Brain Sciences

Background/Objectives: Parkinson’s disease (PD) is clinically heterogeneous, and motor complications are often inadequately captured during routine outpatient assessments. In this cross-sectional observational study, we investigated whether prolonged home-based wearable monitoring could enable digital motor profiling by identifying clinically meaningful patient subgroups through an unsupervised analytical approach. Methods: Seventy-six patients with PD underwent standardized clinical evaluation followed by prolonged home-based monitoring using a single waist-worn inertial sensor. Wearable-derived measures of gait, mobility, and complex motor manifestations, including dyskinesia and freezing of gait (FOG), were analyzed using an unsupervised clustering approach based exclusively on sensor-derived variables. The resulting subgroups were subsequently characterized using clinical and digital outcomes, and cluster stability and interpretability were examined through bootstrap and complementary machine-learning analyses. Results: Unsupervised clustering identified two distinct digital motor profiles that differed primarily in the burden of motor complications rather than overall disease severity. Although disease duration, Hoehn and Yahr stage, and clinical motor phenotype did not differ significantly, patients in one subgroup exhibited significantly greater dyskinesia, motor fluctuations, and dopaminergic treatment requirements, accompanied by a distinct sensor-derived motor profile during daily life. Dyskinesia-related metrics emerged as the strongest contributors to profile discrimination, and the identified clusters were reproduced with high cross-validated agreement by the surrogate classification model. Conclusions: Prolonged home-based wearable monitoring may support clinically meaningful digital motor profiling in PD. Beyond conventional clinical staging, this approach can identify patients with different burdens of motor complications under real-world conditions. Following appropriate validation, objective digital biomarkers could support patient stratification, longitudinal monitoring, and treatment optimization.

Background/Objectives: Auricular Vagal Neuromodulation Therapy (AVNT) constitutes a promising non-invasive therapeutic approach for Disorders of Consciousness (DoCs). However, evidence regarding its effectiveness remains limited. In this context, although the Mismatch Negativity (MMN) is a well-established neurophysiological index associated with recovery in DoCs, its modulation by AVNT has not yet been investigated. This study aimed to evaluate the effect of AVNT on MMN in DoCs. Methods: Forty-four participants with DoCs were evenly assigned to either an experimental or a control group, alongside twenty-two healthy participants used to establish normative data in neurotypical conditions. The latency, peak and peak-to-peak MMN amplitude were assessed as a complement to the Coma Recovery Scale Revised (CRS-R). Subsequently, only the experimental group received AVNT, followed by a second assessment across all groups. Results: The control group showed extensive changes across sessions in both peak and peak-to-peak amplitudes. In contrast, the experimental group exhibited a significant change only in peak amplitude for the fronto-central region, with no changes in peak-to-peak amplitude. Between-session changes in peak and peak-to-peak amplitudes differed between the control and healthy groups in the parieto-occipital region. After adjustment for the inter-session interval, the ANCOVA showed an overall group effect on peak-to-peak amplitude changes in the parieto-occipital and right regions. No differences were detected between the control and experimental groups or between the experimental and healthy groups. Finally, no significant effects were found for MMN latency. Conclusions: These findings could be compatible with a possible effect of AVNT, although the evidence remained insufficient to demonstrate a direct effect of the intervention.

Cancer-related cognitive impairment (CRCI) is often attributed to chemotherapy, yet cognitive deficits are detectable in some patients before systemic treatment begins. This review examines a proposed two-hit framework in which cancer-related psychological and physiological stress contributes to pretreatment neural vulnerability, followed by superimposed chemotherapy-associated neurotoxicity. The review focuses on adults with non-central nervous system cancers. This narrative review synthesizes epidemiologic, neuroendocrine, neuroimaging, neuroimmune, autonomic, preclinical, and clinical evidence addressing chronic stress biology, pretreatment cognitive and brain changes, chemotherapy-related neurotoxicity, hypothalamic–pituitary–adrenal (HPA) axis interactions, and potential interventions. Pretreatment cognitive impairment has been reported in approximately one-quarter of patients, accompanied by elevated inflammatory cytokines, altered neurotrophic signaling, and changes in amygdala–hippocampal connectivity. The first hit may reflect interacting cancer- and host-related factors, including systemic inflammation, distress, sleep disruption, fatigue, pain, and HPA-axis/circadian dysregulation. The second hit occurs when chemotherapy inflicts superimposed neurotoxic injury, including white matter degradation, suppressed hippocampal neurogenesis, and blood–brain barrier disruption. The relationship may also be bidirectional: chemotherapy can perturb HPA-axis function, while glucocorticoid signaling may influence treatment response and antitumor immunity. Behavioral, autonomic, and circadian interventions are mechanistically promising, although neuroprotective efficacy remains uncertain. CRCI is likely multifactorial and may begin before chemotherapy. The two-hit framework integrates pretreatment stress-related vulnerability with treatment-associated neurotoxicity but remains hypothesis-generating rather than causally established. Prospective studies beginning at diagnosis and incorporating serial cognitive, psychological, endocrine, inflammatory, autonomic, and neuroimaging measures are needed to test this model and identify patients at greatest risk.