How Useful Is Electroencephalography in the Diagnosis of Autism Spectrum Disorders and the Delineation of Subtypes: A Systematic Review

Autism spectrum disorders (ASD) are thought to be associated with abnormal neural connectivity. Presently, neural connectivity is a theoretical construct that cannot be easily measured. Research in network science and time series analysis suggests that neural network structure, a marker of neural activity, can be measured with electroencephalography (EEG). EEG can be quantified by different methods of analysis to potentially detect brain abnormalities. The aim of this review is to examine evidence for the utility of three methods of EEG signal analysis in the ASD diagnosis and subtype delineation. We conducted a review of literature in which 40 studies were identified and classified according to the principal method of EEG analysis in three categories: functional connectivity analysis, spectral power analysis, and information dynamics. All studies identified significant differences between ASD patients and non-ASD subjects. However, due to high heterogeneity in the results, generalizations could not be inferred and none of the methods alone are currently useful as a new diagnostic tool. The lack of studies prevented the analysis of these methods as tools for ASD subtypes delineation. These results confirm EEG abnormalities in ASD, but as yet not sufficient to help in the diagnosis. Future research with larger samples and more robust study designs could allow for higher sensitivity and consistency in characterizing ASD, paving the way for developing new means of diagnosis

Network-wide abnormalities explain memory variability in hippocampal amnesia

Patients with hippocampal amnesia play a central role in memory neuroscience but the neural underpinnings of amnesia are hotly debated. We hypothesized that focal hippocampal damage is associated with changes across the extended hippocampal system and that these, rather than hippocampal atrophy per se, would explain variability in memory between patients. We assessed this hypothesis in a uniquely large cohort of patients (n = 38) after autoimmune limbic encephalitis, a syndrome associated with focal structural hippocampal pathology. These patients showed impaired recall, recognition and maintenance of new information, and remote autobiographical amnesia. Besides hippocampal atrophy, we observed correlatively reduced thalamic and entorhinal cortical volume, resting-state inter-hippocampal connectivity and activity in posteromedial cortex. Associations of hippocampal volume with recall, recognition, and remote memory were fully mediated by wider network abnormalities, and were only direct in forgetting. Network abnormalities may explain the variability across studies of amnesia and speak to debates in memory neuroscience

How Useful Is Electroencephalography in the Diagnosis of Autism Spectrum Disorders and the Delineation of Subtypes: A Systematic Review

Autism spectrum disorders (ASD) are thought to be associated with abnormal neural connectivity. Presently, neural connectivity is a theoretical construct that cannot be easily measured. Research in network science and time series analysis suggests that neural network structure, a marker of neural activity, can be measured with electroencephalography (EEG). EEG can be quantified by different methods of analysis to potentially detect brain abnormalities. The aim of this review is to examine evidence for the utility of three methods of EEG signal analysis in the ASD diagnosis and subtype delineation. We conducted a review of literature in which 40 studies were identified and classified according to the principal method of EEG analysis in three categories: functional connectivity analysis, spectral power analysis, and information dynamics. All studies identified significant differences between ASD patients and non-ASD subjects. However, due to high heterogeneity in the results, generalizations could not be inferred and none of the methods alone are currently useful as a new diagnostic tool. The lack of studies prevented the analysis of these methods as tools for ASD subtypes delineation. These results confirm EEG abnormalities in ASD, but as yet not sufficient to help in the diagnosis. Future research with larger samples and more robust study designs could allow for higher sensitivity and consistency in characterizing ASD, paving the way for developing new means of diagnosis

Pathologic tearfulness after limbic encephalitis: A novel disorder and its neural basis

Objective We investigated the nature and neural foundations of pathologic tearfulness in a uniquely large cohort of patients who had presented with autoimmune limbic encephalitis (aLE). Methods We recruited 38 patients (26 men, 12 women; median age 63.06 years; interquartile range [IQR] 16.06 years) in the postacute phase of aLE who completed questionnaires probing emotion regulation. All patients underwent structural/functional MRI postacutely, along with 67 age- and sex-matched healthy controls (40 men, 27 women; median age 64.70 years; IQR 19.87 years). We investigated correlations of questionnaire scores with demographic, clinical, neuropsychological, and brain imaging data across patients. We also compared patients diagnosed with pathologic tearfulness and those without, along with healthy controls, on gray matter volume, resting-state functional connectivity, and activity. Results Pathologic tearfulness was reported by 50% of the patients, while no patient reported pathologic laughing. It was not associated with depression, impulsiveness, memory impairment, executive dysfunction in the postacute phase, or amygdalar abnormalities in the acute phase. It correlated with changes in specific emotional brain networks: volume reduction in the right anterior hippocampus, left fusiform gyrus, and cerebellum, abnormal hippocampal resting-state functional connectivity with the posteromedial cortex and right middle frontal gyrus, and abnormal hemodynamic activity in the left fusiform gyrus, right inferior parietal lobule, and ventral pons. Conclusions Pathologic tearfulness is common following aLE, is not a manifestation of other neuropsychiatric features, and reflects abnormalities in networks of emotion regulation beyond the acute hippocampal focus. The condition, which may also be present in other neurologic disorders, provides novel insights into the neural basis of affective control and its dysfunction in disease

EEG complexity as an ASD biomarker: a data-driven study for the identification of electrophysiological correlates of ASD

Autism Spectrum Disorders (ASD) are a group of lifelong neurodevelopmental disorders, described by three core deficits which are thought to be consequences of atypical cortical activity. Abnormal neural connectivity underlying ASD could be inferred from dynamical system characteristics of the brain measured from electroencephalography (EEG) time series. Simple EEG measurement have the potential to provide important clinical biomarkers for early identification, risk assessment and monitoring the progression of ASD, while spanning the spectrumâs heterogeneity of severity. The explicit goal of this project was to use Multiscale Entropy (sample entropy) measures and Recurrence Quantitative Analysis (RQA) on EEG to identify quantifiable neural correlates of behaviours associated with an ASD diagnosis. The hypotheses were tested on two cohorts: the Kenyan cohort with data collected in Kilifi, Kenya from both neurotypical children as well as children diagnosed with ASD and the NDAR cohort from the National Database for Autism Research (NDAR), also containing data from both groups. The results showed that complexity measured using sample entropy is useful in distinguishing the two groups, both at a whole brain level in the alpha, beta and gamma bands, and at a single electrode level, mainly in the theta band in the frontal, occipital and parietal electrodes and in the high gamma band in the frontal and prefrontal areas. RQA variables analysis showed that determinism can delineate ASD in the theta, gamma and high gamma bands in all electrode locations, in the delta band in the left frontal and temporal areas and in the alpha band in the left hemisphere in the occipital, parietal, temporal and central electrodes; laminarity is useful in delineating ASD in the frontal, temporal and parietal regions in the theta band and the occipital, parietal and temporal regions in the delta band; l_entropy played a role in ASD delineation in the central, temporal and occipital areas in the theta frequency; lastly, l_max accurately distinguished the two groups, mainly in the frontal and central areas in the theta band and in the parietal region in the alpha frequency. These findings represent pilot evidence of potential high utility of this method, which can have great impact on clinical practice, in the early screening and diagnosis stages of ASD.</p

How Useful Is Electroencephalography in the Diagnosis of Autism Spectrum Disorders and the Delineation of Subtypes: A Systematic Review

Autism spectrum disorders (ASD) are thought to be associated with abnormal neural connectivity. Presently, neural connectivity is a theoretical construct that cannot be easily measured. Research in network science and time series analysis suggests that neural network structure, a marker of neural activity, can be measured with electroencephalography (EEG). EEG can be quantified by different methods of analysis to potentially detect brain abnormalities. The aim of this review is to examine evidence for the utility of three methods of EEG signal analysis in the ASD diagnosis and subtype delineation. We conducted a review of literature in which 40 studies were identified and classified according to the principal method of EEG analysis in three categories: functional connectivity analysis, spectral power analysis, and information dynamics. All studies identified significant differences between ASD patients and non-ASD subjects. However, due to high heterogeneity in the results, generalizations could not be inferred and none of the methods alone are currently useful as a new diagnostic tool. The lack of studies prevented the analysis of these methods as tools for ASD subtypes delineation. These results confirm EEG abnormalities in ASD, but as yet not sufficient to help in the diagnosis. Future research with larger samples and more robust study designs could allow for higher sensitivity and consistency in characterizing ASD, paving the way for developing new means of diagnosis

The Effect of the New Imidazole Derivatives Complexation with Betacyclodextrin, on the Antifungal Activity in Oropharyngeal Infections

Ketoconazole (KZ) is a broad-spectrum drug used to treat fungal infections. Local use of ketoconazole has been associated with some side effects in healthy adults, especially local reactions, such as stinging, severe irritation, and itching. Moreover, the bioavailability of KZ after oral administration is low in tablets due to its low water solubility. In addition, oral administration of ketoconazole produces systemic exposure, associated with significant side effects, such as cholestatic and hepatocellular lesions. In an attempt to reduce hepatotoxicity, ketoconazole may be administered at the primary site of infection with cutaneous candidiasis, specifically on the skin tissue. However, the use of ketoconazole in topical dosage forms is limited by its high lipophilicity and extremely poor aqueous solubility (1 ng/mL), thus leading to the rare availability of topical dosage forms on the market. Therefore, a new approach to the effective delivery of ketoconazole to the site of infection is targeted, including obtaining new derivatives (keeping the imidazolic nucleus), with a similar spectrum of action, and finally, their inclusion in betacyclodextrin complexes in order to optimize bioavailability and physico-chemical stability