Explore the vast range of titles available.

There are few studies investigating the short-term effects of Virtual Reality based Exergaming (EG) on motor and cognition simultaneously and pursue the brain functional activity changes after these interventions in patients with Parkinson’s Disease (PD). The purpose of this study was to investigate the synergistic therapeutic effects of Virtual Reality based EG on motor and cognitive symptoms in PD and its possible effects on neuroplasticity. Eligible patients with the diagnosis of PD were randomly assigned to one of the two study groups: (1) an experimental EG group, (2) an active control Exercise Therapy (ET) group. All patients participated in a 4-week exercise program consisting of 12 treatment sessions. Every session lasted 60 min. Participants underwent a motor evaluation, extensive neuropsychological assessment battery and rs-fMRI before and after the interventions. Thirty patients fulfilled the inclusion criteria and were randomly assigned to the EG and ET groups. After the dropouts, 23 patients completed the assessments and interventions (11 in EG, 13 in ET). Within group analysis showed significant improvements in both groups. Between group comparisons considering the interaction of group × time effect, showed superiority of EG in terms of general cognition, delayed visual recall memory and Boston Naming Test. These results were consistent in the within-group and between-group analysis. Finally, rs-fMRI analysis showed increased activity in the precuneus region in the time × group interaction in the favor of EG group. EG can be an effective alternative in terms of motor and cognitive outcomes in patients with PD. Compared to ET, EG may affect brain functional connectivity and can have beneficial effects on patients’ cognitive functions and motor symptoms. Whenever possible, using EG and ET in combination, may have the better effects on patients daily living and patients can benefit from the advantages of both interventions.

Alzheimer’s disease (AD), is characterized by its progressive feature and loss of cognitive functions, is common among dementia types. There is no curative treatment of the disease today. In recent years, transcranial magnetic stimulation (TMS) techniques together with drug therapy have been explored by experts considering that they will produce beneficial results. Repetetive TMS (rTMS) can modulate cortical excitability and prevent long-term neuroplastic changes. The aim of this study is an updated and comprehensive systematic review of studies using TMS/rTMS in AD patients. Our study was designed as a systematic review prepared according to the PRISMA guideline. In this study, English and Turkish AD-TMS articles that entered the literature published between 2002 and 2017 were included. Randomized and non-randomized controlled clinical studies on humans evaluating the effectiveness of rTMS applications at different concentrations, durations and different regions in AD have been reviewed. The databases we used were Pubmed®, MEDLINE®, Webofscience®, EMBASE®, Türkiye Atif Dizini®. Keywords were “TMS, rTMS, Alzheimers Disease” used in our search, 116 artticles complied with the determined protocol were identified and 14 were included in our study. The studies presented in this review, show the therapeutic potential of rTMS in AD patients. Benefits of rTMS were to communicate with patients and especially caregivers in their daily activities, thereby improving their QoL. The possibility of using TMS to increase neuroplasticity is promising not only to improve our understanding of brain plasticity mechanisms, but also to design new neurorehabilitation strategies.

The prevalence of AD worldwide is estimated to reach 131 million by 2050. Most disease-modifying treatments and drug trials have failed, due partly to the heterogeneous and complex nature of the disease. Alzheimer's disease (AD) is a heterogeneous disorder that spans a continuum with multiple phases, including preclinical, mild cognitive impairment, and dementia. Unlike for most other chronic diseases, human studies reporting on AD gut microbiota in the literature are very limited. With the scarcity of approved drugs for AD therapies, the rational and precise modulation of gut microbiota composition using diet and other tools is a promising approach to the management of AD. Such an approach could be personalized if an AD continuum can first be deconstructed into multiple strata based on specific microbiota features by using single or multiomics techniques. However, stratification of AD gut microbiota has not been systematically investigated before, leaving an important research gap for gut microbiota-based therapeutic approaches. Here, we analyze 16S rRNA amplicon sequencing of stool samples from 27 patients with mild cognitive impairment, 47 patients with AD, and 51 nondemented control subjects by using tools compatible with the compositional nature of microbiota. To stratify the AD gut microbiota community, we applied four machine learning techniques, including partitioning around the medoid clustering and fitting a probabilistic Dirichlet mixture model, the latent Dirichlet allocation model, and we performed topological data analysis for population-scale microbiome stratification based on the Mapper algorithm. These four distinct techniques all converge on Prevotella and Bacteroides stratification of the gut microbiota across the AD continuum, while some methods provided fine-scale resolution in stratifying the community landscape. Finally, we demonstrate that the signature taxa and neuropsychometric parameters together robustly classify the groups. Our results provide a framework for precision nutrition approaches aiming to modulate the AD gut microbiota. IMPORTANCE The prevalence of AD worldwide is estimated to reach 131 million by 2050. Most disease-modifying treatments and drug trials have failed, due partly to the heterogeneous and complex nature of the disease. Recent studies demonstrated that gut dybiosis can influence normal brain function through the so-called “gut-brain axis.” Modulation of the gut microbiota, therefore, has drawn strong interest in the clinic in the management of the disease. However, there is unmet need for microbiota-informed stratification of AD clinical cohorts for intervention studies aiming to modulate the gut microbiota. Our study fills in this gap and draws attention to the need for microbiota stratification as the first step for microbiota-based therapy. We demonstrate that while Prevotella and Bacteroides clusters are the consensus partitions, the newly developed probabilistic methods can provide fine-scale resolution in partitioning the AD gut microbiome landscape.

Mutations in genes encoding the formation of ion channels may cause epileptic syndromes. These epileptic syndromes are generally divided into generalized and partial epilepsies. Among the causative agents of generalized epilepsy showing mendelian or non-mendelian inheritance; mutations in sodium channel, calcium channel, GABAA receptor and nicotinic receptor can be listed. Generalized epilepileptic syndromes with mendelian inheritance are Genetic Epilepsy With Febrile Seizures Plus, Autosomal Dominant Juvenile Myoclonic Epilepsy, and Epilepsy Associated With CLCN2 Gene Mutation. Generalized epileptic syndromes with non-mendelian inheritance are JME and Juvenile Absence Epilepsy With Generalized Tonic-Clonic Seizures. The epilepsies of newborns and infants with a single gene inheritanceare classified into three categories: Benign Familial Neonatal Convulsions, Benign Familial Infantile Convulsions, and Benign Familial Neonatal-Infantile Seizures. Autosomal dominant partial epilepsies are examined under the headings of Autosomal Dominant Nocturnal Frontal Lobe Epilepsy, Familial Mesial Temporal Lobe Epilepsy, Familial Lateral Temporal Lobe Epilepsy, and Autosomal Dominant Partial Epilepsy With Auditory Features. While various mutations in different ion channels can produce similar phenotypes, a certain mutation on the same gene can cause different phenotypes. This review provides a summary of the epilepsy classification on the genetic basis and pathophysiological effects of neural channelopathies causing epileptic syndromes.

Background Repetitive transcranial magnetic stimulation (rTMS) has emerged as a promising alternative therapy for Alzheimer's disease (AD) due to its ability to modulate neural networks and enhance cognitive function. This treatment offers the unique advantage of enabling real‐time monitoring of immediate cognitive effects and dynamic brain changes through electroencephalography (EEG). Objective This study focused on exploring the effects of left parietal rTMS stimulation on visual‐evoked potentials (VEP) and visual event‐related potentials (VERP) in AD patients. Methods Sixteen AD patients were recruited for this longitudinal study. EEG data were collected within a Faraday cage both pre‐ and post‐rTMS to evaluate its impact on potentials. Results Significant alterations were found in both VEP and VERP oscillations. Specifically, delta power in VEP decreased, while theta power in VERP increased post‐rTMS, indicating a modulation of brain activities. Discussion These findings confirm the positive modulatory impact of rTMS on brain activities in AD, evidenced by improved cognitive scores. They align with previous studies highlighting the potential of rTMS in managing hyperexcitability and oscillatory disturbances in the AD cortex. Conclusion Cognitive improvements post‐rTMS endorse its potential as a promising neuromodulatory treatment for cognitive enhancement in AD, thereby providing critical insights into the neurophysiological anomalies in AD and possible therapeutic avenues. Administering rTMS along 2 weeks to the left lateral parietal cortex of AD patients results in a significant decrease in VEP delta power, alongside a marked increase in VERP theta power. This compelling evidence broadens our understanding of rTMS's impact, pointing to it as a potentially efficacious intervention for AD patients. (The figure adapted from biorender.com).

Visual hallucinations (VH) are common in Parkinson’s disease (PD), yet their mechanisms remain poorly understood. Several studies have investigated structural brain correlates of Parkinsonian VH, but critical gaps in knowledge persist. An inverse relationship between auditory hallucinations and paracingulate sulcus (PCS) length, associated with reality-monitoring mechanisms, has been reported. This study examines the relationship between PCS length and VH in PD patients. Sixty-five PD patients (aged 48–81 years) meeting diagnostic criteria were included. The University of Miami Parkinson’s Disease Hallucinations Questionnaire was used to classify patients into PD with VH (PDVH, n = 32) or PD without VH (PDnonVH, n = 33) groups. PCS length was measured using sagittal T1-weighted MRI scans, and total intracranial volume was calculated. Clinical and neuropsychometric assessments were also performed. No significant demographic or clinical differences were found between groups. Total PCS length was significantly shorter in the PDVH group (68.56 ± 38.03 mm) compared to the PDnonVH group (106.36 ± 48.77 mm; p < .01). Right and left PCS lengths were also shorter in the PDVH group (p < .01). Visual immediate and long-term memory scores were significantly lower in the PDVH group (p < .01, p < .05, respectively), while Spatial Boundaries Subtest recognition scores were higher (p < .05). In the PDVH group, semantic fluency scores positively correlated with PCS length (p < .05). Reduced PCS length increased the likelihood of VH (β =−0.020, Odds Ratio = 0.980, p < .01). PCS length may serve as a biomarker indicative of anatomical structures associated with reality-monitoring mechanisms and biological predisposition to hallucinations in patients with PD.

•Migraine patients demonstrated significantly lower scores on the MOCA test.•Hippocampal subfields were correlated with cognitive performance in migraine patients.•Hippocampal changes may shed light on potential dementia risks in migraine patients. The etiology of cognitive decline linked to migraine remains unclear, with a growing recurrence rate and potential increased dementia risk among sufferers. Cognitive dysfunction has recently gained attention as a significant problem among migraine sufferers that can be related to alterations in hippocampal function and structure. This study explores hippocampal subfield connectivity and volume changes in migraine patients. We recruited 90 individuals from Alanya University's Neurology Department, including 49 migraine patients and 41 controls, for functional and anatomical imaging. Using the CONN toolbox and FreeSurfer, we assessed functional connectivity and subfield volumes, respectively. Montreal Cognitive Assessment (MOCA) was used to assess cognition in the entire sample. As a result, migraine patients exhibited significantly lower MOCA scores compared to controls (p<.001). Also, we found significant differences in hippocampal subfields between migraine patients and control groups in terms of functional connectivity after adjusting for years of education; here we showed that the left CA3 showed higher connectivity with right MFG and right occipitolateral cortex. Furthermore, the connectivity of left fimbria with the left temporal lobe and hippocampus and the connectivity of the right hippocampal-tail with right insula, heschl's gyrus, and frontorbital cortex were lower in the migraineurs. Additionally, volumes of specific hippocampal subfields were significantly lower in the migraineurs (whole hippocampus p = 0.004, whole hippocampus head p = 0.003, right CA1 head p = 0.006, and right HATA p = 0.005) compared to controls. In conclusion, these findings indicate that migraine-associated cognitive impairment involves significant functional and structural brain changes, particularly in the hippocampus, which may heighten dementia risk. This pioneering study unveils critical hippocampal alterations linked to cognitive function in migraine sufferers, underscoring the potential for these changes to impact dementia development.

The claustrum is a sheet‐like of telencephalic gray matter structure whose function is poorly understood. The claustrum is considered a multimodal computing network due to its reciprocal connections with almost all cortical areas as well as subcortical structures. Although the claustrum has been involved in several neurodegenerative diseases, specific changes in connections of the claustrum remain unclear in Alzheimer's disease (AD), and Parkinson's disease (PD). Resting‐state fMRI and T1‐weighted structural 3D images from healthy elderly (n = 15), AD (n = 16), and PD (n = 12) subjects were analyzed. Seed‐based FC analysis was performed using CONN FC toolbox and T1‐weighted images were analyzed with the Computational Anatomy Toolbox for voxel‐based morphometry analysis. While we observed a decreased FC between the left claustrum and sensorimotor cortex, auditory association cortex, and cortical regions associated with social cognition in PD compared with the healthy control group (HC), no significant difference was found in alterations in the FC of both claustrum comparing the HC and AD groups. In the AD group, high FC of claustrum with regions of sensorimotor cortex and cortical regions related to cognitive control, including cingulate gyrus, supramarginal gyrus, and insular cortex were demonstrated. In addition, the structural results show significantly decreased volume in bilateral claustrum in AD and PD compared with HC. There were no significant differences in the claustrum volumes between PD and AD groups so the FC may offer more precise findings in distinguishing changes for claustrum in AD and PD. The claustrum is functionally connected to regions associated with characterized networks such as salience, default mode, executive, sensorimotor, visual, and language in the HC.

Alzheimer’s disease (AD) is a progressive, neurodegenerative brain disorder that generally affects the elderly. Today, after the limited benefit of the pharmacological treatment strategies, numerous non-invasive brain stimulation techniques have been developed. Transcranial magnetic stimulation (TMS), based on electromagnetic stimulation, is one of the most widely used methods. The main problem in the use of TMS is the existence of large individual variability in the results. This causes a waste of money, time, and more importantly, a burden for delicate patients. Hence, it is a necessity to form an efficient and personalized TMS application protocol. We performed a machine learning analysis to predict AD patients’ responses to TMS by analyzing their Electroencephalography (EEG) signals. For that purpose, we analyzed both the EEG signals collected before and after the TMS application (EEG1 and EEG2 respectively). Through correlating EEG1 and rTMS outcomes, we tried to predict patients’ responses before the treatment application. On the other hand, by EEG2 analysis, we investigated TMS impacts on EEG and more importantly if this impact is correlated with patients’ response to the treatment. We used the Support Vector Machines (SVM) classifier due to its multiple advantages for the current task with feature selection processes by Stepwise Linear Discriminant Analysis (SWLDA) and SVM. However, to justify our numerical analysis framework, we examined and compared the performances of different feature selection and classification techniques. Since we have a limited sample number, we used the leave-one-out method for the validation with the Monte Carlo technique to eliminate bias by small sample size. In the conclusion, we observed that the correlation between rTMS outcomes and EEG2 is stronger than EEG1, since we observed respectively %93 and %79 accuracies during our data analysis. Besides the informative features of EEG2 are focused on theta band. That indicates that TMS is characterizing the theta band signals in Alzheimer’s patients in direct relation to patients’ response to rTMS. This shows that it is more possible to determine patients’ benefit from the TMS at the early stages of the treatment, which would increase the efficiency of rTMS applications on Alzheimer’s disease patients.

Aim Although there exists substantial epidemiological evidence indicating an elevated risk of dementia in individuals with diabetes, our understanding of the neuropathological underpinnings of the association between Type‐2 diabetes mellitus (T2DM) and Alzheimer's disease (AD) remains unclear. This study aims to unveil the microstructural brain changes associated with T2DM in AD and identify the clinical variables contributing to these changes. Methods In this retrospective study involving 64 patients with AD, 31 individuals had concurrent T2DM. The study involved a comparative analysis of diffusion tensor imaging (DTI) images and clinical features between patients with and without T2DM. The FSL FMRIB software library was used for comprehensive preprocessing and tractography analysis of DTI data. After eddy current correction, the “bedpost” model was utilized to model diffusion parameters. Linear regression analysis with a stepwise method was used to predict the clinical variables that could lead to microstructural white matter changes. Results We observed a significant impairment in the left superior longitudinal fasciculus (SLF) among patients with AD who also had T2DM. This impairment in patients with AD and T2DM was associated with an elevation in creatine levels. Conclusion The white matter microstructure in the left SLF appears to be sensitive to the impairment of kidney function associated with T2DM in patients with AD. The emergence of AD in association with T2DM may be driven by mechanisms distinct from the typical AD pathology. Compromised renal function in AD could potentially contribute to impaired white matter integrity.