Objectives To compare cognitive effects and acceptability of repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS) in patients with Alzheimer’s disease (AD) or mild cognitive impairment (MCI), and to determine whether cognitive training (CT) during rTMS or tDCS provides additional benefits. Methods Electronic search of PubMed, Medline, Embase, the Cochrane Library and PsycINFO up to 5 March 2020. We enrolled double-blind, randomised controlled trials (RCTs). The primary outcomes were acceptability and pre–post treatment changes in general cognition measured by Mini-Mental State Examination, and the secondary outcomes were memory function, verbal fluency, working memory and executive function. Durability of cognitive benefits (1, 2 and ≥3 months) after brain stimulation was examined. Results We included 27 RCTs (n=1070), and the treatment components included high-frequency rTMS (HFrTMS) and low-frequency rTMS, anodal tDCS (atDCS) and cathodal tDCS (ctDCS), CT, sham CT and sham brain stimulation. Risk of bias of evidence in each domain was low (range: 0%–11.1%). HFrTMS (1.08, 9, 0.35–1.80) and atDCS (0.56, 0.03–1.09) had short-term positive effects on general cognition. CT might be associated with negative effects on general cognition (−0.79, –2.06 to 0.48) during rTMS or tDCS. At 1-month follow-up, HFrTMS (1.65, 0.77–2.54) and ctDCS (2.57, 0.20–4.95) exhibited larger therapeutic responses. Separate analysis of populations with pure AD and MCI revealed positive effects only in individuals with AD. rTMS and tDCS were well tolerated. Conclusions HFrTMS is more effective than atDCS for improving global cognition, and patients with AD may have better responses to rTMS and tDCS than MCI.

The aim of this study was to conduct a comprehensive analysis of the placement of multiple wearable sensors for the purpose of analyzing and classifying the gaits of patients with neurological disorders. Seven inertial measurement unit (IMU) sensors were placed at seven locations: the lower back (L5) and both sides of the thigh, distal tibia (shank), and foot. The 20 subjects selected to participate in this study were separated into two groups: stroke patients (11) and patients with neurological disorders other than stroke (brain concussion, spinal injury, or brain hemorrhage) (9). The temporal parameters of gait were calculated using a wearable device, and various features and sensor configurations were examined to establish the ideal accuracy for classifying different groups. A comparison of the various methods and features for classifying the three groups revealed that a combination of time domain and gait temporal feature-based classification with the Multilayer Perceptron (MLP) algorithm outperformed the other methods of feature-based classification. The classification results of different sensor placements revealed that the sensor placed on the shank achieved higher accuracy than the other sensor placements (L5, foot, and thigh). The placement-based classification of the shank sensor achieved 89.13% testing accuracy with the Decision Tree (DT) classifier algorithm. The results of this study indicate that the wearable IMU device is capable of differentiating between the gait patterns of healthy patients, patients with stroke, and patients with other neurological disorders. Moreover, the most favorable results were reported for the classification that used the combination of time domain and gait temporal features as the model input and the shank location for sensor placement.

Previous functional imaging studies in episodic cluster headache (CH) patients revealed altered brain metabolism concentrated on the central descending pain control system. However, it remains unclear whether changes in brain metabolism during the "in bout" period are due to structural changes and whether these structural changes vary between the "in bout" and "out of bout" periods. To quantify brain structural changes in CH patients, the regional gray matter volume (GMV) was compared among 49 episodic CH patients during the "in bout" period and 49 age- and sex-matched controls. Twelve patients were rescanned during the "out of bout" period to evaluate the changes, if any, between these 2 periods. Compared with healthy controls, CH patients showed significant "in bout" GMV reductions in the bilateral middle frontal, left superior, and medial frontal gyri. Compared to "out of bout" scans, the "in bout" scans revealed significant GMV increases in the left anterior cingulate, insula, and fusiform gyrus. Additionally, compared to healthy controls, the "out of bout" scans revealed a trend of GMV reduction in the left middle frontal gyrus. These affected regions primarily belong to frontal pain modulation areas, and thus these GMV changes may reflect insufficient pain-modulating capacity in the frontal areas of CH patients.

Migraine has been associated with sleep disturbances. Relationship between sleep quality and migraine frequency is yet to be determined. The present study aimed to investigate sleep disturbances among low-frequency, moderate-frequency, high-frequency, and chronic migraineurs, with and without auras, with well-controlled confounding variables.This cross-sectional controlled study included 357 subjects from an outpatient headache clinic in Taiwan. Standardized questionnaires were utilized to collect demographic, migraine, sleep, depression, anxiety, and restless leg syndrome characteristics in all participants. According to frequency of migraine attacks, patients were divided into 4 groups: with 1 to 4 migraine days per month, 5 to 8 migraine days in a month, 9 to 14 migraine days in a month, and >14 migraine days per month. The Pittsburgh Sleep Quality Index (PSQI) and subgroup items were used to evaluate sleep quality. The association between migraine frequency and sleep quality was investigated using multivariable linear regression and logistic regression.The PSQI total score was highest in patients with high frequent migraine (10.0 ± 3.4) and lowest in controls (7.0 ± 3.4) with a significant trend analysis (P for trend = 0.006). Migraine frequency had an independent effect on the items "Cannot get to sleep within 30 minutes" (P < 0.001), "Wake up in the middle of the night or early morning" (P < 0.001), "Bad dreams" (P = 0.001), "Pain" (P = 0.004), and "Quality of sleep" (P < 0.001). The result showed the effect of migraine frequency in both the aura-present (P for trend = 0.008) and the aura-absent subgroups (P for trend = 0.011).High migraine frequency correlates with poor sleep quality and a higher prevalence of poor sleepers. These associations occur in migraine with aura and without aura.

Autism spectrum disorder (ASD) is a highly heritable and heterogeneous neurodevelopmental disorder characterized by impairments in communication, reciprocal social interaction, and restricted and repetitive behaviors or interests.^1,2 Several environmental risk factors (eg, advanced parental age and maternal overweight)^2,3 and more than 100 genes and genomic regions have been found to be associated with ASD; most of these genes contribute to synaptic structure and function or chromatic modification.² Patients with ASD often present with a wide range of developmental, psychiatric, physical, and neurologic comorbidities that can influence their functional status, treatment strategies, and childhood development.⁴ A multisite surveillance program in the United States reported that only 15% of children aged 8 years with ASD did not have any comorbidities.¹