Comorbidities of Migraine

Migraine is a common neurological disorder and can be severely disabling during attacks. The highest prevalence occurs between the ages of 25 and 55 years, potentially the most productive period of life. Migraine leads to a burden not only for the individual, but also for the family and society in general. Prior studies have found that migraine occurs together with other illnesses at a greater coincidental rate than is seen in the general population. These occurrences are called “comorbidities,” which means that these disorders are interrelated with migraine. To delineate the comorbidities of migraine is important, because it can help improve treatment strategies and the understanding of the possible pathophysiology of migraine. The comorbid illnesses in patients with migraine include stroke, sub-clinical vascular brain lesions, coronary heart disease, hypertension, patent foramen ovale, psychiatric diseases (depression, anxiety, bipolar disorder, panic disorder, and suicide), restless legs syndrome, epilepsy and asthma. In this paper, we review the existing epidemiological and hospital-based studies, and illustrate the connections between these illnesses and migraine

Topological Pattern Recognition of Severe Alzheimer's Disease via Regularized Supervised Learning of EEG Complexity

Alzheimer's disease (AD) is a progressive brain disorder with gradual memory loss that correlates to cognitive deficits in the elderly population. Recent studies have shown the potentials of machine learning algorithms to identify biomarkers and functional brain activity patterns across various AD stages using electroencephalography (EEG). In this study, we aim to discover the altered spatio-temporal patterns of EEG complexity associated with AD pathology in different severity levels. We employed the multiscale entropy (MSE), a complexity measure of time series signals, as the biomarkers to characterize the nonlinear complexity at multiple temporal scales. Two regularized logistic regression methods were applied to extracted MSE features to capture the topographic pattern of MSEs of AD cohorts compared to healthy baseline. Furthermore, canonical correlation analysis was performed to evaluate the multivariate correlation between EEG complexity and cognitive dysfunction measured by the Neuropsychiatric Inventory scores. 123 participants were recruited and each participant was examined in three sessions (length = 10 seconds) to collect resting-state EEG signals. MSE features were extracted across 20 time scale factors with pre-determined parameters (m = 2, r = 0.15). The results showed that comparing to logistic regression model, the regularized learning methods performed better for discriminating severe AD cohort from normal control, very mild and mild cohorts (test accuracy ~ 80%), as well as for selecting significant biomarkers arcoss the brain regions. It was found that temporal and occipitoparietal brain regions were more discriminative in regard to classifying severe AD cohort vs. normal controls, but more diverse and distributed patterns of EEG complexity in the brain were exhibited across individuals in early stages of AD

Use of the Chinese (Taiwan) Version of the Social Phobia Inventory (SPIN) Among Early Adolescents in Rural Areas: Reliability and Validity Study

BackgroundTo assess the screening abilities of the Chinese (Taiwan) version of the Social Phobia Inventory (SPIN) for evaluating social phobia in an adolescent community sample.MethodsA total of 3,393 students (1,669 boys, 1,724 girls), aged 13–15, completed the SPIN questionnaire. A total of 144 students were enrolled for validity. The Mini-International-Neuropsychiatric-Interview-Kid (MINI-Kid) was used to establish Diagnostic and Statistical Manual of Mental Disorders–IV diagnosis.ResultsThe mean SPIN total score of all subjects was 14.2 ± 9.4, which was higher in girls than in boys (14.7 ± 9.4 vs. 13.7 ± 9.1; p < 0.01). The 7th graders had the highest SPIN total scores compared with the 8th and 9th graders (15.4 ± 9.7 vs. 13.4 ± 9.1 and 14.0 ± 9.4; p < 0.001). Internal consistency (Cronbach's α = 0.85) and test–retest reliability (r = 0.73) were both good. A cut-off score of 25 resulted in balanced sensitivity (80%) and specificity (77%).ConclusionThe Chinese (Taiwan) SPIN has good screening abilities. The cut-offs are different from those in other countries, and highlight the importance of culturally adapted cut-offs

The VOICE study – a before and after study of a dementia communication skills training course

Background A quarter of acute hospital beds are occupied by persons living with dementia, many of whom have communication problems. Healthcare professionals lack confidence in dementia communication skills, but there are no evidence-based communication skills training approaches appropriate for professionals working in this context. We aimed to develop and pilot a dementia communication skills training course that was acceptable and useful to healthcare professionals, hospital patients and their relatives. Methods The course was developed using conversation analytic findings from video recordings of healthcare professionals talking to patients living with dementia in the acute hospital, together with systematic review evidence of dementia communication skills training and taking account of expert and service-user opinion. The two-day course was based on experiential learning theory, and included simulation and video workshops, reflective diaries and didactic teaching. Actors were trained to portray patients living with dementia for the simulation exercises. Six courses were run between January and May 2017. 44/45 healthcare professionals attended both days of the course. Evaluation entailed: questionnaires on confidence in dementia communication; a dementia communication knowledge test; and participants’ satisfaction. Video-recorded, simulated assessments were used to measure changes in communication behaviour. Results Healthcare professionals increased their knowledge of dementia communication (mean improvement 1.5/10; 95% confidence interval 1.0–2.0; p<0.001). Confidence in dementia communication also increased (mean improvement 5.5/45; 95% confidence interval 4.1–6.9; p<0.001) and the course was well-received. One month later participants reported using the skills learned in clinical practice. Blind-ratings of simulated patient encounters demonstrated behaviour change in taught communication behaviours to close an encounter, consistent with the training, but not in requesting behaviours. Conclusion We have developed an innovative, evidence-based dementia communication skills training course which healthcare professionals found useful and after which they demonstrated improved dementia communication knowledge, confidence and behaviour

A holo-spectral EEG analysis provides an early detection of cognitive decline and predicts the progression to Alzheimer’s disease

AimsOur aim was to differentiate patients with mild cognitive impairment (MCI) and Alzheimer’s disease (AD) from cognitively normal (CN) individuals and predict the progression from MCI to AD within a 3-year longitudinal follow-up. A newly developed Holo-Hilbert Spectral Analysis (HHSA) was applied to resting state EEG (rsEEG), and features were extracted and subjected to machine learning algorithms.MethodsA total of 205 participants were recruited from three hospitals, with CN (n = 51, MMSE &gt; 26), MCI (n = 42, CDR = 0.5, MMSE ≥ 25), AD1 (n = 61, CDR = 1, MMSE &lt; 25), AD2 (n = 35, CDR = 2, MMSE &lt; 16), and AD3 (n = 16, CDR = 3, MMSE &lt; 16). rsEEG was also acquired from all subjects. Seventy-two MCI patients (CDR = 0.5) were longitudinally followed up with two rsEEG recordings within 3 years and further subdivided into an MCI-stable group (MCI-S, n = 36) and an MCI-converted group (MCI-C, n = 36). The HHSA was then applied to the rsEEG data, and features were extracted and subjected to machine-learning algorithms.Results(a) At the group level analysis, the HHSA contrast of MCI and different stages of AD showed augmented amplitude modulation (AM) power of lower-frequency oscillations (LFO; delta and theta bands) with attenuated AM power of higher-frequency oscillations (HFO; beta and gamma bands) compared with cognitively normal elderly controls. The alpha frequency oscillation showed augmented AM power across MCI to AD1 with a reverse trend at AD2. (b) At the individual level of cross-sectional analysis, implementation of machine learning algorithms discriminated between groups with good sensitivity (Sen) and specificity (Spec) as follows: CN elderly vs. MCI: 0.82 (Sen)/0.80 (Spec), CN vs. AD1: 0.94 (Sen)/0.80 (Spec), CN vs. AD2: 0.93 (Sen)/0.90 (Spec), and CN vs. AD3: 0.75 (Sen)/1.00 (Spec). (c) In the longitudinal MCI follow-up, the initial contrasted HHSA between MCI-S and MCI-C groups showed significantly attenuated AM power of alpha and beta band oscillations. (d) At the individual level analysis of longitudinal MCI groups, deploying machine learning algorithms with the best seven features resulted in a sensitivity of 0.9 by the support vector machine (SVM) classifier, with a specificity of 0.8 yielded by the decision tree classifier.ConclusionIntegrating HHSA into EEG signals and machine learning algorithms can differentiate between CN and MCI as well as also predict AD progression at the MCI stage

Brain-Derived Neurotrophic Factor Gene Val66Met Polymorphism Modulates Reversible Cerebral Vasoconstriction Syndromes

BACKGROUND: Reversible cerebral vasoconstriction syndrome (RCVS) could be complicated by cerebral ischemic events. Hypothetical mechanisms of RCVS involve endothelial dysfunction and sympathetic overactivity, both of which were reported to be related to brain-derived neurotrophic factor (BDNF). The study investigated the association between functional BDNF Val66Met polymorphism and RCVS. METHODS: Patients with RCVS and controls were prospectively recruited and genotyped for the BDNF Val66Met polymorphism. Magnetic resonance angiography (MRA) and transcranial color-coded Doppler sonography were employed to evaluate cerebral vasoconstriction. Genotyping results, clinical parameters, vasoconstriction scores, mean flow velocities of the middle cerebral artery (V(MCA)), and Lindegaard indices were analyzed. Split-sample approach was employed to internally validate the data. PRINCIPAL FINDINGS: Ninety Taiwanese patients with RCVS and 180 age- and gender-matched normal controls of the same ethnicity completed the study. The genotype frequencies did not differ between patients and controls. Compared to patients with Met/Met homozygosity, patients with Val allele had higher mean vasoconstriction scores of all arterial segments (1.60±0.72 vs. 0.87±0.39, p<0.001), V(MCA) values (116.7±36.2 vs. 82.7±17.9 cm/s, p<0.001), and LI (2.41±0.91 vs. 1.89±0.41, p = 0.001). None of the Met/Met homozygotes, but 38.9% of the Val carriers, had V(MCA) values of >120 cm/s (p<0.001). Split-sample validation by randomization, age, entry time or residence of patients demonstrated concordant findings. CONCLUSIONS: Our findings link BDNF Val66Met polymorphism with the severity of RCVS for the first time and implicate possible pathogenic mechanisms for vasoconstriction in RCVS