroimaging environments. ConclusionsPeaglet provides a robust probabilistic estimation of the cortical and subcortical distribution of DES sites going beyond a region of interest approach, respecting cortical and subcortical intrinsic geometrical features. Results can be easily integrated within the neuroimaging workflow to drive connectomic analysis.

Magnetic Resonance Imaging in the Study of Catatonia: Use of DWI and Resting State fMRI

Background: catatonia is the focus of neurophysiological research as a syndrome with unspecified pathogenesis. Modern neuroimaging techniques contribute to the understanding of the pathophysiological mechanisms of this disorder. The aim was to conduct a systematic review of the scientific literature to confirm that catatonia is associated with structural and functional changes in the brain. The analysis made up researches using diffusion MRI for judgement on indirect measure of changes in white or gray matter density using a fractional anisotropy (FA) and resting state functional MRI for assessment a measure of connectivity. Materials and methods: PubMed, ScienceDirect and Mendeley databases were searched using the search terms (and their derivatives) for: “catatonia”, “resting state functional magnetic resonance imaging” and “catatonia”, “diffusion weighted magnetic resonance imaging”. The search yielded 147 publications for preliminary screening, of which 96 were on fMRI of catatonia and 51 on dMRI. During the screening stage, duplicates and articles that could not be accessed were removed. This left 21 fMRI articles and 18 dMRI articles. After which the articles were checked for compliance with the inclusion criteria: 1) original full-text articles; 2) identification of catatonia not caused by a somatic disease and verified using the Bush–Francis and/or Northoff psychometric scales; 3) age of the examined 18 years and over. 3 fMRI and 3 dMRI articles were included. Conclusions: aberrations of FA indicators were found in catatonia, which may be associated with the density of the white matter of the brain. Changes in connectivity in the somatosensory network have been identified, which allows to consider these disorders as potential markers of catatonia. To confirm the hypothesis and results obtained, further research is required due to the small number of publications on this topic.

Neurological Basis in Schizophrenia

The aim of the article is to investigate the neurocognitive basis of schizophrenia. Schizophrenia is a group of psychotic disorders in which personal, social, occupational functioning of the individual deteriorate. Schizophrenia is characterized by having Positive, Negative and Psychomotor symptoms. The previously known casual factors include Hormone Imbalances, Physical Deprivation, Pregnancy or delivery complications, Psychological Stress, Faulty Learning patterns etc. The research was undertaken as a secondary research measure wherein 15 published research articles were reviewed to understand and analyse the various precipitating and neurological basis that may lead to schizophrenia. In order to investigate the various causal factors, a variety of neuroimaging techniques including EEG, fMRI, MRI, Post-mortem analysis were undertaken. The findings revealed that various sub-domains including Prenatal, Genetics generally provide the huge understanding that the disorder will be developed.

Novel Biomarkers for Alzheimer's Disease: Plasma Neurofilament Light and Cerebrospinal Fluid.

Neurodegenerative disorders such as Alzheimer's disease (AD) represent an increasingly significant public health concern. As clinical diagnosis faces challenges, biomarkers are becoming increasingly important in research, trials, and patient assessments. While biomarkers like amyloid-β peptide, tau proteins, CSF levels (Aβ, tau, and p-tau), and neuroimaging techniques are commonly used in AD diagnosis, they are often limited and invasive in monitoring and diagnosis. For this reason, blood-based biomarkers are the optimal choice for detecting neurodegeneration in brain diseases due to their noninvasiveness, affordability, reliability, and consistency. This literature review focuses on plasma neurofilament light (NfL) and CSF NfL as blood-based biomarkers used in recent AD diagnosis. The findings revealed that the core CSF biomarkers of neurodegeneration (T-tau, P-tau, and Aβ42), CSF NFL, and plasma T-tau were strongly associated with Alzheimer's disease, and the core biomarkers were strongly associated with mild cognitive impairment due to Alzheimer's disease. Elevated levels of plasma and cerebrospinal fluid NfL were linked to decreased [18F]FDG uptake in corresponding brain areas. In participants with Aβ positivity (Aβ+), NfL correlated with reduced metabolism in regions susceptible to Alzheimer's disease. In addition, CSF NfL levels correlate with brain atrophy and predict cognitive changes, while plasma total tau does not. Plasma P-tau, especially in combination with Aβ42/Aβ40, is promising for symptomatic AD stages. Though not AD-exclusive, blood NfL holds promise for neurodegeneration detection and assessing treatment efficacy. Given the consistent levels of T-tau, P-tau, Aβ42, and NFL in CSF, their incorporation into both clinical practice and research is highly recommended.