Retention Rates and Successful Treatment with Antiseizure Medications in Newly-Diagnosed Epilepsy Patients.

PURPOSE: Treatment for epilepsy primarily involves antiseizure medications (ASMs), which can be characterized using the clinical data warehouse (CDW) database. In this study, we compared retention rates and time to successful treatment for various ASMs to reflect both efficacy and adverse effects in patients with newly diagnosed epilepsy. MATERIALS AND METHODS: We identified newly diagnosed epilepsy patients with ASM treatment for more than 12 months using CDW of a tertiary referral hospital. Clinical characteristics were compared between groups with successful and unsuccessful treatment. Cox regression analysis was performed to evaluate independent variables of age, sex, comorbidities, and attributes of ASM regimens. RESULTS: Of 2515 eligible participants, 46.2% were successfully treated with the first ASM regimen, and 74.7% with all ASM regimens with the median time-to-treatment success of 14 months. Participants with second-generation ASM as the first ASM were more likely to be successfully treated with the first regimen compared to those with first-generation ASM (51.6% vs. 42.3%, p<0.001) and more successfully treated [hazard ratio (HR)=1.26; 95% confidence interval (CI): 1.15-1.39]. Overall, valproic acid was the most common ASM across a wide range of ages under 65 years, while levetiracetam in patients aged over 65 years or lamotrigine in female adult patients. Clinical factors associated with less favorable treatment outcomes included renal disease (HR=0.78; 95% CI: 0.66-0.92), liver disease (HR=0.65; 95% CI: 0.52-0.81), depression (HR=0.70; 95% CI: 0.57-0.84), and mechanical ventilation (HR=0.58; 95% CI: 0.50-0.67). CONCLUSION: Second-generation ASMs have the advantage of more successful treatment with fewer ASM regimen changes compared with first-generation drugs. Various comorbid conditions as well as age and sex should be considered when selecting ASMs.

Clinical Outcomes and Systemic Complications Related to the Severity and Etiology of Status Epilepticus Using a Common Data Model.

BACKGROUND: Status epilepticus (SE) is a critical neurological emergency in patients with neurological and nonneurological diseases. Mortality rises with SE severity. However, whether brain injury or systemic organ dysfunction causes death after SE remains unclear. We studied clinical outcomes and systemic dysfunctions associated with SE using standardized data from the common data model. This model includes clinical evaluations and treatments that provide real-world evidence for standard practice. METHODS: This retrospective cohort study used the common data model database of a single tertiary academic medical center. Patients diagnosed with SE (corresponding to G41 of the International Classification of Diseases 10 and administration of antiseizure medication) between January 1, 2001, and January 1, 2018, were enrolled. Demographics, classifications of SE severity, and outcomes were collected as operational definitions by using a common data model format. Systemic complications were defined based on the Sequential Organ Failure Assessment criteria. RESULTS: The electronic medical records of 1,825,196 patients were transformed into a common data model, and 410 patients were enrolled. The proportion of patients classified as having nonrefractory SE was 65.4% (268/410), followed by refractory (28.5%, 117/410) and super-refractory SE (6.1%, 25/410). Patients with more severe SE had longer intensive care unit and hospital stays. Renal dysfunction and thrombocytopenia were higher in the in-hospital death group (P = 0.002 and 0.003, respectively). In multivariable analysis, the Acute Physiology and Chronic Health Evaluation II score and platelet count were significantly different in the in-hospital death group (odds ratio, 1.169, P = 0.004; and 0.989, P = 0.043). CONCLUSIONS: Systemic complications after SE, especially low platelet counts, were linked to worse outcomes and increased mortality in a common data model. The common data model offers expandability and comprehensive analysis, making it a potentially valuable tool for SE research.

Machine Learning-assisted Quantitative Mapping of Intracortical Axonal Plasticity Following a Focal Cortical Stroke in Rodents.

Stroke destroys neurons and their connections leading to focal neurological deficits. Although limited, many patients exhibit a certain degree of spontaneous functional recovery. Structural remodeling of the intracortical axonal connections is implicated in the reorganization of cortical motor representation maps, which is considered to be an underlying mechanism of the improvement in motor function. Therefore, an accurate assessment of intracortical axonal plasticity would be necessary to develop strategies to facilitate functional recovery following a stroke. The present study developed a machine learning-assisted image analysis tool based on multi-voxel pattern analysis in fMRI imaging. Intracortical axons originating from the rostral forelimb area (RFA) were anterogradely traced using biotinylated dextran amine (BDA) following a photothrombotic stroke in the mouse motor cortex. BDA-traced axons were visualized in tangentially sectioned cortical tissues, digitally marked, and converted to pixelated axon density maps. Application of the machine learning algorithm enabled sensitive comparison of the quantitative differences and the precise spatial mapping of the post-stroke axonal reorganization even in the regions with dense axonal projections. Using this method, we observed a substantial extent of the axonal sprouting from the RFA to the premotor cortex and the peri-infarct region caudal to the RFA. Therefore, the machine learningassisted quantitative axonal mapping developed in this study can be utilized to discover intracortical axonal plasticity that may mediate functional restoration following stroke.

Age-integrated artificial intelligence framework for sleep stage classification and obstructive sleep apnea screening.

Introduction: Sleep is an essential function to sustain a healthy life, and sleep dysfunction can cause various physical and mental issues. In particular, obstructive sleep apnea (OSA) is one of the most common sleep disorders and, if not treated in a timely manner, OSA can lead to critical problems such as hypertension or heart disease. Methods: The first crucial step in evaluating individuals' quality of sleep and diagnosing sleep disorders is to classify sleep stages using polysomnographic (PSG) data including electroencephalography (EEG). To date, such sleep stage scoring has been mainly performed manually via visual inspection by experts, which is not only a time-consuming and laborious process but also may yield subjective results. Therefore, we have developed a computational framework that enables automatic sleep stage classification utilizing the power spectral density (PSD) features of sleep EEG based on three different learning algorithms: support vector machine, k-nearest neighbors, and multilayer perceptron (MLP). In particular, we propose an integrated artificial intelligence (AI) framework to further inform the risk of OSA based on the characteristics in automatically scored sleep stages. Given the previous finding that the characteristics of sleep EEG differ by age group, we employed a strategy of training age-specific models (younger and older groups) and a general model and comparing their performance. Results: The performance of the younger age-specific group model was similar to that of the general model (and even higher than the general model at certain stages), but the performance of the older age-specific group model was rather low, suggesting that bias in individual variables, such as age bias, should be considered during model training. Our integrated model yielded an accuracy of 73% in sleep stage classification and 73% in OSA screening when MLP algorithm was applied, which indicates that patients with OSA could be screened with the corresponding accuracy level only with sleep EEG without respiration-related measures. Discussion: The current outcomes demonstrate the feasibility of AI-based computational studies that when combined with advances in wearable devices and relevant technologies could contribute to personalized medicine by not only assessing an individuals' sleep status conveniently at home but also by alerting them to the risk of sleep disorders and enabling early intervention.

Earlier chronotype in midlife as a predictor of accelerated brain aging: a population-based longitudinal cohort study.

STUDY OBJECTIVES: Evidence suggests that sleep-wake cycle disruption could be an early manifestation of neurodegeneration and might even be a risk factor for developing diseases in healthy adults. We investigated the impact of circadian phase change on structural and functional brain deterioration in a late-adulthood population. METHODS: We analyzed the data of 1874 participants (mean age 58.6 ± 6.3 years, 50.3% female) from the Korean Genome and Epidemiology Study, who were identified as cognitively unimpaired. The mid-sleep time on free days corrected for sleep debt on workdays (MSFsc) at baseline was adopted as an indicator of the chronotype and used to categorize the participants into three groups. The relationships between the chronotype and longitudinal changes in the gray matter volume (GMV) and cognitive function were investigated (mean interval: 4.2 ± 0.5 years). RESULTS: The mean MSFsc of the participants was 2:45 am. The earlier MSFsc was linearly associated with smaller right entorhinal GMV (ß [SE] = 0.02 [0.01]; p = .001) and lower visual memory function test scores at baseline. Longitudinally, the earlier MSFsc at baseline was only significantly associated with more rapid atrophy in the temporal lobe (ß [SE] = 0.18 [0.07]; p = .018) and not with other brain lobes or subregions. Moreover, the earlier MSFsc was associated with more deteriorated verbal learning and visual memory function test scores. CONCLUSIONS: An earlier chronotype in midlife, measured using a questionnaire, can be a valuable indicator for individuals who should be closely monitored for the development of neurodegenerative disorders.

Chrysanthemum morifolium and Its Bioactive Substance Enhanced the Sleep Quality in Rodent Models via Cl- Channel Activation.

Dried Chrysanthemum morifolium (Chry) flowers have been used in Korea as a traditional insomnia treatment. In this study, the sleep-promoting activity and improving sleep quality of Chry extract (ext) and its active substance linarin were analyzed by pentobarbital-induced sleep experiment in mice and electroencephalography (EEG), electromyogram (EMG) analysis in rats. In a dose-dependent manner, Chry ext and linarin promoted longer sleep duration in the pentobarbital-induced sleep test compared to pentobarbital-only groups at both hypnotic and subhypnotic doses. Chry ext administration also significantly improved sleep quality, as seen in the relative power of low-frequency (delta) waves when compared with the control group. Linarin increased Cl- uptake in the SH-SY5Y human cell line and chloride influx was reduced by bicuculline. After administration of Chry ext, the hippocampus, frontal cortex, and hypothalamus from rodents were collected and blotted for glutamic acid decarboxylase (GAD)65/67 and gamma-aminobutyric acid (GABA)A receptors subunit expression levels. The expression of α1-subunits, ß2-subunits, and GAD65/67 of the GABAA receptor was modulated in the rodent brain. In conclusion, Chry ext augments pentobarbital-induced sleep duration and enhances sleep quality in EEG waves. These effects might be due to the activation of the Cl- channel.

Factors Affecting Depressive Symptoms in Children and Adolescents With Epilepsy.

Objectives: We aimed to evaluate the clinical and psychological factors influencing depressive symptoms in children and adolescents with epilepsy. Methods: We administered self-reported questionnaires assessing children's depressive symptoms (Children's Depression Inventory, CDI) and anxiety (Revised Children's Manifest Anxiety Scale, RCMAS) to children and adolescents with epilepsy (n=87, age range=6-17 years). We asked their parents to complete questionnaires on epilepsy-related variables, parental stress (Questionnaire on Resources and Stress, QRS), parental anxiety (State-Trait Anxiety Inventory, STAI), family functioning (Family Adaptability and Cohesion Evaluation Scale, FACES), children's attention problems (Abbreviated Conners Parent Rating Scale Revised, CPRS), and children's behavioral problems (Korean Child Behavior Checklist, K-CBCL). Stepwise multiple regression analysis was performed to identify predictive variables affecting depressive symptoms. Results: Family adaptability (r=-0.240, p=0.026), family cohesion (r=-0.381, p<0.001), children's attention problems (r=0.290, p=0.006), children's anxiety (r=0.714, p<0.001), children's behavioral problems (r=0.371, p<0.001), parental anxiety (r=0.320, p=0.003), and parental stress (r=0.335, p=0.002) were significantly correlated with children's depressive symptoms. Children's anxiety (ß=0.655, p<0.001) and parental stress (ß=0.198, p=0.013) were significantly related to their depressive symptoms (adjusted R2=0.539). Conclusion: Clinicians should detect and manage children's anxiety and parental stress, which may affect depressive symptoms in children and adolescents with epilepsy.

Neuromodulation Using Transcranial Focused Ultrasound on the Bilateral Medial Prefrontal Cortex.

Transcranial focused ultrasound (tFUS) is a promising technique of non-invasive brain stimulation for modulating neuronal activity with high spatial specificity. The medial prefrontal cortex (mPFC) has been proposed as a potential target for neuromodulation to prove emotional and sleep qualities. We aim to set up an appropriate clinical protocol for investigating the effects of tFUS stimulation of the bilateral mPFC for modulating the function of the brain-wide network using different sonication parameters. Seven participants received 20 min of 250 kHz tFUS to the bilateral mPFC with excitatory (70% duty cycle with sonication interval at 5 s) or suppressive (5% duty cycle with no interval) sonication protocols, which were compared to a sham condition. By placing the cigar-shaped sonication focus on the falx between both mPFCs, it was possible to simultaneously stimulate the bilateral mPFCs. Brain activity was analyzed using continuous electroencephalographic (EEG) recording during, before, and after tFUS. We investigated whether tFUS stimulation under the different conditions could lead to distinctive changes in brain activity in local brain regions where tFUS was directly delivered, and also in adjacent or remote brain areas that were not directly stimulated. This kind of study setting suggests that dynamic changes in brain cortical responses can occur within short periods of time, and that the distribution of these responses may differ depending on local brain states and functional brain architecture at the time of tFUS administration, or perhaps, at least temporarily, beyond the stimulation time. If so, tFUS could be useful for temporarily modifying regional brain activity, modulating functional connectivity, or reorganizing brain functions associated with various neuropsychiatric diseases, such as insomnia and depression.

Silicon optrode array with monolithically integrated SU-8 waveguide and single LED light source.

Objective. This paper presents a conventional light emitting diode (LED) and polymer waveguide coupled silicon optrode array.Approach. Unique lens design at the waveguide inlet enables a high light coupling efficiency with a single LED light source, and provides small power consumption compatible with a wireless optogenetic neuromodulation system. To increase the light intensity at the waveguide tip, a lensed waveguide is fabricated with epoxy-based photoresist SU-8, which has a plano-convex lens shape at the waveguide inlet to focus the light in the horizontal direction. In addition, a cylindrical lens is assembled in front of the waveguide inlet to focus the source light in the vertical direction.Main results. The glass cylindrical lens and SU-8 plano-convex lens increased the light coupling efficiency by 6.7 dB and 6.6 dB, respectively. The fabricated 1 × 4 array of optrodes is assembled with a single LED with 465 nm wavelength, which produces a light intensity of approximately 2.7 mW mm-2at the SU-8 waveguide outlet when 50 mA input current is applied to the LED. Each optrode has four recording electrodes at the SU-8 waveguide outlet. The average impedance of the iridium oxide (IrOx) electroplated recording electrodes is 43.6 kΩ.Significance.In-vivoexperiment at the hippocampus region CA1 and CA2 demonstrated the capability of optical stimulation and neural signal recording through the LED and SU-8 waveguide coupled silicon optrode array.

Transferring Structured Knowledge in Unsupervised Domain Adaptation of a Sleep Staging Network.

Automatic sleep staging based on deep learning (DL) has been attracting attention for analyzing sleep quality and determining treatment effects. It is challenging to acquire long-term sleep data from numerous subjects and manually labeling them even though most DL-based models are trained using large-scale sleep data to provide state-of-the-art performance. One way to overcome this data shortage is to create a pre-trained network with an existing large-scale dataset (source domain) that is applicable to small cohorts of datasets (target domain); however, discrepancies in data distribution between the domains prevent successful refinement of this approach. In this paper, we propose an unsupervised domain adaptation method for sleep staging networks to reduce discrepancies by re-aligning the domains in the same space and producing domain-invariant features. Specifically, in addition to a classical domain discriminator, we introduce local discriminators - subject and stage - to maintain the intrinsic structure of sleep data to decrease local misalignments while using adversarial learning to play a minimax game between the feature extractor and discriminators. Moreover, we present several optimization schemes during training because the conventional adversarial learning is not effective to our training scheme. We evaluate the performance of the proposed method by examining the staging performances of a baseline network compared with direct transfer (DT) learning in various conditions. The experimental results demonstrate that the proposed domain adaptation significantly improves the performance though it needs no labeled sleep data in target domain.

CLOCK Genetic Variations Are Associated With Age-Related Changes in Sleep Duration and Brain Volume.

BACKGROUND: Although a connection between sleep disruption and brain aging has been documented, biological mechanisms need to be further clarified. Intriguingly, aging is associated with circadian rhythm and/or sleep dysfunction in a key gene regulating circadian rhythm, Circadian Locomotor Output Cycles Kaput (CLOCK), has been linked to both aging-related sleep disturbances and neurodegenerative diseases. This study aims to investigate how CLOCK genetic variation associates with sleep duration changes and/or volumetric brain alteration. METHODS: This population-based cross-sectional study used data from the Korean Genome Epidemiology Study and analyzed sleep characteristics and genetic and brain imaging data in 2 221 participants (mean 58.8 ± 6.8 years, 50.2% male). Eleven single-nucleotide polymorphisms (SNPs) in CLOCK were analyzed using PLINK software v1.09 to test for their association with sleep duration and brain volume. Haplotype analysis was performed by using pair-wise linkage disequilibrium of CLOCK polymorphisms, and multivariate analysis of covariance was for statistical analysis. RESULTS: Decreased sleep duration was associated with several SNPs in CLOCK intronic regions, with the highest significance for rs10002541 (p = 1.58 × 10-5). Five SNPs with the highest significance (rs10002541, rs6850524, rs4580704, rs3805151, rs3749474) revealed that CGTCT was the most prevalent. In the major CGTCT haplotype, decreased sleep duration over time was associated with lower cortical volumes predominantly in frontal and parietal regions. Less common haplotypes (GCCTC/CGTTC) had shorter sleep duration and more decreases in sleep duration over 8 years, which revealed smaller total and gray matter volumes, especially in frontal and temporal regions of the left hemisphere. CONCLUSION: CLOCK genetic variations could be involved in age-related sleep and brain volume changes.

Can Corticomuscular Coherence Differentiate between REM Sleep Behavior Disorder with or without Parkinsonism?

REM sleep behavior disorder (RBD) could be a predictor of Parkinsonism even before development of typical motor symptoms. This study aims to characterize clinical features and corticomuscular and corticocortical coherence (CMC and CCC, respectively) during sleep in RBD patients with or without Parkinsonism. We enrolled a total of 105 subjects, including 20 controls, 54 iRBD, and 31 RBD+P patients, patients who were diagnosed as idiopathic RBD (iRBD) and RBD with Parkinsonism (RBD+P) in our neurology department. We analyzed muscle atonia index (MAI) and CMC between EEG and chin/limb muscle electromyography (EMG) and CCC during different sleep stages. Although differences in the CMC of iRBD group were observed only during REM sleep, MAI differences between groups were noted during both REM and NREM N2 stage sleep. During REM sleep, CMC was higher and MAI was reduced in iRBD patients compared to controls (p = 0.001, p < 0.001, respectively). Interestingly, MAI was more reduced in RBD+P compared to iRBD patients. In comparison, CCC was higher in iRBD patients compared to controls whereas CCC was lower in RBD+P groups compared to control and iRBD groups in various frequency bands during both NREM N2 and REM sleep stages. Among them, increased CMC during REM sleep revealed correlation between clinical severities of RBD symptoms. Our findings indicate that MAI, CMC, and CCC showed distinctive features in iRBD and RBD+P patients compared to controls, suggesting potential usefulness to understand possible links between these diseases.

Mortality Risk within 14 Days after Coronavirus Disease 2019 Diagnosis in Dementia Patients: A Nationwide Analysis.

INTRODUCTION: The study evaluated the increased mortality risk within 14 days of coronavirus disease 2019 (COVID-19) diagnosis in dementia patients. METHODS: This retrospective study was conducted from February to April 2020 using the COVID-19 patients' database from the Korea Disease Control and Prevention Agency. The risk factors for early death within 14 days were determined using generalized logistic regression performed in a stepwise manner. Dementia patients diagnosed with COVID-19 were used for the study. The propensity score-matched cohort was included as controls. The differences in mortality within 14 days after COVID-19 diagnosis between the dementia patients and controls were evaluated. RESULTS: We enrolled 5,349 COVID-19 patients from the database; 224 had dementia as comorbidity. The mortality rate within 14 days after COVID-19 diagnosis in dementia patients and the controls was 23.7% versus 1.7%, respectively, before propensity score matching (PSM) (p < 0.001), and 23.7% versus 9.2% after PSM (p < 0.001). The hazard ratio (HR) for mortality within 14 days in COVID-19 patients with dementia was significant even after PSM (HR 5.104, 95% confidence interval 2.889-5.673, p < 0.001). The survival curve of dementia patients was steeply inclined within 14 days after COVID-19 diagnosis, resulting in 70.7% of all deaths in dementia patients. CONCLUSIONS: COVID-19 patients with dementia had a higher risk of early death within 14 days. Thus, prompt intervention is necessary for dementia patients after COVID-19 diagnosis.

Cardiac Autonomic Dysfunction Is Associated with Severity of REM Sleep without Atonia in Isolated REM Sleep Behavior Disorder.

123I-metaiodobenzylguanidine (MIBG) cardiac scintigraphy was performed to assess cardiac autonomic dysfunction and demonstrate its correlation with clinical and polysomnographic characteristics in patients with isolated rapid eye movement (REM) sleep behavior disorder. All subjects including 39 patients with isolated REM sleep behavior disorder and 17 healthy controls underwent MIBG cardiac scintigraphy for cardiac autonomic dysfunction assessment. The isolated REM sleep behavior disorder was confirmed by in-lab overnight polysomnography. A receiver operating curve was constructed to determine the cut-off value of the early and delayed heart-to-mediastinum ratio in patients with isolated REM sleep behavior disorder. Based on each cut-off value, a comparison analysis of REM sleep without atonia was performed by dividing isolated REM sleep behavior disorder patients into two groups. MIBG uptake below the cut-off value was associated with higher REM sleep without atonia. The lower heart-to-mediastinum ratio had significantly higher REM sleep without atonia (%), both with cut-off values of early (11.0 ± 5.6 vs. 29.3 ± 23.2%, p = 0.018) and delayed heart-to-mediastinum ratio (9.1 ± 4.3 vs. 30.0 ± 22.9%, p = 0.011). These findings indicate that reduced MIBG uptake is associated with higher REM sleep without atonia in isolated REM sleep behavior disorder.

Cerebellar White Matter Abnormalities in Charcot-Marie-Tooth Disease: A Combined Volumetry and Diffusion Tensor Imaging Analysis.

Charcot-Marie-Tooth disease (CMT) is a genetically heterogeneous hereditary peripheral neuropathy. Brain volumetry and diffusion tensor imaging (DTI) were performed in 47 controls and 47 CMT patients with PMP22 duplication (n = 10), MFN2 (n = 15), GJB1 (n = 11), or NEFL mutations (n = 11) to investigate for structural changes in the cerebellum. Volume of cerebellar white matter (WM) was significantly reduced in CMT patients with NEFL mutations. Abnormal DTI findings were observed in the superior, middle, and inferior cerebellar peduncles, predominantly in NEFL mutations and partly in GJB1 mutations. Cerebellar ataxia was more prevalent in the NEFL mutation group (72.7%) than the GJB1 mutation group (9.1%) but was not observed in other genotypic subtypes, which indicates that structural cerebellar abnormalities were associated with the presence of cerebellar ataxia. However, NEFL and GJB1 mutations did not affect cerebellar gray matter (GM), and neither cerebellar GM nor WM abnormalities were observed in the PMP22 duplication or MFN2 mutation groups. We found structural evidence of cerebellar WM abnormalities in CMT patients with NEFL and GJB1 mutations and an association between cerebellar WM involvement and cerebellar ataxia in these genetic subtypes, especially in the NEFL subgroup. Therefore, we suggest that neuroimaging, such as MRI volumetry or DTI, for CMT patients could play an important role in detecting abnormalities of cerebellar WM.

Sex differences in deterioration of sleep properties associated with aging: a 12-year longitudinal cohort study.

STUDY OBJECTIVES: The sleep patterns of humans are greatly influenced by age and sex and have various effects on overall health as they change continuously during the lifespan. We investigated age-dependent changes in sleep properties and their relation to sex in middle-aged individuals. METHODS: We analyzed data from 2,640 participants (mean age of 49.8 ± 6.8 years at baseline, 50.6% women) in the Korean Genome and Epidemiology Study, which assessed sleep habits using the Pittsburgh Sleep Quality Index and other clinical characteristics. We analyzed the sleep habit changes that occurred between baseline and a follow-up point (mean interval: 12.00 ± 0.16 years). Associations of age and sex with 9 sleep characteristics were evaluated. RESULTS: Age was associated with most of the sleep characteristics cross-sectionally and longitudinally (P < .05), except for the time in bed at baseline (P = .455) and change in sleep duration (P = .561). Compared with men, women had higher Pittsburgh Sleep Quality Index scores, shorter time in bed, shorter sleep duration, and longer latency at baseline (P ≤ .001). Longitudinal deterioration in Pittsburgh Sleep Quality Index score, habitual sleep efficiency, duration, and latency was more prominent in women (P < .001). The sex differences in these longitudinal sleep changes were mainly noticeable before age 60 years (P < .05). Worsening of Pittsburgh Sleep Quality Index scores, habitual sleep efficiency, and latency was most evident in perimenopausal women. Men presented with greater advancement of chronotype (P = .006), with the peak sex-related difference occurring when they were in their late 40s (P = .048). CONCLUSIONS: Aging is associated with substantial deterioration in sleep quantity and quality as well as chronotype advancement, with the degree and timing of these changes differing by sex.

Embracing diversity and inclusivity in an academic setting: Insights from the Organization for Human Brain Mapping.

Scientific research aims to bring forward innovative ideas and constantly challenges existing knowledge structures and stereotypes. However, women, ethnic and cultural minorities, as well as individuals with disabilities, are systematically discriminated against or even excluded from promotions, publications, and general visibility. A more diverse workforce is more productive, and thus discrimination has a negative impact on science and the wider society, as well as on the education, careers, and well-being of individuals who are discriminated against. Moreover, the lack of diversity at scientific gatherings can lead to micro-aggressions or harassment, making such meetings unpleasant, or even unsafe environments for early career and underrepresented scientists. At the Organization for Human Brain Mapping (OHBM), we recognized the need for promoting underrepresented scientists and creating diverse role models in the field of neuroimaging. To foster this, the OHBM has created a Diversity and Inclusivity Committee (DIC). In this article, we review the composition and activities of the DIC that have promoted diversity within OHBM, in order to inspire other organizations to implement similar initiatives. Activities of the committee over the past four years have included (a) creating a code of conduct, (b) providing diversity and inclusivity education for OHBM members, (c) organizing interviews and symposia on diversity issues, and (d) organizing family-friendly activities and providing childcare grants during the OHBM annual meetings. We strongly believe that these activities have brought positive change within the wider OHBM community, improving inclusivity and fostering diversity while promoting rigorous, ground-breaking science. These positive changes could not have been so rapidly implemented without the enthusiastic support from the leadership, including OHBM Council and Program Committee, and the OHBM Special Interest Groups (SIGs), namely the Open Science, Student and Postdoc, and Brain-Art SIGs. Nevertheless, there remains ample room for improvement, in all areas, and even more so in the area of targeted attempts to increase inclusivity for women, individuals with disabilities, members of the LGBTQ+ community, racial/ethnic minorities, and individuals of lower socioeconomic status or from low and middle-income countries. Here, we present an overview of the DIC's composition, its activities, future directions and challenges. Our goal is to share our experiences with a wider audience to provide information to other organizations and institutions wishing to implement similar comprehensive diversity initiatives. We propose that scientific organizations can push the boundaries of scientific progress only by moving beyond existing power structures and by integrating principles of equity and inclusivity in their core values.

A longitudinal observational population-based study of brain volume associated with changes in sleep timing from middle to late-life.

STUDY OBJECTIVES: Sleep behaviors are related to brain structure and function, but the impact of long-term changes in sleep timing on brain health has not been clearly addressed. The purpose of this study was to examine the association of longitudinal changes in sleep timing from middle to late-life with gray matter volume (GMV), an important marker of brain aging. METHODS: We enrolled 1798 adults (aged 49-82 years, men 54.6%) who underwent magnetic resonance imaging (MRI) between 2011 and 2014. Midsleep time (MST) on free days corrected for sleep debt on workdays was adopted as a marker of sleep timing. Data on MST were available at the time of MRI assessment and at examinations that were given 9 years earlier (2003-2004). Longitudinal changes in MST over the 9-year period were derived and categorized into quartiles. Subjects in quartile 1 were defined as "advancers" (MST advanced ≥ 1 h) while those in quartile 4 were defined as "delayers" (MST delayed ≥ 0.2 h). Quartiles 2-3 defined a reference group (MST change was considered modest). The relationship of GMV with MST changes over 9 years was investigated. RESULTS: Nine-year change in MST were significantly associated with GMV. Compared to the reference group, advancers had smaller GMVs in the frontal and temporal regions. A delay in MST was also associated with smaller cerebellar GMV. CONCLUSIONS: In middle-to-late adulthood, the direction of change in MST is associated with GMV. While advancers and delayers in MST tend to present lower GMV, associations appear to differ across brain regions.

Radiomics features of hippocampal regions in magnetic resonance imaging can differentiate medial temporal lobe epilepsy patients from healthy controls.

To investigative whether radiomics features in bilateral hippocampi from MRI can identify temporal lobe epilepsy (TLE). A total of 131 subjects with MRI (66 TLE patients [35 right and 31 left TLE] and 65 healthy controls [HC]) were allocated to training (n = 90) and test (n = 41) sets. Radiomics features (n = 186) from the bilateral hippocampi were extracted from T1-weighted images. After feature selection, machine learning models were trained. The performance of the classifier was validated in the test set to differentiate TLE from HC and ipsilateral TLE from HC. Identical processes were performed to differentiate right TLE from HC (training set, n = 69; test set; n = 31) and left TLE from HC (training set, n = 66; test set, n = 30). The best-performing model for identifying TLE showed an AUC, accuracy, sensitivity, and specificity of 0.848, 84.8%, 76.2%, and 75.0% in the test set, respectively. The best-performing radiomics models for identifying right TLE and left TLE subgroups showed AUCs of 0.845 and 0.840 in the test set, respectively. In addition, multiple radiomics features significantly correlated with neuropsychological test scores (false discovery rate-corrected p-values < 0.05). The radiomics model from hippocampus can be a potential biomarker for identifying TLE.