Relationship between personal recovery, autobiographical memory, and clinical recovery in people with mental illness in the acute phase.

Aim: Narratives are important in psychiatric rehabilitation. People with a psychiatric diagnosis find it difficult to recall specific autobiographical memories of events that lasted less than a day. Although personal narratives play a central role in personal recovery, the factors influencing personal and clinical recovery, such as psychiatric symptoms and cognitive function, have not been fully explored. Therefore, this study examined the associations between personal recovery and autobiographical memory, age, psychiatric symptoms, and neurocognitive function. Method: The Self-Identified Stage of Recovery, Parts A and B (SISR-A, SISR-B), Autobiographical Memory Test (AMT), Japanese version of the Brief Assessment Scale of Schizophrenia Cognitive Function, and Brief Psychiatric Symptom Rating Scale were administered to 40 individuals with psychiatric disorders who were undergoing psychiatric rehabilitation. Results: A significant positive correlation was found between the total number of specific memories in the AMT and total SISR-B scores. A binary logistic regression analysis revealed that the total number of specific memories, especially high responsiveness to negative cue words, significantly predicted greater personal recovery. Age, psychiatric symptoms, and neurocognitive function did not significantly predict higher personal recovery. Conclusion: In psychiatric rehabilitation, negative episodes should be treated with caution; however, they may also facilitate personal recovery.

Understanding the Pathophysiology of Mental Diseases and Early Diagnosis Thanks to Electrophysiological Tools: Some Insights and Empirical Facts.

Objective. Neurophysiological tools remain indispensable instruments in the assessment of psychiatric disorders. These techniques are widely available, inexpensive and well tolerated, providing access to the assessment of brain functional alterations. In the clinical psychiatric context, electrophysiological techniques are required to provide important information on brain function. While there is an immediate benefit in the clinical application of these techniques in the daily routine (emergency assessments, exclusion of organic brain alterations), these tools are also useful in monitoring the progress of psychiatric disorders or the effects of therapy. There is increasing evidence and convincing literature to confirm that electroencephalography and related techniques can contribute to the diagnostic workup, to the identification of subgroups of disease categories, to the assessment of long-term causes and to facilitate response predictions. Methods and Results. In this report we focus on 3 different novel developments of the use of neurophysiological techniques in 3 highly prevalent psychiatric disorders: (1) the value of EEG recordings and machine learning analyses (deep learning) in order to improve the diagnosis of dementia subtypes; (2) the use of mismatch negativity in the early diagnosis of schizophrenia; and (3) the monitoring of addiction and the prevention of relapse using cognitive event-related potentials. Empirical evidence was presented. Conclusion. Such information emphasized the important role of neurophysiological tools in the identification of useful biological markers leading to a more efficient care management. The potential of the implementation of machine learning approaches together with the conduction of large cross-sectional and longitudinal studies was also discussed.

A deep learning model for the detection of various dementia and MCI pathologies based on resting-state electroencephalography data: A retrospective multicentre study.

Dementia and mild cognitive impairment (MCI) represent significant health challenges in an aging population. As the search for noninvasive, precise and accessible diagnostic methods continues, the efficacy of electroencephalography (EEG) combined with deep convolutional neural networks (DCNNs) in varied clinical settings remains unverified, particularly for pathologies underlying MCI such as Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and idiopathic normal-pressure hydrocephalus (iNPH). Addressing this gap, our study evaluates the generalizability of a DCNN trained on EEG data from a single hospital (Hospital #1). For data from Hospital #1, the DCNN achieved a balanced accuracy (bACC) of 0.927 in classifying individuals as healthy (n = 69) or as having AD, DLB, or iNPH (n = 188). The model demonstrated robustness across institutions, maintaining bACCs of 0.805 for data from Hospital #2 (n = 73) and 0.920 at Hospital #3 (n = 139). Additionally, the model could differentiate AD, DLB, and iNPH cases with bACCs of 0.572 for data from Hospital #1 (n = 188), 0.619 for Hospital #2 (n = 70), and 0.508 for Hospital #3 (n = 139). Notably, it also identified MCI pathologies with a bACC of 0.715 for Hospital #1 (n = 83), despite being trained on overt dementia cases instead of MCI cases. These outcomes confirm the DCNN's adaptability and scalability, representing a significant stride toward its clinical application. Additionally, our findings suggest a potential for identifying shared EEG signatures between MCI and dementia, contributing to the field's understanding of their common pathophysiological mechanisms.

Predicting postoperative delirium after cardiovascular surgeries from preoperative portable electroencephalography oscillations.

Introduction: Postoperative delirium (POD) is common and life-threatening, however, with intensive interventions, a potentially preventable clinical syndrome. Although electroencephalography (EEG) is a promising biomarker of delirium, standard 20-leads EEG holds difficulties for screening usage in clinical practice. Objective: We aimed to develop an accurate algorithm to predict POD using EEG data obtained from portable device. Methods: We recruited 128 patients who underwent scheduled cardiovascular surgery. Cognitive function assessments were conducted, and portable EEG recordings were obtained prior to surgery. Results: Among the patients, 47 (36.7%) patients with POD were identified and they did not significantly differ from patients without POD in sex ratio, age, cognitive function, or treatment duration of intensive care unit. However, significant differences were observed in the preoperative EEG power spectrum densities at various frequencies, especially gamma activity, between patients with and without POD. POD was successfully predicted using preoperative EEG data with a machine learning algorithm, yielding accuracy of 86% and area under the receiver operating characteristic curve of 0.93. Discussion: This study provides new insights into the objective and biological vulnerability to delirium. The developed algorithm can be applied in general hospitals without advanced equipment and expertise, thereby enabling the reduction of POD occurrences with intensive interventions for high-risk patients.

Social Buffering Effects during Craft Activities in Parallel Group Session Revealed by EEG Analysis and Parasympathetic Activity.

INTRODUCTION: The therapeutic structure of occupational therapy (OT) includes groups. Although the presence of others is expected to be relaxing due to the social buffering effect and the tend and befriend theory, it has not been sufficiently validated in accordance with the therapeutic structure of OT. The aim of this study was to investigate the electrophysiological evidence for the effectiveness of parallel groups and states of concentration on craft activities used in OT. METHODS: Thirty healthy young adults were used as controls to measure EEG and autonomic activity during craft activities in three conditions: alone, parallel, and nonparallel. EEG was analyzed using exact low-resolution electromagnetic tomography, and autonomic activity was analyzed using Lorenz plot analysis. RESULTS: Parasympathetic activity was significantly higher in the parallel condition than in the alone condition. A significant negative correlation was found between current source density and parasympathetic activity in the region centered on the right insular cortex in the α1 band, and functional connectivity in regions including the anterior cingulate cortex and insular cortex was associated with autonomic activity. CONCLUSION: Craft activities that occurred during frontal midline theta rhythm also increased parasympathetic activity. The results suggest that the parallel groups used in OT and the intensive state of craft activities induce a social buffering effect that increases parasympathetic activity despite the absence of physical contact or social support. This provides evidence for the effectiveness of the therapeutic structure of occupational activities and groups in OT.

The Neurophysiological Features Associated with Unilateral Spatial Neglect Recovery: A Scoping Review.

The purpose of this scoping review is to provide updated information on the neural basis and neurophysiological features associated with unilateral spatial neglect (USN) recovery. We applied the Preferred Reporting Systems for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) framework and identified 16 relevant papers from the databases. Critical appraisal was performed by two independent reviewers using a standardized appraisal instrument developed by the PRISMA-ScR. We identified and categorized investigation methods for the neural basis and neurophysiological features of USN recovery after stroke using magnetic resonance imaging (MRI), functional MRI, and electroencephalography (EEG). This review found two brain-level mechanisms underlying USN recovery at the behavioral level. These include the absence of stroke-related damage to the right ventral attention network during the acute phase and compensatory recruitment of analogous areas of the undamaged opposite hemisphere and prefrontal cortex during visual search tasks in the subacute or later phases. However, the relationship between the neural and neurophysiological findings and improvements in USN-related activities of daily living remains unknown. This review adds to the growing body of evidence regarding the neural mechanisms underlying USN recovery.

Motor-Related Mu/Beta Rhythm in Older Adults: A Comprehensive Review.

Mu rhythm, also known as the mu wave, occurs on sensorimotor cortex activity at rest, and the frequency range is defined as 8-13Hz, the same frequency as the alpha band. Mu rhythm is a cortical oscillation that can be recorded from the scalp over the primary sensorimotor cortex by electroencephalogram (EEG) and magnetoencephalography (MEG). The subjects of previous mu/beta rhythm studies ranged widely from infants to young and older adults. Furthermore, these subjects were not only healthy people but also patients with various neurological and psychiatric diseases. However, very few studies have referred to the effect of mu/beta rhythm with aging, and there was no literature review about this theme. It is important to review the details of the characteristics of mu/beta rhythm activity in older adults compared with young adults, focusing on age-related mu rhythm changes. By comprehensive review, we found that, compared with young adults, older adults showed mu/beta activity change in four characteristics during voluntary movement, increased event-related desynchronization (ERD), earlier beginning and later end, symmetric pattern of ERD and increased recruitment of cortical areas, and substantially reduced beta event-related desynchronization (ERS). It was also found that mu/beta rhythm patterns of action observation were changing with aging. Future work is needed in order to investigate not only the localization but also the network of mu/beta rhythm in older adults.

EEG resting-state networks in Alzheimer's disease associated with clinical symptoms.

Alzheimer's disease (AD) is a progressive neuropsychiatric disease affecting many elderly people and is characterized by progressive cognitive impairment of memory, visuospatial, and executive functions. As the elderly population is growing, the number of AD patients is increasing considerably. There is currently growing interest in determining AD's cognitive dysfunction markers. We used exact low-resolution-brain-electromagnetic-tomography independent-component-analysis (eLORETA-ICA) to assess activities of five electroencephalography resting-state-networks (EEG-RSNs) in 90 drug-free AD patients and 11 drug-free patients with mild-cognitive-impairment due to AD (ADMCI). Compared to 147 healthy subjects, the AD/ADMCI patients showed significantly decreased activities in the memory network and occipital alpha activity, where the age difference between the AD/ADMCI and healthy groups was corrected by linear regression analysis. Furthermore, the age-corrected EEG-RSN activities showed correlations with cognitive function test scores in AD/ADMCI. In particular, decreased memory network activity showed correlations with worse total cognitive scores for both Mini-Mental-State-Examination (MMSE) and Alzheimer's Disease-Assessment-Scale-cognitive-component-Japanese version (ADAS-J cog) including worse sub-scores for orientation, registration, repetition, word recognition and ideational praxis. Our results indicate that AD affects specific EEG-RSNs and deteriorated network activity causes symptoms. Overall, eLORETA-ICA is a useful, non-invasive tool for assessing EEG-functional-network activities and provides better understanding of the neurophysiological mechanisms underlying the disease.

Frontal midline theta rhythm and gamma activity measured by sheet-type wearable EEG device.

Introduction: The current study measured the frontal midline theta rhythm (Fmθ), which appears in the frontal midline region during the attentional focus state, using the sheet-type wearable electroencephalograph (EEG) device HARU-1, and examined the modulation of frontal gamma band activity by cognitive tasks. Methods: We measured the frontal EEG of 20 healthy subjects using HARU-1 for 2 min during the rest eyes-closed condition and simple mental calculation task condition, respectively. Statistical analyses were conducted using permutation testing based on t-test and cluster analysis to compare the results between the resting state and the task condition. Results: Twelve of 20 subjects showed Fmθ during the task condition. The 12 subjects with Fmθ showed significantly higher activity of the theta and gamma bands, and significantly low activity of the alpha band during the task condition compared to the resting condition. In the eight subjects without Fmθ were significantly low activity of the alpha and beta bands and no significant activity in the theta and gamma band activity during the task condition compared to the resting condition. Discussion: These results indicate that it is possible to measure Fmθ using HARU-1. A novel finding was the gamma band activity appearing with Fmθ in the left and right frontal forehead regions, suggesting that it reflects the function of the prefrontal cortex in working memory tasks.

Precise Discrimination for Multiple Etiologies of Dementia Cases Based on Deep Learning with Electroencephalography.

INTRODUCTION: It is critical to develop accurate and universally available biomarkers for dementia diseases to appropriately deal with the dementia problems under world-wide rapid increasing of patients with dementia. In this sense, electroencephalography (EEG) has been utilized as a promising examination to screen and assist in diagnosing dementia, with advantages of sensitiveness to neural functions, inexpensiveness, and high availability. Moreover, the algorithm-based deep learning can expand EEG applicability, yielding accurate and automatic classification easily applied even in general hospitals without any research specialist. METHODS: We utilized a novel deep neural network, with which high accuracy of discrimination was archived in neurological disorders in the previous study. Based on this network, we analyzed EEG data of healthy volunteers (HVs, N = 55), patients with Alzheimer's disease (AD, N = 101), dementia with Lewy bodies (DLB, N = 75), and idiopathic normal pressure hydrocephalus (iNPH, N = 60) to evaluate the discriminative accuracy of these diseases. RESULTS: High discriminative accuracies were archived between HV and patients with dementia, yielding 81.7% (vs. AD), 93.9% (vs. DLB), 93.1% (vs. iNPH), and 87.7% (vs. AD, DLB, and iNPH). CONCLUSION: This study revealed that the EEG data of patients with dementia were successfully discriminated from HVs based on a novel deep learning algorithm, which could be useful for automatic screening and assisting diagnosis of dementia diseases.

Psychoses after an antiepileptic drug administration: Frequency, timing, and duration.

OBJECTIVES: To clarify the pathophysiology of psychoses after the new administration of antiepileptic drugs (AED), we analyzed the annual incidence, timing of development, and duration of episodes. METHODS: Psychotic outcomes in the first 6-month period after an AED or non-AED administration in patients with focal epilepsy were exhaustively reviewed in eight Japanese neuropsychiatry institutions. In cases with psychotic episodes, the subtype of psychosis, timing of development, previous history of psychosis, and duration of the episode were evaluated. RESULTS: Between 1981 and 2015, 5018 new drugs (4402 AED and 616 non-AED) were administered to 2067 patients with focal epilepsy. In the first 6-month period, 105 psychotic episodes occurred (81 interictal psychosis [IIP] and 24 postictal psychosis). Furthermore, 55 cases were first episodes and 50 were recurrent episodes. The frequency of psychoses is significantly higher after AED administration (n = 102) compared with non-AED administration (n = 3). Psychosis occurred most frequently in the initial 1-month period after new-AED administration and tended to decrease with increasing time. The estimated annual incidence of all psychoses after a new AED administration was 3.5% (2.0% for first-episode psychosis and 1.8% for first-episode IIP). Duration of psychoses (mean, 38.5 weeks) was equivalent to overall IIP. Duration of IIP did not shorten with discontinuation of newly administered AED. SIGNIFICANCE: Patients with epilepsy exhibit psychosis more frequently after new AED administration than after non-AED administration. This study shows the pathophysiology of psychoses after AED administration with annual incidence, the timing of development, and the duration of PAP, which have rarely been reported.

Normalized Power Variance: A new Field Orthogonal to Power in EEG Analysis.

To date, electroencephalogram (EEG) has been used in the diagnosis of epilepsy, dementia, and disturbance of consciousness via the inspection of EEG waves and identification of abnormal electrical discharges and slowing of basic waves. In addition, EEG power analysis combined with a source estimation method like exact-low-resolution-brain-electromagnetic-tomography (eLORETA), which calculates the power of cortical electrical activity from EEG data, has been widely used to investigate cortical electrical activity in neuropsychiatric diseases. However, the recently developed field of mathematics "information geometry" indicates that EEG has another dimension orthogonal to power dimension - that of normalized power variance (NPV). In addition, by introducing the idea of information geometry, a significantly faster convergent estimator of NPV was obtained. Research into this NPV coordinate has been limited thus far. In this study, we applied this NPV analysis of eLORETA to idiopathic normal pressure hydrocephalus (iNPH) patients prior to a cerebrospinal fluid (CSF) shunt operation, where traditional power analysis could not detect any difference associated with CSF shunt operation outcome. Our NPV analysis of eLORETA detected significantly higher NPV values at the high convexity area in the beta frequency band between 17 shunt responders and 19 non-responders. Considering our present and past research findings about NPV, we also discuss the advantage of this application of NPV representing a sensitive early warning signal of cortical impairment. Overall, our findings demonstrated that EEG has another dimension - that of NPV, which contains a lot of information about cortical electrical activity that can be useful in clinical practice.

Disentangling cognitive inflexibility in major depressive disorder: A transcranial direct current stimulation study.

BACKGROUND: Cognitive dysfunction is a persistent residual symptom in major depressive disorders (MDDs) that hinders social and occupational recovery. Cognitive inflexibility is a typical cognitive dysfunction in MDD and refers to difficulty in switching tasks, which requires two subcomponents: forgetting an old task and adapting to a new one. Here, we aimed to disentangle the subcomponents of cognitive inflexibility in MDD and investigate whether they can be improved by transcranial direct current stimulation (tDCS) on the prefrontal cortex. METHODS: The current study included 20 patients with MDD (seven females) and 22 age-matched healthy controls (HCs) (seven females). The participants received anodal tDCS on either the dorsomedial prefrontal cortex (DMPFC) or dorsolateral prefrontal cortex (DLPFC) in a crossover design. Before and after the application of tDCS, the participants performed a modified Wisconsin Card Sorting Test, in which the task-switching rules were explicitly described and proactive interference from a previous task rule was occasionally released. RESULTS: We found that the behavioral cost of a task switch was increased in patients with MDD, but that of proactive interference was comparable between patients with MDD and HCs. The response time for anodal DMPFC tDCS was decreased compared with that for anodal tDCS on the DLPFC in MDD. CONCLUSIONS: These findings suggest that cognitive inflexibility in MDD is primarily explained by the difficulty to adapt to a new task and environment, and that tDCS on the DMPFC improves behavioral performance during cognitively demanding tasks that require conflict resolution.

Parietal Gamma Band Oscillation Induced by Self-Hand Recognition.

Physiological studies have shown that self-body images receive unique recognition processing in a wide range of brain areas, from the frontal lobe to the parietal-occipital cortex. Event-related potential (ERP) studies have shown that the self-referential effect on the image of a hand increases P300 components, but such studies do not evaluate brain oscillatory activity. In this study, we aimed to discover the self-specific brain electrophysiological activity in relation to hand images. ERPs on the fronto-parietal midline were elicited by a three-stimulus visual oddball task using hand images: the self-hand, another hand (most similar to the self-hand), and another hand (similar to the self-hand). We analyzed ERP waveform and brain oscillatory activity by simple averaging and time-frequency analysis. The simple averaging analysis found no significant differences between the responses for the three stimulus tasks in all time windows. However, time-frequency analysis showed that self-hand stimuli elicited high gamma ERS in 650-900 ms at the Cz electrode compared to other hand stimuli. Our results show that brain activity specific to the self-referential process to the self-hand image was reflected in the long latency gamma band activity in the mid-central region. This high gamma-band activity at the Cz electrode may be similar to the activity of the mirror neuron system, which is involved in hand motion.