Frontiers in Neuroscience最新文献

Introduction: Auditory beat perception (ABP) is crucial for rhythmic auditory stimulation (RAS), a therapeutic approach for neurological disorders, and particularly Parkinson's disease (PD). The mechanism is thought to be associated with increased activation of the cerebellum and motor-related regions. However, few studies have explored its effects on patients with multiple system atrophy (MSA). This study aimed to compare the compensatory mechanism of ABP in patients with PD and MSA.

Methods: Participants with PD and MSA and normal controls (NCs) were recruited from National Cheng Kung University Hospital. During task-based functional MRI (fMRI), the participants listened to rhythmic beat sounds using a boxcar paradigm. Basic characteristics and brain volumes were compared between the PD and MSA groups, and fMRI findings were further compared with the NCs.

Results: There were no significant differences in age, gender, motor scores or disease duration between PD (n = 16) and MSA (n = 14) groups. Brain volume analysis revealed a significant reduction in cerebellar volume in the MSA group. ABP analysis via fMRI demonstrated greater activation in the left premotor cortex in MSA groups compared to NCs. Activation in the right cerebellum was significantly higher in PD group but absent in the MSA group. Different correlation trends between activation and cerebellar volume were observed.

Conclusion: ABP in the PD and MSA groups showed distinct compensatory mechanisms by cerebellar and premotor cortex activation, respectively. These findings provide insights into compensatory mechanisms regarding RAS therapy in patients with atypical parkinsonian disorders.

Conventionally, evaluations of haptic interfaces have relied on self-reported assessments, which offer limited objectivity and can disrupt the user experience, making it challenging to design interfaces that dynamically adapt to users' cognitive state in real time. To overcome these limitations, cognitive haptic interfaces leverage neurophysiological measures such as EEG and deep learning to directly capture the brain's responses to haptic stimulation. A key challenge is how to label these neural responses: do we ground models in objectively controlled Physical Stimulation (PS) parameters, or in participants' Self-Reported (SR) perceptions? The goal of this work is not to demonstrate that EEG can reproduce subjective reports, but rather to systematically examine how neural responses relate to these two aspects of haptic experience by training deep learning models under both PS and SR labeling schemes. Here, we investigate how PS- versus SR-based labeling impacts model performance across four modalities: (i) delayed force-feedback (DFF), (ii) fingertip vibration feedback (FVF), (iii) upper-body vibration feedback (UVF), and (iv) fingertip thermal feedback (FTF). We evaluate three deep learning benchmarked architectures: ATCNet, EEG Inception, and EEG Conformer on EEG data labeled according to both approaches. Across all modalities, PS-labeled models yield more stable and higher performance than SR-labeled models in a group-level leave-one-subject-out (LOSO) setting, with the largest gains at near-perceptual-threshold levels (e.g., mild thermal changes, moderate vibration intensities, borderline delay settings) where SR labels are most variable across individuals. Rather than aiming to replace self-reports, these results reveal when EEG-based models align more closely with the physical stimulation than with participants' reports and support using PS-trained decoders as a structured first-stage representation that can later be adapted with user-specific SR information.

Background: The supplementary motor area (SMA) is a cortical region involved in motor and language functions. Motor representations within the SMA follow a somatotopic organization: Anterior regions are linked to orofacial movements, middle regions to upper limb movements, and posterior regions to lower limb movements. SMA lesions may produce impairments that correspond to this somatotopy; therefore, preoperative assessment may aid diagnosis.

Objective: This study aimed to revise and extend a protocol for assessing the SMA using navigated repetitive transcranial magnetic stimulation (nrTMS), incorporating somatotopic organization and validating positive stimulation points against non-motor regions.

Methods: The dominant-hemisphere SMA of 30 healthy participants (27.1 ± 6.21 years, 18 female) was examined. After mapping of the primary motor cortex with single-pulse TMS, six predefined SMA sites were stimulated using 20 Hz nrTMS while participants performed the Nine Hole Peg Test (NHPT; 120% resting motor threshold (RMT)), the lower extremity motor coordination test (LEMOCOT; 140% RMT), and an orofacial task (130% RMT). Each test was repeated under identical parameters at non-motor control sites. Kinematic measurements were obtained using high-speed recordings.

Results: SMA stimulation disrupted upper extremity function, with the strongest effects observed at posterior sites. In contrast, lower extremity performance was not impaired during SMA stimulation, where tapping speed increased under validation conditions. Orofacial effects were limited and inconsistent, occurring mainly during stimulation outside the SMA and showing no significant spatial pattern.

Conclusion: The expected somatotopic organization of the SMA could not be demonstrated using nrTMS. However, SMA-selective disruptions of upper extremity movements suggest a functional, rather than effector-specific, organization. The novel kinematic paradigm enabled detailed, objective analysis of movement phases and may benefit future TMS studies.

Background: Alzheimer's disease (AD) presents a significant global health challenge, with its prevalence projected to increase substantially by 2050. Despite its widespread impact, the underlying causes and mechanisms remain incompletely understood, complicating efforts toward effective diagnosis and treatment. Pathologically, AD is marked by the accumulation of senile plaques and neurofibrillary tangles, but the relationship between these factors and disease progression is complex and heterogeneous.

Objective: The present study aimed to compare the efficacy of different deep/machine learning models based on MRI scanning.

Methods: The study follows rigorous systematic review protocols, adhering to the Cochrane Handbook of Systematic Reviews and Interventions and the PRISMA guidelines. A comprehensive search strategy was employed across multiple databases, including PubMed, Web of Science, Cochrane, Medline, and EMBASE. Advanced statistical methods were used for data synthesis and analysis, incorporating network meta-analysis and machine learning techniques to evaluate the accuracy and efficacy of different diagnostic models.

Results: The meta-analysis included 11 studies that met the predefined inclusion criteria. The studies employed various machine learning algorithms, including CNN, ResNet, and DenseNet, to classify AD and distinguish it from mild cognitive impairment (MCI) and healthy controls. The results indicate that CNN and ResNet consistently outperform other models in terms of classification accuracy. Additionally, the integration of nanotechnology and AI-driven diagnostics demonstrates significant potential in enhancing the diagnostic process.

Conclusion: Despite challenges such as data heterogeneity and the interpretability of AI-driven models, the study highlights the transformative potential of computational techniques and advanced imaging technologies in AD diagnosis and management. The integration of network-based analyses and machine learning approaches offers promising avenues for future research, aiming to revolutionize the understanding and approach to Alzheimer's disease.

Background: Individuals with Autism Spectrum Disorder (ASD) consistently exhibit difficulties in comprehending figurative language. While prior neuroimaging studies have identified discrepancies in brain activation between ASD individuals and neurotypical controls (NCs) during such tasks, the specific and consistent neural patterns underlying these deficits remain unclear.

Methods: We conducted an activation likelihood estimation (ALE) meta-analysis to identify consistent brain activation patterns associated with figurative language comprehension in ASD. A systematic literature search was performed across five databases (PubMed, Embase, Web of Science, CNKI, Wanfang) up to January 31, 2025. Six functional magnetic resonance imaging (fMRI) studies, comprising 95 individuals with ASD and 98 NCs, met the inclusion criteria.

Results: The analysis revealed that during figurative language comprehension, both ASD and NCs consistently activated a core network involving the bilateral superior temporal gyrus (STG), transverse temporal gyrus, and right insula. A conjunction analysis confirmed the stability of these shared regions. Crucially, the meta-analysis of group differences (NCs > ASD) identified a significant cluster of hypoactivation in the ASD group within the left STG and middle temporal gyrus (MTG). No significant hyperactivation was found in the ASD group compared to NCs.

Conclusion: This meta-analysis demonstrates that while individuals with ASD recruit typical language networks during figurative language comprehension, they exhibit a consistent pattern of reduced neural recruitment in the left STG and MTG. This hypoactivation may reflect a dual deficit encompassing reduced efficiency in semantic access/integration and impaired socio-linguistic integration, providing a neurobiological substrate for the pragmatic language difficulties characteristic of ASD.

Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD42023467185, CRD42023467185.

Introduction: Prion diseases (PrD) are a group of rapidly progressive dementias. Among its subtypes, Creutzfeldt-Jakob Disease (CJD) is the most common, affecting around 1-2 individuals per million inhabitants yearly. In 2005, Brazil's Ministry of Health (MH) initiated a surveillance program for CJD, creating a protocol to report the cases. Despite advances, the MH still struggles to make a reliable database to determine PrD profile in Brazil. Therefore, the aim of the present study was to understand the Brazilian PrD surveillance system.

Methods: This is a retrospective and descriptive study based on the epidemiological records of CJD surveillance from 2005 to 2021 in the Ministry of Health's Epidemiological Bulletin published in 2022.

Results: 1,576 suspected cases of CJD were reported, concentrated in the Southeast, South and Northeast regions of Brazil. Among the notifications, the following age groups predominated: 55-74 years (60.2%), 45-54 years (15%), and 75-85 years (11.8%). Suspected cases were mainly represented by women (53.6%), white individuals (61.4%), and residents of urban areas (90%). 547 cases (34.7%) were confirmed based on the following confirmation criteria: laboratory (65.6%), clinical-epidemiological (29.4%), and not informed (4.9%).

Conclusion: In this period, the expected number of cases for the Brazilian population would be 3,200. Additionally to underreporting, the data shared by MH is limited due to the use of a database destined to mainly observe epidemic outbreaks and mismatches between official documents. Despite some progress since 2005, PrD surveillance in Brazil faces significant problems, due to the inaccurate treatment of these data and the lack of a specific database for CJD.

Objective: This study investigated the distinct mechanistic pathways linking depression, sleep disturbances, and cognitive decline in Parkinson's disease, with emphasis on differential roles of daytime sleepiness versus rapid eye movement sleep disorder.

Participants: The study included 450 Parkinson's disease patients enrolled in the Parkinson's Progression Markers Initiative (PPMI) between 2010 and 2024, leveraging longitudinal clinical and biomarker data.

Measurements: Depressive symptoms (Geriatric Depression Scale, GDS), sleep disturbances (Epworth Sleepiness Scale, ESS for daytime sleepiness; REM Sleep Behavior Disorder Questionnaire, RBDSQ), and cognition (Montreal Cognitive Assessment, MoCA) were analyzed using mixed-effects models and structural equation modeling (SEM) with bootstrapped mediation.

Results: Structural equation modeling demonstrated excellent model fit (CFI/TLI > 0.94; RMSEA < 0.08). Analyses revealed distinct pathological pathways: (1) Excessive daytime sleepiness impaired cognition fully through depression mediation (indirect effect β = -0.084, 95%CI [-0.097,-0.028], 66.1% mediated), while (2) REM sleep behavior disorder showed both direct negative effects on cognition (β = -0.116, p = 0.035) and depression-mediated effects (indirect effect β = -0.071, 95%CI [-0.110,-0.029], 38.2% mediated). These results highlight different intervention targets - treating depressive symptoms may fully mitigate cognitive impacts of daytime hypersomnia, while RBD may require both antidepressant and direct neuroprotective approaches.

Conclusion: Divergent neurobehavioral mechanisms underlie sleep-related cognitive decline in PD: daytime sleepiness operates through mood dysregulation, while rapid eye movement sleep disorder involves additional direct neural insult. Interventions targeting these pathways may require distinct strategies.

Introduction: Subthalamic nucleus deep brain stimulation (STN-DBS) is a standard-of-care (SoC) treatment for the motor symptoms of Parkinson's disease (PD); however, how therapeutic DBS influences motor output is incompletely understood. Specifically, the extent of electromyographic (EMG) modulation during DBS at therapeutic (or SoC) amplitude and its relationship to activity state could be better characterized.

Methods: We studied the effects of DBS on muscle activity in sixteen participants receiving STN-DBS (in 17 stimulated hemispheres) by recording EMG activity in bilateral biceps, triceps, flexor carpi radialis, and extensor digitorum communis muscles. Data was acquired in the resting state and while participants alternated between isometric contraction and brief relaxation. We recorded EMG activity during low-frequency stimulation at participant-specific therapeutic amplitude and during stimulation using pulse amplitudes slightly above (125%) and below (75%) this level. Stimulus-locked responses from each condition were evaluated for the presence of a myogenic evoked potential.

Results: DBS at therapeutic amplitude elicited a myogenic response almost exclusively in the contralateral upper extremity (CUE), with at least one response occurring in 16 out of 17 hemispheres. Myogenic responses, which typically started between 10-30 milliseconds and often lasted until ~70-150 milliseconds post-stimulation, were more common in biceps/triceps. Responses were more prevalent during active contraction compared to relax/rest states as stimulation amplitude increased.

Discussion: These findings support that STN-DBS-induced myogenic activity is commonplace at therapeutic stimulation amplitudes used clinically; thus, studies evaluating the degree to which myogenic effects during SoC STN DBS are associated with the clinical and side effects of STN DBS therapy are warranted.

Multiphoton microscopy allows the imaging of biological phenomena deep within brain tissues and has greatly advanced knowledge in neuroscience. However, many optical phenomena other than the multiphoton excitation of fluorophores in nonlinear optics are underrecognized. Coherent Raman scattering (CRS) uses multiple photons to boost weak Raman scattering. CRS has been used to enable molecular vibration-dependent contrast imaging of tissues and has been particularly useful for pathophysiological investigations of brain tissues. Recently, the combination of CRS with Raman-active bio-orthogonal chemical bonds or groups has proven particularly powerful for visualizing molecules not detectable by fluorescence imaging. This review introduces a new and exciting imaging strategy and its applications in neuroscience.