The application of artificial intelligence to understand the pathophysiological basis of psychogenic nonepileptic seizures

FDA-approved machine learning algorithms in neuroradiology: A systematic review of the current evidence for approval

Over the past decade, machine learning (ML) and artificial intelligence (AI) have become increasingly prevalent in the medical field. In the United States, the Food and Drug Administration (FDA) is responsible for regulating AI algorithms as "medical devices" to ensure patient safety. However, recent work has shown that the FDA approval process may be deficient. In this study, we evaluate the evidence supporting FDA-approved neuroalgorithms, the subset of machine learning algorithms with applications in the central nervous system (CNS), through a systematic review of the primary literature. Articles covering the 53 FDA-approved algorithms with applications in the CNS published in PubMed, EMBASE, Google Scholar and Scopus between database inception and January 25, 2022 were queried. Initial searches identified 1505 studies, of which 92 articles met the criteria for extraction and inclusion. Studies were identified for 26 of the 53 neuroalgorithms, of which 10 algorithms had only a single peer-reviewed publication. Performance metrics were available for 15 algorithms, external validation studies were available for 24 algorithms, and studies exploring the use of algorithms in clinical practice were available for 7 algorithms. Papers studying the clinical utility of these algorithms focused on three domains: workflow efficiency, cost savings, and clinical outcomes. Our analysis suggests that there is a meaningful gap between the FDA approval of machine learning algorithms and their clinical utilization. There appears to be room for process improvement by implementation of the following recommendations: the provision of compelling evidence that algorithms perform as intended, mandating minimum sample sizes, reporting of a predefined set of performance metrics for all algorithms and clinical application of algorithms prior to widespread use. This work will serve as a baseline for future research into the ideal regulatory framework for AI applications worldwide.

Perspective of artificial intelligence in healthcare data management: A journey towards precision medicine

Mounting evidence has highlighted the implementation of big data handling and management in the healthcare industry to improve the clinical services. Various private and public companies have generated, stored, and analyzed different types of big healthcare data, such as omics data, clinical data, electronic health records, personal health records, and sensing data with the aim to move in the direction of precision medicine. Additionally, with the advancement in technologies, researchers are curious to extract the potential involvement of artificial intelligence and machine learning on big healthcare data to enhance the quality of patient's lives. However, seeking solutions from big healthcare data requires proper management, storage, and analysis, which imposes hinderances associated with big data handling. Herein, we briefly discuss the implication of big data handling and the role of artificial intelligence in precision medicine. Further, we also highlighted the potential of artificial intelligence in integrating and analyzing the big data that offer personalized treatment. In addition, we briefly discuss the applications of artificial intelligence in personalized treatment, especially in neurological diseases. Lastly, we discuss the challenges and limitations imposed by artificial intelligence in big data management and analysis to hinder precision medicine.

Morphometric correlates in patients with functional seizures with and without comorbid epilepsy

OBJECTIVE

Functional seizures (FS) or psychogenic, non-epileptic seizures (PNES) are episodic alterations of behaviour with similar semiology to epileptic seizures but which are not caused by epileptic brain activity. Epilepsy patients show a high risk in developing FS; therefore, the purpose of this study is to examine morphometric correlates in patients with FS as well as in epilepsy patients with FS by comparing them separately to healthy controls (HC).

METHODS

Twenty-one clinical three-dimensional (3D) T1-magnetic resonance imaging (MRI) scans of patients with FS (FS group) and 15 patients with FS and epilepsy (EFS group) were retrospectively compared with one control group of 21 age- and gender-matched HC. Two separate general linear model analyses were conducted via FreeSurfer version 6.0.

RESULTS

The study population consisted of 21 FS patients (66.7% females, n = 14) with a median age at the time of the scan of 24 years (range 17-44 years); 15 EFS patients (80% females, n = 12) with a median age at the time of the scan of 27 years (range 16-43 years); and 21 healthy subjects (66.7% females, n = 14) with a median age at the time of the scan of 24 years (range 19-38 years). Both patient groups showed an increased Cth in the right prefrontal lobe: in the FS group in the right superior frontal, rostral middle frontal gyri and the right orbitofrontal cortex and, in the EFS group, in the right superior frontal gyrus and the right orbitofrontal cortex. Decreases in Cth were present in the right lateral occipital lobe in the FS group, while also in both hemispheres in the EFS group, namely the left paracentral, superior frontal, caudal middle frontal, lateral occipital and right superior frontal gyri. Neither group showed changes in curvature.

CONCLUSION

These results suggest alterations in regions of emotional processing and executive control in patients with FS regardless of the presence of epilepsy.

[Psychogenic non-epileptic seizures in autistic spectrum disorder]

Psychogenic non-epileptic seizures (PNES) are paroxysmal conditions that resemble epileptic seizures, but are not accompanied by epileptiform activity on the electroencephalogram and are not associated with other neurological or somatic disorders. Unrecognized PNES places a heavy burden on the patient and family, and on the health care system. Among many possible combinations of PNES with psychiatric disorders, autistic spectrum disorders are the least studied. This article presents a case of a 19-year-old female patient with autistic spectrum disorder and paroxysmal events and the presence of potentially epileptogenic changes in the brain. A multidisciplinary approach made it possible to diagnose PNEP in the patient.

Intelligent Algorithm-Based Ultrasound Images in Evaluation of Therapeutic Effects of Radiofrequency Ablation for Liver Tumor and Analysis on Risk Factors of Postoperative Infection

This research aimed to explore the therapeutic effects of radiofrequency ablation (RFA) for liver tumors and to investigate the postoperative infection factors. Specifically, 80 patients with liver tumors undergoing ultrasound-guided FRA were selected as research subjects. They were diagnosed in the hospital. An intelligent fitting (IF) algorithm was compared with a genetic algorithm (GA) and applied to the RFA of the 80 patients. It was found that the running time of the IF algorithm was about 0.2 times than that of the GA, demonstrating better global searching capabilities. The mean diameter of single liver tumors was (3.45 ± 1.24) cm, and the complete ablation rate of tumors with diameters less than 3 cm was 87.88%, that of tumors with diameters of 3-5 cm was 72.92%, and that of tumors with a diameter of more than 5 cm was 63.33%. Posttreatment, the AST level decreased significantly and the ALB level increased significantly, and the difference was notable (P < 0.05P<); the TBIL level (36.8 ± 9.7 umol/L) was lower than prior treatment (17.9 ± 8.5 umol/L) and the ALT level (45.2 ± 6.8 g/L) was lower than prior treatment (19.6 ± 5.7 g/L), showing a notable difference (P < 0.05P<). The diameter, whether there was great vessel invasion, and TNM staging were associated with infection after RFA, and the difference was notable. The ultrasound images can effectively evaluate the therapeutic effects of RFA and the degree of inactivation of liver tumors. In addition, the tumor stage was an independent risk factor for postoperative infection.

Clinical MRI morphological analysis of functional seizures compared to seizure-naïve and psychiatric controls

PURPOSE

Functional seizures (FS), also known as psychogenic nonepileptic seizures (PNES), are physical manifestations of acute or chronic psychological distress. Functional and structural neuroimaging have identified objective signs of this disorder. We evaluated whether magnetic resonance imaging (MRI) morphometry differed between patients with FS and clinically relevant comparison populations.

METHODS

Quality-screened clinical-grade MRIs were acquired from 666 patients from 2006 to 2020. Morphometric features were quantified with FreeSurfer v6. Mixed-effects linear regression compared the volume, thickness, and surface area within 201 regions-of-interest for 90 patients with FS, compared to seizure-naïve patients with depression (n = 243), anxiety (n = 68), and obsessive-compulsive disorder (OCD, n = 41), respectively, and to other seizure-naïve controls with similar quality MRIs, accounting for the influence of multiple confounds including depression and anxiety based on chart review. These comparison populations were obtained through review of clinical records plus research studies obtained on similar scanners.

RESULTS

After Bonferroni-Holm correction, patients with FS compared with seizure-naïve controls exhibited thinner bilateral superior temporal cortex (left 0.053 mm, p = 0.014; right 0.071 mm, p = 0.00006), thicker left lateral occipital cortex (0.052 mm, p = 0.0035), and greater left cerebellar white-matter volume (1085 mm³, p = 0.0065). These findings were not accounted for by lower MRI quality in patients with FS.

CONCLUSIONS

These results reinforce prior indications of structural neuroimaging correlates of FS and, in particular, distinguish brain morphology in FS from that in depression, anxiety, and OCD. Future work may entail comparisons with other psychiatric disorders including bipolar and schizophrenia, as well as exploration of brain structural heterogeneity within FS.

Multi-centre classification of functional neurological disorders based on resting-state functional connectivity

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Using machine learning on multi-centre data, FND patients were successfully classified with an accuracy of 72%.

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The angular- and supramarginal gyri, cingular- and insular cortex, and the hippocampus were the most discriminant regions.

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To provide diagnostic utility, future studies must include patients with similar symptoms but different diagnoses.

Background

Patients suffering from functional neurological disorder (FND) experience disabling neurological symptoms not caused by an underlying classical neurological disease (such as stroke or multiple sclerosis). The diagnosis is made based on reliable positive clinical signs, but clinicians often require additional time- and cost consuming medical tests and examinations. Resting-state functional connectivity (RS FC) showed its potential as an imaging-based adjunctive biomarker to help distinguish patients from healthy controls and could represent a “rule-in” procedure to assist in the diagnostic process. However, the use of RS FC depends on its applicability in a multi-centre setting, which is particularly susceptible to inter-scanner variability. The aim of this study was to test the robustness of a classification approach based on RS FC in a multi-centre setting.

Methods

This study aimed to distinguish 86 FND patients from 86 healthy controls acquired in four different centres using a multivariate machine learning approach based on whole-brain resting-state functional connectivity. First, previously published results were replicated in each centre individually (intra-centre cross-validation) and its robustness across inter-scanner variability was assessed by pooling all the data (pooled cross-validation). Second, we evaluated the generalizability of the method by using data from each centre once as a test set, and the data from the remaining centres as a training set (inter-centre cross-validation).

Results

FND patients were successfully distinguished from healthy controls in the replication step (accuracy of 74%) as well as in each individual additional centre (accuracies of 73%, 71% and 70%). The pooled cross validation confirmed that the classifier was robust with an accuracy of 72%. The results survived post-hoc adjustment for anxiety, depression, psychotropic medication intake, and symptom severity. The most discriminant features involved the angular- and supramarginal gyri, sensorimotor cortex, cingular- and insular cortex, and hippocampal regions. The inter-centre validation step did not exceed chance level (accuracy below 50%).

Conclusions

The results demonstrate the applicability of RS FC to correctly distinguish FND patients from healthy controls in different centres and its robustness against inter-scanner variability. In order to generalize its use across different centres and aim for clinical application, future studies should work towards optimization of acquisition parameters and include neurological and psychiatric control groups presenting with similar symptoms.

A Comprehensive Survey on the Detection, Classification, and Challenges of Neurological Disorders

Simple Summary

This study represents a resourceful review article that can deliver resources on neurological diseases and their implemented classification algorithms to reveal the future direction of researchers. Researchers interested in studying neurological diseases and previously implemented techniques in this field can follow this article. Various challenges occur in detecting different stages of the disorders. A limited amount of labeled and unlabeled datasets and other limitations is represented in this article to assist them in finding out the directions. The authors’ purpose for composing this article is to make a straightforward and concrete path for researchers to quickly find the way and the scope in this field for implementing future research on neurological disease detection.

Abstract

Neurological disorders (NDs) are becoming more common, posing a concern to pregnant women, parents, healthy infants, and children. Neurological disorders arise in a wide variety of forms, each with its own set of origins, complications, and results. In recent years, the intricacy of brain functionalities has received a better understanding due to neuroimaging modalities, such as magnetic resonance imaging (MRI), magnetoencephalography (MEG), and positron emission tomography (PET), etc. With high-performance computational tools and various machine learning (ML) and deep learning (DL) methods, these modalities have discovered exciting possibilities for identifying and diagnosing neurological disorders. This study follows a computer-aided diagnosis methodology, leading to an overview of pre-processing and feature extraction techniques. The performance of existing ML and DL approaches for detecting NDs is critically reviewed and compared in this article. A comprehensive portion of this study also shows various modalities and disease-specified datasets that detect and records images, signals, and speeches, etc. Limited related works are also summarized on NDs, as this domain has significantly fewer works focused on disease and detection criteria. Some of the standard evaluation metrics are also presented in this study for better result analysis and comparison. This research has also been outlined in a consistent workflow. At the conclusion, a mandatory discussion section has been included to elaborate on open research challenges and directions for future work in this emerging field.

Cortical thickness in default mode network hubs correlates with clinical features of dissociative seizures

BACKGROUND

Dissociative seizures (DS) are a common subtype of functional neurological disorder (FND) with an incompletely understood pathophysiology. Here, gray matter variations and their relationship to clinical features were investigated.

METHODS

Forty-eight patients with DS without neurological comorbidities and 43 matched clinical control patients with syncope with structural brain MRIs were identified retrospectively. FreeSurfer-based cortical thickness and FSL FIRST-based subcortical volumes were used for quantitative analyses, and all findings were age and sex adjusted, and corrected for multiple comparisons.

RESULTS

Groups were not statistically different in cortical thickness or subcortical volumes. For patients with DS, illness duration was inversely correlated with cortical thickness of left-sided anterior and posterior cortical midline structures (perigenual/dorsal anterior cingulate cortex, superior parietal cortex, precuneus), and clusters at the left temporoparietal junction (supramarginal gyrus, postcentral gyrus, superior temporal gyrus), left postcentral gyrus, and right pericalcarine cortex. Dissociative seizure duration was inversely correlated with cortical thickness in the left perigenual anterior cingulate cortex, superior/middle frontal gyri, precentral gyrus and lateral occipital cortex, along with the right isthmus-cingulate and posterior-cingulate, middle temporal gyrus, and precuneus. Seizure frequency did not show any significant correlations.

CONCLUSIONS

In patients with DS, illness duration inversely correlated with cortical thickness of left-sided default mode network cortical hubs, while seizure duration correlated with left frontopolar and right posteromedial areas, among others. Etiological factors contributing to neuroanatomical variations in areas related to self-referential processing in patients with DS require more research inquiry.

Functional seizures are associated with cerebrovascular disease and functional stroke is more common in patients with functional seizures than epileptic seizures

PURPOSE

To characterize the relationship between functional seizures (FSe), cerebrovascular disease (CVD), and functional stroke.

METHOD

A retrospective case-control study of 189 patients at a single large tertiary medical center. We performed a manual chart review of medical records of patients with FSe or epileptic seizures (ES), who also had ICD code evidence of CVD. The clinical characteristics of FSe, ES, CVD, and functional stroke were recorded. Logistic regression and Welch's t-tests were used to evaluate the differences between the FSe and ES groups.

RESULTS

Cerebrovascular disease was confirmed in 58.7% and 87.6% of patients with FSe or ES through manual chart review. Stroke was significantly more common in patients with ES (76.29%) than FSe (43.48%) (p = 4.07 × 10^-6). However, compared to nonepileptic controls FSe was associated with both CVD (p < 0.0019) and stroke (p < 6.62 × 10^-10). Functional stroke was significantly more common in patients with FSe (39.13%) than patients with ES (4.12%) (p = 4.47 × 10^-9). Compared to patients with ES, patients with FSe were younger (p = 0.00022), more likely to be female (p = 0.00040), and more likely to have comorbid mental health needs including anxiety (p = 1.06 × 10^-6), PTSD or history of trauma (e.g., sexual abuse) (p = 1.06 × 10^-13), and bipolar disorder (p = 0.0011).

CONCLUSION

Our results confirm the initial observation of increased CVD in patients with FSe and further suggest that patients with FSe may be predisposed to developing another functional neurological disorder (FND) (i.e., functional stroke). We speculate that this may be due to shared risk factors and pathophysiological processes that are common to various manifestations of FND.

Machine learning applications to differentiate comorbid functional seizures and epilepsy from pure functional seizures

PURPOSE

We have utilized different methods in machine learning (ML) to develop the best algorithm to differentiate comorbid functional seizures (FS) and epilepsy from those who have pure FS.

METHODS

This was a retrospective study of an electronic database of patients with seizures. All patients with a diagnosis of FS (with or without comorbid epilepsy) were studied at the outpatient epilepsy clinic at Shiraz University of Medical Sciences, Shiraz, Iran, from 2008 until 2021. We arbitrarily selected 14 features that are important in making the diagnosis of patients with seizures and also are easily obtainable during history taking. Pytorch and Scikit-learn packages were used to construct various models including random forest classifier, decision tree classifier, support vector classifier, k-nearest neighbor, and TabNet classifier.

RESULTS

Three hundred and two patients had FS (82.5%), while 64 patients had FS and comorbid epilepsy (17.5%). The "TabNet classifier" could provide the best sensitivity (90%) and specificity (74%) measures (accuracy of 76%) to help differentiate patients with FS from those with FS and comorbid epilepsy.

CONCLUSION

These satisfactory differentiating measures suggest that the current algorithm could be used in clinical practice to help with the difficult task of distinguishing patients with FS from those with FS and comorbid epilepsy. Based on the results of the current study, we have developed an Application (SeiDx). This App is freely accessible at the following address: https://drive.google.com/file/d/1rAgBXKNPW9bmUCDioaGHHzLBQgzZ-HZ2/view. This App should be validated in a prospective assessment.

Orbito-frontal thinning together with a somatoform dissociation might be the fingerprint of PNES

OBJECTIVE

To investigate neuroanatomical changes in patients with psychogenic nonepileptic seizures (PNES) compared to major depressive disorder (MDD) and healthy controls.

METHODS

Forty-two drug-naïve PNES subjects and 25 patients with MDD, matched for demographic characteristics and level of depression (as measured by Beck Depression Inventory-II, BDI-II), were consecutively recruited. Patients performed an extensive neuropsychiatric assessment including: Hamilton Anxiety Rating Scale, Traumatic Experience Checklist, Dissociative Experiences Scale, Toronto Alexithymia Scale and Somatoform Dissociation Questionnaire (SDQ-20). All patients, together with 78 healthy matched controls, underwent 3T brain MRI followed by surface-based morphometry.

RESULTS

Cortical thickness analysis revealed significant cortical thinning in bilateral medial orbitofrontal cortex (OFC) and left rostral anterior cingulate cortex (ACC) in patients with MDD compared to subjects with PNES and controls. Interestingly, increased thickness of the right pars triangularis was found in PNES subjects compared to controls. PNES showed higher scores in SDQ-20 (p < 0.001) compared to MDD, which was corroborated by neuroimaging data, where somatoform dissociation scores correlated with morphological changes in the left medial OFC.

CONCLUSION

Our results show selective cortical thinning over the medial OFC in patients with PNES compared to wider regions of thinning in patients with MDD. Somatoform dissociation was the only psychopathological assessment significantly different in PNES and MDD.

Clinical Application of Machine Learning Models for Brain Imaging in Epilepsy: A Review

Epilepsy is a common neurological disorder characterized by recurrent and disabling seizures. An increasing number of clinical and experimental applications of machine learning (ML) methods for epilepsy and other neurological and psychiatric disorders are available. ML methods have the potential to provide a reliable and optimal performance for clinical diagnoses, prediction, and personalized medicine by using mathematical algorithms and computational approaches. There are now several applications of ML for epilepsy, including neuroimaging analyses. For precise and reliable clinical applications in epilepsy and neuroimaging, the diverse ML methodologies should be examined and validated. We review the clinical applications of ML models for brain imaging in epilepsy obtained from a PubMed database search in February 2021. We first present an overview of typical neuroimaging modalities and ML models used in the epilepsy studies and then focus on the existing applications of ML models for brain imaging in epilepsy based on the following clinical aspects: (i) distinguishing individuals with epilepsy from healthy controls, (ii) lateralization of the temporal lobe epilepsy focus, (iii) the identification of epileptogenic foci, (iv) the prediction of clinical outcomes, and (v) brain-age prediction. We address the practical problems and challenges described in the literature and suggest some future research directions.

Are Functional (Psychogenic Nonepileptic) Seizures the Sole Expression of Psychological Processes?

Functional [psychogenic nonepileptic/dissociative] seizures (FND-seiz) and related functional neurological disorder subtypes were of immense interest to early founders of modern-day neurology and psychiatry. Unfortunately, the divide that occurred between the both specialties throughout the mid-twentieth century placed FND-seiz at the borderland between the two disciplines. In the process, a false Cartesian dualism emerged that labeled psychiatric conditions as impairments of the mind and neurological conditions as disturbances in structural neuroanatomy. Excitingly, modern-day neuropsychiatric perspectives now consider neurologic and psychiatric conditions as disorders of both brain and mind. In this article, we aim to integrate neurologic and psychiatric perspectives in the conceptual framing of FND-seiz. In doing so, we explore emerging relationships between symptoms, neuropsychological constructs, brain networks, and neuroendocrine/autonomic biomarkers of disease. Evidence suggests that the neuropsychological constructs of emotion processing, attention, interoception, and self-agency are important in the pathophysiology of FND-seiz. Furthermore, FND-seiz is a multi-network brain disorder, with evidence supporting roles for disturbances within and across the salience, limbic, attentional, multimodal integration, and sensorimotor networks. Risk factors, including the magnitude of previously experienced adverse life events, relate to individual differences in network architecture and neuroendocrine profiles. The time has come to use an integrated neuropsychiatric approach that embraces the closely intertwined relationship between physical health and mental health to conceptualize FND-seiz and related functional neurological disorder subtypes.

Neuroimaging in Functional Neurological Disorder: State of the Field and Research Agenda

Functional neurological disorder (FND) was of great interest to early clinical neuroscience leaders. During the 20th century, neurology and psychiatry grew apart - leaving FND a borderland condition. Fortunately, a renaissance has occurred in the last two decades, fostered by increased recognition that FND is prevalent and diagnosed using "rule-in" examination signs. The parallel use of scientific tools to bridge brain structure - function relationships has helped refine an integrated biopsychosocial framework through which to conceptualize FND. In particular, a growing number of quality neuroimaging studies using a variety of methodologies have shed light on the emerging pathophysiology of FND. This renewed scientific interest has occurred in parallel with enhanced interdisciplinary collaborations, as illustrated by new care models combining psychological and physical therapies and the creation of a new multidisciplinary FND society supporting knowledge dissemination in the field. Within this context, this article summarizes the output of the first International FND Neuroimaging Workgroup meeting, held virtually, on June 17th, 2020 to appraise the state of neuroimaging research in the field and to catalyze large-scale collaborations. We first briefly summarize neural circuit models of FND, and then detail the research approaches used to date in FND within core content areas: cohort characterization; control group considerations; task-based functional neuroimaging; resting-state networks; structural neuroimaging; biomarkers of symptom severity and risk of illness; and predictors of treatment response and prognosis. Lastly, we outline a neuroimaging-focused research agenda to elucidate the pathophysiology of FND and aid the development of novel biologically and psychologically-informed treatments.

Reduced limbic microstructural integrity in functional neurological disorder

BACKGROUND

Functional neurological disorder (FND) is a condition at the intersection of neurology and psychiatry. Individuals with FND exhibit corticolimbic abnormalities, yet little is known about the role of white matter tracts in the pathophysiology of FND. This study characterized between-group differences in microstructural integrity, and correlated fiber bundle integrity with symptom severity, physical disability, and illness duration.

METHODS

A diffusion tensor imaging (DTI) study was performed in 32 patients with mixed FND compared to 36 healthy controls. Diffusion-weighted magnetic resonance images were collected along with patient-reported symptom severity, physical disability (Short Form Health Survey-36), and illness duration data. Weighted-degree and link-level graph theory and probabilistic tractography analyses characterized fractional anisotropy (FA) values across cortico-subcortical connections. Results were corrected for multiple comparisons.

RESULTS

Compared to controls, FND patients showed reduced FA in the stria terminalis/fornix, medial forebrain bundle, extreme capsule, uncinate fasciculus, cingulum bundle, corpus callosum, and striatal-postcentral gyrus projections. Except for the stria terminalis/fornix, these differences remained significant adjusting for depression and anxiety. In within-group analyses, physical disability inversely correlated with stria terminalis/fornix and medial forebrain bundle FA values; illness duration negatively correlated with stria terminalis/fornix white matter integrity. A FND symptom severity composite score did not correlate with FA in patients.

CONCLUSIONS

In this first DTI study of mixed FND, microstructural differences were observed in limbic and associative tracts implicated in salience, defensive behaviors, and emotion regulation. These findings advance our understanding of neurocircuit pathways in the pathophysiology of FND.

Sleep Disturbances in Patients with Nonepileptic Seizures

OBJECTIVE

Up to 20% of patients treated for epileptic seizures experience psychogenic nonepileptic paroxysms (PNES). These patients present a significant burden for the health care systems because of poor treatment outcomes. The presented review aims to summarize the current state of knowledge on sleep disturbances in patients with nonepileptic seizures.

METHODS

Articles were acquired via PubMed and Web of Science, and papers between January 1990 and March 2020 were extracted. Inclusion criteria were (1) published in a peer-reviewed journal: (2) studies in humans only; or (3) reviews on a related topic; (4) English language. The exclusion criteria were: (1) abstracts from conferences; (2) commentaries; (3) subjects younger than 18 years. From primary assessment, 122 articles were extracted; after obtaining full texts and secondary articles from reference lists, 45 papers were used in this review.

RESULTS

Limited data are available regarding sleep disorders in PNES patients, over the last 30 years only nine original research papers addressed sleep problems in patients with PNES with only six studies assessing objectively measured changes in sleep. Current literature supports the subjective perception of the sleep disturbances with mixed results in objective pathophysiological findings. Conflicting results regarding the REM phase can be found, and studies reported both shortening and prolonging of the REM phase with methodological limitations. Poor sleep quality and shortened duration have been consistently described in most of the studies.

CONCLUSION

Further research on a broader spectrum of patients with PNES is needed, primarily focusing on objective neurophysiological findings. Quality of life in patients suffering from PNES can be increased by good sleep habits and treatment of comorbid sleep disorders.

Structural neuroimaging of somatoform disorders: A systematic review

Although there has been an increment in neuroimaging research in somatoform disorders (SD), to date little is known about the neural correlates of these diseases. Therefore, in this systematic, review we aimed at summarizing the existing evidence of structural brain alterations in SD as per DSM-IV and DSM-5 criteria. Three electronic databases (Scopus, PubMed and Web of Science) were searched. Only case-control studies using structural neuroimaging were included. Forty-five out of 369 articles fulfilled inclusion criteria and were reviewed. Compared to controls, subjects with SD showed morphological alterations encompassing motor, limbic and somatosensory circuits. Although far from being conclusive, the results suggested that SD are characterized by selective alterations of large-scale brain networks implicated in cognitive control, emotion regulation and processing, stress and somatic-visceral perception. This review highlights the need for further multimodal neuroimaging studies with longitudinal designs, in larger and better-characterized samples, to elucidate the temporal and causal relationship between neuroanatomical changes and SD, which is paramount for informing tailored treatments.

Psychogenic Non-epileptic Seizures and Pseudo-Refractory Epilepsy, a Management Challenge

Psychogenic nonepileptic seizures (PNES) are neurobehavioral conditions positioned in a gray zone, not infrequently a no-man land, that lies in the intersection between Neurology and Psychiatry. According to the DSM 5, PNES are a subgroup of conversion disorders (CD), while the ICD 10 classifies PNES as dissociative disorders. The incidence of PNES is estimated to be in the range of 1.4-4.9/100,000/year, and the prevalence range is between 2 and 33 per 100,000. The International League Against Epilepsy (ILAE) has identified PNES as one of the 10 most critical neuropsychiatric conditions associated with epilepsy. Comorbidity between epilepsy and PNES, a condition leading to "dual diagnosis," is a serious diagnostic and therapeutic challenge for clinicians. The lack of prompt identification of PNES in epileptic patients can lead to potentially harmful increases in the dosage of anti-seizure drugs (ASD) as well as erroneous diagnoses of refractory epilepsy. Hence, pseudo-refractory epilepsy is the other critical side of the PNES coin as one out of four to five patients admitted to video-EEG monitoring units with a diagnosis of pharmaco-resistant epilepsy is later found to suffer from non-epileptic events. The majority of these events are of psychogenic origin. Thus, the diagnostic differentiation between pseudo and true refractory epilepsy is essential to prevent actions that lead to unnecessary treatments and ASD-related side effects as well as produce a negative impact on the patient's quality of life. In this article, we review and discuss recent evidence related to the neurobiology of PNES. We also provide an overview of the classifications and diagnostic steps that are employed in PNES management and dwell on the concept of pseudo-resistant epilepsy.

Artificial intelligence as an emerging technology in the current care of neurological disorders

BACKGROUND

Artificial intelligence (AI) has influenced all aspects of human life and neurology is no exception to this growing trend. The aim of this paper is to guide medical practitioners on the relevant aspects of artificial intelligence, i.e., machine learning, and deep learning, to review the development of technological advancement equipped with AI, and to elucidate how machine learning can revolutionize the management of neurological diseases. This review focuses on unsupervised aspects of machine learning, and how these aspects could be applied to precision neurology to improve patient outcomes. We have mentioned various forms of available AI, prior research, outcomes, benefits and limitations of AI, effective accessibility and future of AI, keeping the current burden of neurological disorders in mind.

DISCUSSION

The smart device system to monitor tremors and to recognize its phenotypes for better outcomes of deep brain stimulation, applications evaluating fine motor functions, AI integrated electroencephalogram learning to diagnose epilepsy and psychological non-epileptic seizure, predict outcome of seizure surgeries, recognize patterns of autonomic instability to prevent sudden unexpected death in epilepsy (SUDEP), identify the pattern of complex algorithm in neuroimaging classifying cognitive impairment, differentiating and classifying concussion phenotypes, smartwatches monitoring atrial fibrillation to prevent strokes, and prediction of prognosis in dementia are unique examples of experimental utilizations of AI in the field of neurology. Though there are obvious limitations of AI, the general consensus among several nationwide studies is that this new technology has the ability to improve the prognosis of neurological disorders and as a result should become a staple in the medical community.

CONCLUSION

AI not only helps to analyze medical data in disease prevention, diagnosis, patient monitoring, and development of new protocols, but can also assist clinicians in dealing with voluminous data in a more accurate and efficient manner.

Structural alterations in functional neurological disorder and related conditions: a software and hardware problem?

Functional neurological (conversion) disorder (FND) is a condition at the interface of neurology and psychiatry. A "software" vs. "hardware" analogy describes abnormal neurobiological mechanisms occurring in the context of intact macroscopic brain structure. While useful for explanatory and treatment models, this framework may require more nuanced considerations in the context of quantitative structural neuroimaging findings in FND. Moreover, high co-occurrence of FND and somatic symptom disorders (SSD) as defined in DSM-IV (somatization disorder, somatoform pain disorder, and undifferentiated somatoform disorder; referred to as SSD for brevity in this article) raises the possibility of a partially overlapping pathophysiology. In this systematic review, we use a transdiagnostic approach to review and appraise the structural neuroimaging literature in FND and SSD. While larger sample size studies are needed for definitive characterization, this article highlights that individuals with FND and SSD may exhibit sensorimotor, prefrontal, striatal-thalamic, paralimbic, and limbic structural alterations. The structural neuroimaging literature is contextualized within the neurobiology of stress-related neuroplasticity, gender differences, psychiatric comorbidities, and the greater spectrum of functional somatic disorders. Future directions that could accelerate the characterization of the pathophysiology of FND and DSM-5 SSD are outlined, including "disease staging" discussions to contextualize subgroups with or without structural changes. Emerging neuroimaging evidence suggests that some individuals with FND and SSD may have a "software" and "hardware" problem, although if structural alterations are present the neural mechanisms of functional disorders remain distinct from lesional neurological conditions. Furthermore, it remains unclear whether structural alterations relate to predisposing vulnerabilities or consequences of the disorder.

Widely Impaired White Matter Integrity and Altered Structural Brain Networks in Psychogenic Non-Epileptic Seizures

OBJECTIVE

The underlying neural correlates of psychogenic non-epileptic seizures (PNES) are still unknown and their identification would be helpful for clinicians and patients. This study aimed to reveal details of white matter microstructure and alterations in brain structural networks in patients with PNES by using diffusion tensor imaging (DTI) and graph theoretical connectivity analysis.

METHODS

Seventeen patients with PNES and 26 age- and sex-matched healthy controls were enrolled. All participants underwent DTI on a 3.0-T MRI scanner, and fractional anisotropy (FA) and mean diffusivity (MD) maps were compared by tract-based spatial statistics. Additionally, the structural networks derived from DTI data were analyzed using graph theory and two different parcellation schemes.

RESULTS

Patients with PNES showed widespread decreases in FA and increases in MD, particularly in the deep white matter. In addition, graph theoretical analysis revealed impaired brain networks in PNES, including increased path length, decreased network efficiency, altered nodal topology, and reduced regional connectivity in the right posterior areas.

CONCLUSION

We found widely impaired white matter integrity and impaired brain structural networks in Japanese patients with PNES. These findings contribute to the accumulation of evidence on PNES and may improve understanding of this condition.