Understanding Functional Neurological Disorder: Recent Insights and Diagnostic Challenges.

Functional neurological disorder (FND), formerly called conversion disorder, is a condition characterized by neurological symptoms that lack an identifiable organic purpose. These signs, which can consist of motor, sensory, or cognitive disturbances, are not deliberately produced and often vary in severity. Its diagnosis is predicated on clinical evaluation and the exclusion of other medical or psychiatric situations. Its treatment typically involves a multidisciplinary technique addressing each of the neurological symptoms and underlying psychological factors via a mixture of medical management, psychotherapy, and supportive interventions. Recent advances in neuroimaging and a deeper exploration of its epidemiology, pathophysiology, and clinical presentation have shed new light on this disorder. This paper synthesizes the current knowledge on FND, focusing on its epidemiology and underlying mechanisms, neuroimaging insights, and the differentiation of FND from feigning or malingering. This review highlights the phenotypic heterogeneity of FND and the diagnostic challenges it presents. It also discusses the significant role of neuroimaging in unraveling the complex neural underpinnings of FND and its potential in predicting treatment response. This paper underscores the importance of a nuanced understanding of FND in informing clinical practice and guiding future research. With advancements in neuroimaging techniques and growing recognition of the disorder's multifaceted nature, the paper suggests a promising trajectory toward more effective, personalized treatment strategies and a better overall understanding of the disorder.

Inclusion body myositis. Genetics, biomarkers and muscle biopsy.

Sporadic inclusion body myositis is the most common idiopathic inflammatory myopathy over the age of 50, with a male-to-female ratio of 3:1. Symptoms onset before age of 60 occurs in 18-20% of patients, with a delay in diagnosis of 5 to 8 years.The classic clinical presentation of SIBM consists of proximal leg and distal arm weakness, and most commonly patients present early slowly progressive quadriceps weakness which leads to falls and to difficulties in climbing stairs, while less common the initial complaints refer to finger flexor weakness and atrophy, foot drop, or dysphagia, and rare presentations include prominent forearm weakness, sparing the quadriceps. The aetiopathogenesis of the disease remains unclear and despite some preliminary promising results, to the day there is no effective treatment.The diagnosis of SIBM is based on the clinical presentation and the histopathological findings in muscle biopsy, however increasing evidence on genetics and paraclinical biomarkers has recently come to light giving new insights on the pathogenesis, the diagnosis and the potential treatment of the disease. In the present study we aim to review the histopathological findings, genetics and blood biomarkers, and to review the role of muscle biopsy in the diagnosis of SIBM.

A Personalized User Authentication System Based on EEG Signals.

Conventional biometrics have been employed in high-security user-authentication systems for over 20 years now. However, some of these modalities face low-security issues in common practice. Brainwave-based user authentication has emerged as a promising alternative method, as it overcomes some of these drawbacks and allows for continuous user authentication. In the present study, we address the problem of individual user variability, by proposing a data-driven Electroencephalography (EEG)-based authentication method. We introduce machine learning techniques, in order to reveal the optimal classification algorithm that best fits the data of each individual user, in a fast and efficient manner. A set of 15 power spectral features (delta, theta, lower alpha, higher alpha, and alpha) is extracted from three EEG channels. The results show that our approach can reliably grant or deny access to the user (mean accuracy of 95.6%), while at the same time poses a viable option for real-time applications, as the total time of the training procedure was kept under one minute.

The role of neurofilament light chain in frontotemporal dementia: a meta-analysis.

Frontotemporal dementia (FTD) is the second most frequent dementia, after Alzheimer's, in patients under the age of 65. It encompasses clinical entities characterized by behavioral, language, and executive control dysfunction. Neurofilament light chain (NfL) is a new, non-disease specific, widely studied biomarker indicative of axonal injury and degeneration. Various studies have previously explored the role of NfL in the diagnostic process, monitoring, and prognosis of dementia. The current systematic review and meta-analysis include all the available data concerning the role of NfL in frontotemporal dementia and its use as a potential biomarker in differentiating patients with FTD from (a) healthy individuals, (b) Alzheimer's dementia, (c) Dementia with Lewy bodies, (d) Motor Neuron disease, (e) Parkinsonian syndromes, and (f) psychiatric disorders. We also analyze the utility of NfL in distinguishing specific FTD subgroups. Neurofilament light chain has a potential role in differentiating patients with frontotemporal dementia from healthy controls, patients with Alzheimer's dementia, and psychiatric disorders. Higher NfL levels were also noted in patients with semantic primary progressive aphasia (PPA) when compared with behavioral FTD and non-fluent PPA patients. Further studies exploring the use of NfL in frontotemporal dementia are needed.

Electroencephalogram in dementia with Lewy bodies: a systematic review.

Dementia with Lewy bodies (DLB) belongs to the spectrum of Lewy body dementia (LBD) that also encompasses Parkinson's disease dementia (PDD). It is a common neurodegenerative disorder characterized by memory decline, cognitive fluctuations, visual hallucinations, autonomic nervous system disturbance, REM sleep behavior disorder, and parkinsonism. Definite diagnosis can be established only through neuropathological confirmation of Lewy bodies' presence in brain tissue. Probable or possible diagnosis relies upon clinical features, imaging, polysomnography, and electroencephalogram (EEG) findings. Potential neurophysiological biomarkers for the diagnosis, management, and evaluation of treatment-response in DLB should be affordable and widely available outside academic centers. Increasing evidence supports the use of quantitative EEG (qEEG) as a potential DLB biomarker, with promising results in discriminating DLB from other dementias and in identifying subjects who are on the trajectory to develop DLB. Several studies evaluated the diagnostic value of EEG in DLB. Visual analysis and qEEG techniques have been implemented, showing a superiority of the last in terms of sensitivity and objectivity. In this systematic review, we attempt to provide a general synthesis of the current knowledge on EEG application in DLB. We review the findings from original studies and address the issues remaining to be further clarified.

Cognitive Status Epilepticus: Two Case Reports.

Cognitive status epilepticus is an uncommon form of focal status epilepticus presenting with a dysfunction of language, thinking or associated higher cortical functions. The absence of ictal manifestations can be misleading and delay a prompt diagnosis. Here we present two patients; one with amnesic and one with aphasic status epilepticus. Through these cases, we aim to highlight the value of EEG performance early in the diagnostic work-up and early antiepileptic drug initiation in cases where an epileptic disorder cannot be excluded.

A Voxel-Wise Meta-Analysis on the Cerebellum in Essential Tremor.

Background and Objectives: Essential tremor is a chronic progressive neurological condition. The clinical presentation of essential tremor is heterogeneous and includes involuntary tremor on hands or arms and progressively on head, jaw, and voice. More extensive and complex symptoms may also be noticed in several patients. Many studies have been carried out to identify biomarkers to help the diagnosis, however, all the efforts have not shown any substantial results yet. Materials and Methods: Here, we aimed to perform a voxel-based meta-analysis using a dedicated cerebellar mask to clarify whether the results from the previous studies are robust and have any clinical significance. We included studies with a total of 377 essential tremor patients and 338 healthy control individuals. Results: A significant regional decrease in the volume of the gray matter was detected in the right cerebellar hemispheric lobule IV/V, and in the cerebellar vermic lobule IV/V. Conclusions: This is the first study focused on the cerebellum and using a specific cerebellar mask, which increases the sensitivity. It showed regional statistically significant changes that could not be seen in the whole-brain analysis.

Gray Matter Changes in Juvenile Myoclonic Epilepsy. A Voxel-Wise Meta-Analysis.

Background and Objectives. Juvenile myoclonic epilepsy (JME) is an idiopathic generalized epileptic syndrome, with a genetic basis clinically identified by myoclonic jerks of the upper limbs upon awaking, generalized tonic-clonic seizures and less frequent absences. Although the brain magnetic resonance imaging (MRI) is by definition normal, computer-based Voxel-Based morphometry studies have shown a number of volumetric changes in patients with juvenile myoclonic epilepsy. Thus, the aim of the present Voxel-Wise Meta-Analysis was to determine the most consistent regional differences of gray matter volume between JME patients and healthy controls. Materials and Methods. The initial search returned 31 studies. After excluding reviews and studies without control groups or without detailed peak coordinates, 12 studies were finally included in the present meta-analysis. The total number of JME patients was 325, and that of healthy controls was 357. Results. Our study showed a statistically significant increase of the gray matter in the left median cingulate/paracingulate gyri, the right superior frontal gyrus, the left precentral gyrus, the right supplementary motor area and left supplementary motor area. It also showed a decrease in the gray matter volume in the left thalamus, and in the left insula. Conclusions. Our findings could be related to the functional deficits and changes described by previous studies in juvenile myoclonic epilepsy. In this way, the volumetric changes found in the present study could be related to the impaired frontal lobe functions, the emotional dysfunction and impaired pain empathy, and to the disrupted functional connectivity of supplementary motor areas described in JME. It additionally shows changes in the volume of the left thalamus, supporting the theory of thalamocortical pathways being involved in the pathogenesis of juvenile myoclonic epilepsy.

YKL-40 as a Potential Biomarker for the Differential Diagnosis of Alzheimer's Disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder, associated with extensive neuronal loss, dendritic and synaptic changes resulting in significant cognitive impairment. An increased number of studies have given rise to the neuroinflammatory hypothesis in AD. It is widely accepted that AD brains show chronic inflammation, probably triggered by the presence of insoluble amyloid beta deposits and neurofibrillary tangles (NFT) and is also related to the activation of neuronal death cascade. In the present study we aimed to investigate the role of YKL-40 levels in the cerebrospinal fluid (CSF) in the diagnosis of AD, and to discuss whether there are further potential roles of this protein in the management and treatment of AD. We conducted an online search on PubMed, Web of Science, and the Cochrane library databases from 1990 to 2021. The quantitative analysis showed that the levels of YKL-40 were significantly higher in Alzheimer's disease compared to controls, to mild cognitive impairment (MCI) AD (MCI-AD) and to stable MCI. They were also increased in MCI-AD compared to stable MCI. The present study shows that the CSF levels of YKL-40 could be potentially used as a biomarker for the prognosis of mild cognitive impairment and the likelihood of progression to AD, as well as for the differential diagnosis between AD and MCI.

Serum BDNF Levels in Acute Stroke: A Systematic Review and Meta-Analysis.

Background and objectives: Brain-derived neurotrophic factor (BDNF) is one of the most studied neurotrophins. Low BDNF concentrations have been noted in patients with traditional cardiovascular disease risk factors and have been associated with the increased risk of stroke/transient ischemic attack (TIA). We aimed to study the correlation of BDNF serum levels with acute stroke severity and its potential role as a biomarker in predicting functional outcome. Materials and methods: We systematically searched PubMed, Web of Science, and the Cochrane database using specific keywords. The endpoints examined were the correlation of BDNF with functional outcome, the National Institute of Health stroke scale (NIHSS) measured at the acute phase, and stroke infarct volume. We also compared serum BDNF levels between stroke patients and healthy controls. Results: Twenty-six records were included from the initial 3088 identified. Twenty-five studies reported NIHSS and BDNF levels on the first day after acute stroke. Nine studies were further meta-analyzed. A statistically significant negative correlation between NIHSS and BDNF levels during the acute phase of stroke was noted (COR: -0.3013, 95%CI: (-0.4725; -0.1082), z = -3.01, p = 0.0026). We also noted that BDNF levels were significantly lower in patients with stroke compared to healthy individuals. Due to the heterogeneity of studies, we only conducted a qualitative analysis regarding serum BDNF and functional outcome, while no correlation between BDNF levels and stroke infarct volume was noted. Conclusions: We conclude that in the acute stroke phase, stroke severity is negatively correlated with BDNF levels. Concurrently, patients with acute stroke have significantly lower BDNF levels in serum compared to healthy controls. No correlations between BDNF and stroke infarct volume or functional outcome at follow-up were noted.

Alpha-synuclein Levels in the Differential Diagnosis of Lewy Bodies Dementia and Other Neurodegenerative Disorders: A Meta-analysis.

SUBJECTIVES: Lewy body dementia (LBD) is the second most common type of neurodegenerative dementia after Alzheimer disease (AD). It is characterized by the accumulation of Lewy bodies and Lewy neurites which are composed of aggregated phosphorylated alpha-synuclein, which is a presynaptic neuronal protein genetically and neuropathologically linked to Parkinson disease and to LBD. Alpha-synuclein is thought to contribute to LBD pathogenesis and to linked to disruption of cellular homeostasis and neuronal death, through effects on various intracellular targets, including synaptic function. METHODS: In the present study, we did a meta-analysis on the reliability of alpha-synuclein levels in the cerebrospinal fluid (CSF) for the discrimination between LBD and other neurodegenerative disorders including AD, Parkinson disease (PD) dementia, progressive supranuclear palsy (PSP), multiple system atrophy (MSA) and frontotemporal dementia (FTD). RESULTS: CSF alpha-synuclein levels were significantly different in LBD compared with AD, but no statistical difference was found between LBD, and dementia in PD, MSA, PSP, and FTD. CONCLUSION: Alpha-synuclein levels in the CSF can be used for the discrimination between LBD and AD, but not LBD and other neurodegenerative disorders such as dementia in PD, MSA, FTD, and PSP.

A meta-analysis on CSF neurogranin levels for the diagnosis of Alzheimer's disease and mild cognitive impairment.

OBJECTIVE: Neurogranin is a postsynaptic protein involved in long-term potentiation and synaptic plasticity. Recent studies have shown increased neurogranin levels in the cerebrospinal fluid of Alzheimer's disease patients, and in patients with mild cognitive impairment. METHOD: We searched the online databases for studies on neurogranin cerebrospinal fluid levels in Alzheimer's disease, mild cognitive impairment and other neurodegenerative disorders, and we did a meta-analysis to clarify whether this can be a reliable biomarker for the diagnosis of Alzheimer's disease, and the discrimination from other disorders. RESULTS: The present meta-analysis showed that neurogranin CSF levels are significantly higher in AD patients compared to NC [SMD: 268.26, 95% CI (143.47, 393.04), Z = 4.21, P = 0.0001], MCI [SMD: 23.45 (15.97, 30.92), Z = 6.15, P < 0.00001], FTD [SMD: 1.91 (0.92, 2.89), Z = 3.80, P < 0.0001], but no significant difference was found between AD and LBD [SMD: 138.51 (- 14.92, 291.95), Z = 1.77, P = 0.08]. Comparison of stable MCI and MCI that progressed to AD showed significantly higher levels in the CSF of MCI patients who progressed to AD, compared to stable MCI patients [SMD: 230.84 (12.54, 449.14), Z = 2.07, P = 0.04]. Neurogranin can also be a useful biomarker for the differentiation MCI and NC, but not between MCI and FTD or LBD. CONCLUSION: Neurogranin could be added to the panel of existing biomarkers for a more accurate diagnosis and progress of AD and assessment of underlying pathological changes in the brain.

Neuroimaging and neuropathological findings in essential tremor.

Essential tremor is a chronic neurological syndrome of heterogenous clinical phenotypes and multiple etiologies. Numerous studies have been done in order to investigate the pathological, neuroimaging, physiological, and clinical features of essential tremor; however, a clear pathophysiological mechanism has not been identified. One of the brain structures has been extensively investigated at the macroscopic and the microscopic level in the cerebellum. In the present study, we aim to discuss the main neuroimaging and neuropathological changes of the cerebellum in essential tremor.

The Parkinson fatigue scale: an evaluation of its validity and reliability in Greek Parkinson's disease patients.

OBJECTIVE: Fatigue is one of the most frequent and important nonmotor symptoms of patients with Parkinson disease (PD), affecting quality of life. Although, in some cases, it may be a severe and debilitating complaint, it remains relatively unexplored. The PFS-16 is a fatigue measure, specifically designed for PD patients. The aim of this study was to investigate the psychometric properties of Parkinson fatigue scale (PFS-16) in Greek PD patients. METHODS: In total, 99 patients with PD were assessed. The following psychometric properties were tested: data quality, floor/ceiling effects, reliability (internal consistency, test-retest reliability), and construct validity. Construct validity was evaluated by examining correlations with other variables including other fatigue measures such as Fatigue Severity Scale (FSS) and the vitality scale (SF-VT) of SF-36. Moreover, assumptions were explored about "known" groups concerning fatigue. RESULTS: The mean score for the PFS-16 was 2.95 (± 0.91); acceptability was good with negligible floor and ceiling effects. Results showed high internal consistency (Cronbach's alpha, 0.96) and test-retest reliability (ICC, 0.93). Strong correlations were observed between the PFS-16 and other fatigue (FFS and SF-VT) measures (rs = 0.77 and - 0.70, p < 0.001), revealing appropriate validity. Furthermore, predictions for "known" groups validity were verified. CONCLUSION: The Greek version of the PFS-16 showed satisfactory reliability and validity and thus can be regarded as a useful tool in assessing fatigue in PD.

Epileptic tissue localization using graph-based networks in the high frequency oscillation range of intracranial electroencephalography.

PURPOSE: High frequency oscillations (HFOs) are an emerging biomarker of epilepsy. However, very few studies have investigated the functional connectivity of interictal iEEG signals in the frequency range of HFOs. Here, we study the corresponding functional networks using graph theory, and we assess their predictive value for automatic electrode classification in a cohort of 20 drug resistant patients. METHODS: Coherence-based connectivity analysis was performed on the iEEG recordings, and six different local graph measures were computed in both sub-bands of the HFO frequency range (80-250 Hz and 250-500 Hz). Correlation analysis was implemented between the local graph measures and the ripple and fast ripple rates. Finally, the WEKA software was employed for training and testing different predictive models on the aforementioned local graph measures. RESULTS: The ripple rate was significantly correlated with five out of six local graph measures in the functional network. For fast ripples, their rate was also significantly (but negatively) correlated with most of the local metrics. The results from WEKA showed that the Logistic Regression algorithm was able to classify highly HFO-contaminated electrodes with an accuracy of 82.5 % for ripples and 75.4 % for fast ripples. CONCLUSION: Functional connectivity networks in the HFO band could represent an alternative to the direct use of distinct HFO events, while also providing important insights about hub epileptic areas that can represent possible surgical targets. Automatic electrode classification through FC-based classifiers can help bypass the burden of manual HFO annotation, providing at the same time similar amount of information about the epileptic tissue.

Epidemiology, Risk Factors, and Biomarkers of Post-Traumatic Epilepsy: A Comprehensive Overview.

This paper presents an in-depth exploration of Post-Traumatic Epilepsy (PTE), a complex neurological disorder following traumatic brain injury (TBI), characterized by recurrent, unprovoked seizures. With TBI being a global health concern, understanding PTE is crucial for effective diagnosis, management, and prognosis. This study aims to provide a comprehensive overview of the epidemiology, risk factors, and emerging biomarkers of PTE, thereby informing clinical practice and guiding future research. The epidemiological aspect of the study reveals PTE as a significant contributor to acquired epilepsies, with varying incidence influenced by injury severity, age, and intracranial pathologies. The paper delves into the multifactorial nature of PTE risk factors, encompassing clinical, demographic, and genetic elements. Key insights include the association of injury severity, intracranial hemorrhages, and early seizures with increased PTE risk, and the roles of age, gender, and genetic predispositions. Advancements in neuroimaging, electroencephalography, and molecular biology are presented, highlighting their roles in identifying potential PTE biomarkers. These biomarkers, ranging from radiological signs to electroencephalography EEG patterns and molecular indicators, hold promise for enhancing PTE pathogenesis understanding, early diagnosis, and therapeutic guidance. The paper also discusses the critical roles of astrocytes and microglia in PTE, emphasizing the significance of neuroinflammation in PTE development. The insights from this review suggest potential therapeutic targets in neuroinflammation pathways. In conclusion, this paper synthesizes current knowledge in the field, emphasizing the need for continued research and a multidisciplinary approach to effectively manage PTE. Future research directions include longitudinal studies for a better understanding of TBI and PTE outcomes, and the development of targeted interventions based on individualized risk profiles. This research contributes significantly to the broader understanding of epilepsy and TBI.