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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 a neurodegenerative dementia which causes neurovascular dysfunction and cognitive impairment. Currently 50 million people live with dementia worldwide, and there are nearly 10 million new cases every year. There is a need for relatively less costly and more objective methods of screening and early diagnosis. Functional Near-Infrared Spectroscopy (fNIRS) systems are a promising solution for early detection of AD. For a practical clinically relevant system, a smaller number of optimally placed channels are clearly preferable. In this study we investigated the number and locations of best performing fNIRS channels measuring prefrontal cortex (PFC) activations. Twenty-one subjects diagnosed with AD and eighteen healthy controls were recruited for the study. We have shown that resting-state fNIRS recordings from a small number of prefrontal locations provide a promising methodology for detecting AD and monitoring its progression. A high-density continuous-wave fNIRS system was first used to verify the relatively lower hemodynamic activity in the prefrontal cortical areas observed in AD patients. By using the episode averaged standard deviation of the oxyhemoglobin concentration changes as features that were fed into a Support Vector Machine, we then showed that the accuracy of subsets of optical channels in predicting the presence and severity of AD was significantly above chance. Results suggest that AD can be detected with 0.76 sensitivity and 0.68 specificity while the severity of AD could be detected with 0.75 sensitivity and 0.72 specificity with ≤5 channels. These suggest that fNIRS is a viable technology for conveniently detecting and monitoring AD as well as investigating underlying mechanisms of disease progression.

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 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.

Repetitive transcranial magnetic stimulation (rTMS) has gained interest as a non-invasive treatment for stroke based on the data promoting its effects on functional recovery. However, the exact action mechanisms by which the rTMS exert beneficial effects in cellular and molecular aspect are largely unknown. To elucidate the effects of high- and low-frequency rTMS in the acute-ischemic brain, we examined how rTMS influences injury development, cerebral blood flow (CBF), DNA fragmentation, neuronal survival, pro- and anti-apoptotic protein activations after 30 and 90 min of focal cerebral ischemia. In addition, inflammation, angiogenesis, growth factors and axonal outgrowth related gene expressions, were analyzed. Furthermore, we have investigated the effects of rTMS on post-acute ischemic brain, particularly on spontaneous locomotor activity, perilesional tissue remodeling, axonal sprouting of corticobulbar tracts, glial scar formation and cell proliferation, in which rTMS was applied starting 3 days after the stroke onset for 28 days. In the high-frequency rTMS received animals reduced DNA fragmentation, infarct volume and improved CBF were observed, which were associated with increased Bcl-xL activity and reduced Bax, caspase-1, and caspase-3 activations. Moreover, increased angiogenesis, growth factors; and reduced inflammation and axonal sprouting related gene expressions were observed. These results correlated with reduced microglial activation, neuronal degeneration, glial scar formation and improved functional recovery, tissue remodeling, contralesional pyramidal tract plasticity and neurogenesis in the subacute rTMS treated animals. Overall, we propose that high-frequency rTMS in stroke patients can be used to promote functional recovery by inducing the endogenous repair and recovery mechanisms of the brain.

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.

Post-stroke depression (PSD) is a significant sequela of cerebrovascular accidents, affecting a substantial proportion of stroke survivors. However, it is still unclear whether the existence of depression after stroke is an independent risk factor for stroke recurrence and if the increased risk of cognitive impairment in PSD is related to the location of stroke. We aimed to compare the role of cortical, subcortical and cortico-subcortical infarcts in the development of PSD and cognitive impairment, as well as the role of the existence of depression in stroke recurrence. In this study, a 52-week, randomised, double-blind study consisted of 1059 stroke patients (866 non-depressive and 193 untreated depressive persons) who were matched in terms of demographic and clinical parameters. The Mini Mental State Examination Test (MMSE), Executive function (Trail Making Test Part A), processing speed (colour naming condition of the Stroop test), episodic memory (Rey Auditory Verbal Learning Test [RAVLT], including delayed free recall), semantic memory (verbal fluency test [animal naming]), language processing (Boston Naming Test [(number correct]), visuospatial perception (the bells test) was assessed at the baseline. The lesion sites are subdivided as cortical, subcortical, and cortico-subcortical territory infarcts on MRI. The stroke recurrence ratio was also recorded after a year. In results, we observed a higher rate of depression associated with lesions affecting the cortico-subcortical structures in patients with PSD compared to non-depressive patients (p < 0.05). Our results further indicated impaired cognitive scores in patients with PSD compared to those with non-depressive individuals (p < 0.05). Regarding the risk of stroke recurrence, we also found an increased rate of stroke recurrence in PSD after 12 months (p < 0.05). In detail, binomial logistic regression analyses using the backward Wald method determined that patients with depression (p = 007; odds ratio (OR) = 1.64; CI 1.14–2.35), hypertension (p = 0.004; OR = 1.74; CI 1.19–2.55), atrial fibrillation (p = 0.007; OR = 1.61; CI 1.14–2.28) and older age (p = 0.019; OR = 1.02; CI 1.003–1.03) were significantly predictors for stroke recurrency. Our regression analysis further revealed that PSD was a predictive factor for disabling cognitive test scores (impaired executive function [p < 0.001; OR = 4.51; CI 3.24–6.27], reduced processing speed [p < 0.001; OR = 4.29; CI 3.12–5.91], episodic memory [p < 0.001; OR = 4.65; CI 3.37–6.42), semantic memory [p < 0.001; OR = 4.79; 3.47–6.61], visuospatial [p < 0.001; OR = 6.10; CI 4.36–8.55], and language function [p < 0.001; OR = 5.086; CI 3.67–7.05]) after adjusting for age and education. In conclusion, the present study provides strong evidence confirming the importance of depression in predicting cognitive impairment and recurrence in stroke patients. Despite these positive findings, our findings warrant the performance of further research to demonstrate the efficacy of treatment on stroke recurrence, together with other vascular risk factors and cognitive disorders.

Alzheimer’s disease (AD) is a debilitating neurodegenerative disorder with a global impact, yet its pathogenesis remains poorly understood. While age, metabolic abnormalities, and accumulation of neurotoxic substances are potential risk factors for AD, their effects are confounded by other factors. To address this challenge, we first utilized multi-omics data from 87 well phenotyped AD patients and generated plasma proteomics and metabolomics data, as well as gut and saliva metagenomics data to investigate the molecular-level alterations accounting the host-microbiome interactions. Second, we analyzed individual omics data and identified the key parameters involved in the severity of the dementia in AD patients. Next, we employed Artificial Intelligence (AI) based models to predict AD severity based on the significantly altered features identified in each omics analysis. Based on our integrative analysis, we found the clinical relevance of plasma proteins, including SKAP1 and NEFL, plasma metabolites including homovanillate and glutamate, and Paraprevotella clara in gut microbiome in predicting the AD severity. Finally, we validated the predictive power of our AI based models by generating additional multi-omics data from the same group of AD patients by following up for 3 months. Hence, we observed that these results may have important implications for the development of potential diagnostic and therapeutic approaches for AD patients.

Depressive cognitive impairment is seen in a significant number of patients with depression. However, it remains challenging to differentiate between patients with amnestic (those with subjective cognitive impairment complaints) and non-amnestic major depressive disorder, highlighting the urgent need for additional objective tools to help classify these patients more accurately. We analyzed cognitive state, alterations in regional entropy and functional connectivity measures of the brain between patients with major depression and healthy controls. The depressed cohort was categorized as either “amnestic” or “non-amnestic,” depending on self-reported experiences of forgetfulness. The superior temporal region and insula exhibited altered entropy and connectivity measures in individuals with depression and subjective cognitive impairment, which was correlated with impaired executive functions, a pattern not being evident in the control group. Our findings support the notion that insular and superior temporal entropic alterations are linked to subjective cognitive changes in the pathology of depression. These regions also hold potential as biomarkers for determining the underlying objective cognitive deficits in subjective cognitive complaints in patients with major depressive disorder (MDD). This underscores the need for improved diagnostic approaches and the implementation of practical dynamic neuroimaging modalities capable of addressing the current challenges in diagnosing subjective cognitive impairment in MDD, offering promise for the future management of patients with depression.