Patients with movement disorders such as Parkinson’s disease (PD) living in remote and underserved areas often have limited access to specialized healthcare, while the feasibility and reliability of the video-based examination remains unclear. The aim of this narrative review is to examine which parts of remote neurological assessment are feasible and reliable in movement disorders. Clinical studies have demonstrated that most parts of the video-based neurological examination are feasible, even in the absence of a third party, including stance and gait—if an assistive device is not required—bradykinesia, tremor, dystonia, some ocular mobility parts, coordination, and gross muscle power and sensation assessment. Technical issues (video quality, internet connection, camera placement) might affect bradykinesia and tremor evaluation, especially in mild cases, possibly due to their rhythmic nature. Rigidity, postural instability and deep tendon reflexes cannot be remotely performed unless a trained healthcare professional is present. A modified version of incomplete Unified Parkinson’s Disease Rating Scale (UPDRS)-III and a related equation lacking rigidity and pull testing items can reliably predict total UPDRS-III. UPDRS-II, -IV, Timed “Up and Go”, and non-motor and quality of life scales can be administered remotely, while the remote Movement Disorder Society (MDS)-UPDRS-III requires further investigation. In conclusion, most parts of neurological examination can be performed virtually in PD, except for rigidity and postural instability, while technical issues might affect the assessment of mild bradykinesia and tremor. The combined use of wearable devices may at least partially compensate for these challenges in the future.

Background/Objectives: Access to specialized care for patients with cognitive impairment in remote areas is often limited. Despite the increasing adoption of telemedicine, standardized guidelines have not yet been specified. This study aimed to develop a comprehensive protocol for the specialized neurological, neuropsychological, and neuropsychiatric assessment of patients with cognitive disorders in remote areas through telemedicine. Methods: We analyzed data from (i) a comprehensive literature review of the existing recommendations, reliability studies, and telemedicine models for cognitive disorders, (ii) insights from a three-year experience of a specialized telemedicine outpatient clinic for cognitive movement disorders in Greece, and (iii) suggestions coming from dementia specialists experienced in telemedicine (neurologists, neuropsychologists, psychiatrists) who took part in three focus groups. A critical synthesis of the findings was performed in the end. Results: The final protocol included: technical and organizational requirements (e.g., a high-resolution screen and a camera with zoom, room dimensions adequate for gait assessment, a noise-canceling microphone); medical history; neurological, neuropsychiatric, and neuropsychological assessment adapted to videoconferencing; ethical–legal aspects (e.g., data security, privacy, informed consent); clinician–patient interaction (e.g., empathy, eye contact); diagnostic work-up; linkage to other services (e.g., tele-psychoeducation, caregiver support); and instructions for treatment and follow-up. Conclusions: This protocol is expected to serve as an example of good clinical practice and a source for official telemedicine guidelines for cognitive disorders. Ultimate outcomes include the potential enhanced access to specialized care, minimized financial and logistical costs, and the provision of a standardized, effective model for the remote diagnosis, treatment, and follow-up. This model could be applied not only in Greece, but also in other countries with similar healthcare systems and populations living in remote, difficult-to-access areas.

Efficient filtering in biosignals acquisition is challenging. The resistance of the sources exhibits inter- and intra-subject variability or is unknown; thus, using passive filters before the first amplification stage is problematic. Conversely, filtering after amplification does not effectively eliminate the amplified electrical noise, main’s interference, and the artifacts. In this context, the design and utilization of filters in the analog front end of biosensors, in conjunction with the first amplification stage, is not common but offers substantial advantages. In this study, the design of a novel Multi-feedback Differential Filter Instrumentation Amplifier (MFDFIA) is proposed. The design and the equations governing the gain and bandwidth characteristics of the MFDFIA are presented, and relevant topologies are explored. Even though MFDFIA has two op-amps in its first stage, due to its symmetric topology, the analysis can be conducted separately for the differential- and common-mode input signal with a simplified one op-amp equivalent circuit. Notably, MFDFIA’s CMRR is equal and depends only on the CMRR of the second stage. An exemplary simulation for EEG signal acquisition is provided, with a flat band of 1db between 0.7 Hz and 25.4 Hz, a gain of 34.1 db, and an input noise of 70.66 nVrms in the range of 0.1–10 Hz.

Efficient filtering in bio-signals acquisition is challenging. The resistance of the sources exhibits inter- and intra-subject variability or is unknown, thus using passive filters before the first amplification stage is problematic. Conversely, filtering after amplification does not effectively eliminate the amplified electrical noise, Main’s Interference, and the artifacts. In this context, the design and utilization of filters in the Analog Front End of biosensors, in conjunction with the first amplification stage, is not common but offers substantial advantages. In this study, the design of a novel Multi-feedback Differential Filter Instrumentation Amplifier (MFDFIA) is proposed. For addressing the aforementioned issues, the design and the equations governing the gain and bandwidth characteristics of the MFDFIA are presented and relevant topologies are explored. Even though MFDFIA has two op-amps in its first stage, due to its symmetric topology, the analysis can be conducted separately for the differential and common mode input signal with a simplified one op-amp equivalent circuit. Notably, MFDFIA’s CMRR is equal and depends only on the CMRR of the second stage. An exemplary application for EEG signal acquisition is provided, which has bandwidth 0.7 to 98.6 Hz, gain 50.3 db, power consumption 11.583 mW and 0.1-10 Hz noise 462.94 nVrms.

Alzheimer’s disease (AD) is a genetically complex disorder in which inherited susceptibility is distributed across rare high-impact variants, the apolipoprotein E (APOE) locus, and a broad polygenic background of common risk alleles. Polygenic risk scores (PRSs) provide a quantitative framework for summarizing this distributed genetic liability and have become increasingly important tools for risk stratification, cohort enrichment, and biological interpretation. In this review, we synthesize evidence on AD PRSs across multiple biological and clinical scales, moving from genes and molecular pathways to biomarkers, brain structure and function, cognition, behavior, and clinical translation. Genome-wide PRSs capture aggregate inherited risk, whereas pathway-, tissue-, cell-type-, and biomarker-specific PRSs may help clarify the biological routes through which genetic susceptibility is expressed, including amyloid and tau metabolism, lipid homeostasis, endosomal trafficking, immune regulation, glial activation, proteostasis, and mitochondrial quality control. Evidence indicates that AD PRSs are most consistently associated with amyloid-related biomarkers, while associations with tau, neurodegeneration, brain atrophy, functional connectivity, cognitive decline, and neuropsychiatric manifestations are more heterogeneous and often depend on APOE, disease stage, biomarker context, and variable computation methodologies. Although PRSs show promise for research enrichment and mechanistic stratification, their individual-level clinical utility remains limited by modest predictive performance, ancestry-related transferability, calibration challenges, and ethical considerations. Future progress will require multi-ancestry cohorts, integration of common and rare variation, multi-omic annotation, longitudinal modeling, and multimodal prediction frameworks. Ultimately, PRSs are best viewed not as stand-alone diagnostic tools, but as cross-scale instruments for linking inherited susceptibility to the heterogeneous biological and clinical manifestations of AD.