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.

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.