Prognostic Implications of Regional Cerebral Blood Flow Abnormality Assessed by Single Photon Emission Computed Tomography in Chronic Heart Failure Patients.

Background"Inflammaging" describes chronic low-grade inflammation observed in aging individuals. It may play a major role in neurodegeneration.ObjectiveTo assess blood inflammatory markers in older adults. We hypothesized that elevated inflammation would be found in some cognitively normal older adults but would be more prevalent in individuals with cognitive impairment.MethodsInterleukin-6 (IL-6) and C-reactive protein (CRP) were assessed in 514 Canadian individuals in COMPASS-ND, a detailed study of cognitive impairment in the elderly. Cumulative link model (CLM) was used to investigate the relationship between inflammation status (low, medium, or high tertiles) and demographic and lifestyle factors along with cognitive function and cognitive diagnoses.ResultsWe found that 12% of cognitively normal older adults had IL-6 levels in the highest tertile, but this increased in cognitively impaired cohorts-36% in Alzheimer's disease, 55% mixed dementia, 30% mild cognitive impairment, and 39% vascular mild cognitive impairment. We found that 36% of cognitively unimpaired older individuals display "elevated" IL-6 (middle and high tertile values), while approximately 70% of those with cognitive impairment also do so. Inflammation markers increased most robustly in association with age, higher body mass index, and higher Fazekas (MRI white matter hyperintensity) score. There were also weaker associations with female sex, nutrition, number of comorbidities, and poor sleep.ConclusionsPeripheral low-grade inflammation was common, particularly in individuals with cognitive impairment; and obesity and age were the main drivers. It remains unclear whether treatment targeting such inflammation might have a therapeutic role in dementia prevention.

To understand medication adherence and associated healthcare costs of patients with early Alzheimer's disease (AD). This retrospective cohort study used the Axon Registry® linked with claims data to examine medication adherence of U.S. patients with early AD (mild cognitive impairment [MCI] and mild dementia due to AD) from 2015 to 2022. Medication adherence was quantified by the proportion of days covered (PDC) over a one-year follow-up, and adherence rate was defined at a PDC ≥ 80%. Patient comorbidities and healthcare costs were described. Of 333 patients included, 213 (64%) were female with a median (IQR) age 79 (72-83) years. Patients had a mean (SD) of 2.3 (2.1) comorbidities and took a mean (SD) of 3.0 (1.5) medications. Weighted-average PDC across medications was 74.4% with 7 out of 10 medication classes having a medication adherence rate lower than 60%. DPP-4 inhibitors had the highest medication adherence rate (66.67% of patients), and memantine had the lowest (39.13% of patients). Annual median (IQR) medical and pharmacy costs per-patient were $5,268 ($1,808-$14,651) and $658 ($187-$2,736), respectively. Patients with early AD had multiple comorbidities and took multiple medications. Suboptimal medication adherence and high healthcare costs were observed.

Dementia with Lewy bodies (DLB) is a common neurodegenerative disorder, yet difficult to diagnose. Carefully selected research cohorts may not represent the clinical reality. We aimed to characterize a naturalistic cohort of patients with clinical features of DLB, reporting their final diagnosis, clinical features, and cognitive profile. Patients were recruited from a specialized cognitive clinic. Data from patient visits such as core clinical features, biomarkers, cognitive screening, and neuropsychological assessment were collected from health records. We used normative data to assess distribution of impairment in patients with DLB and Parkinson's disease (PD) with and without dementia. A total of 143 patients were included in the cohort. Following specialized dementia evaluation, 88 patients fulfilled clinical criteria for DLB, 35 patients for PD with dementia (PDD), 14 had mild cognitive impairment (MCI), and the remaining 6 patients had other types of dementia. Parkinsonism was the most common core clinical feature (87%), followed by visual hallucinations (65%), cognitive fluctuations (52%) and, lastly, probable REM sleep behavior disorder (RBD, 47%). A majority of DLB patients had cognitive impairment on visuospatial constructive, attentional and executive tasks, and visual memory. The differential diagnosis of DLB may be difficult within a clinical context because other cognitive disorders frequently present with core features of DLB. The cognitive profile and frequency of core clinical features in the DLB group were generally in line with previous reports. Probable RBD was lower than in other cohorts, which may reflect challenges in collecting this information in a clinical interview.

When designing socially assistive robots, HRI researchers often focus on robot personality as a means of increasing a person's engagement, enjoyment, and trust. In this work, we argue that using only trait-based personality models is often limited in its ability to capture the nuance that matches end users’ desires, experiences, and cultural backgrounds. To address this gap, we introduce the concept of a robot character , a holistic framing of robot personality that extends the trait-based approach to include external factors, such as shared interests between the user and robot, as sociocultural and environmental factors. We introduced and validated the Robot Role Character Creation (R2C2) tool, an accessible scaffolding tool to co-design robot characters with end users in order to support more nuanced and personalized robots. R2C2 highlights the voices of end users and enables them to easily ideate and communicate their unique robot characters, particularly for populations often underrepresented in robot design. Through a cross-cultural study (the U.S. and Mexico), we validated the R2C2 tool in eliciting rich design insights for robot characters from people with mild cognitive impairment (MCI) and dementia (PwD). We report our findings , enabled by the R2C2 tool, on the role participants envisioned for their desired robot characters, the multidimensionality and adaptability of these robot characters, and how participants’ socio-cultural backgrounds influenced their characters. Our findings demonstrate that R2C2 can facilitate the creation of nuanced and personalized robot characters that resonate with user experiences, needs, and preferences. We analyze how participants envisioned the roles, multidimensionality, and cultural influences shaping their ideal robot characters, highlighting R2C2's ability to capture these diverse perspectives. This work will serve as a basis for HRI designers to create more effective robot interactions, enhance acceptance and trust, and promote engagement with robot characters while centering the wisdom and personhood of people with cognitive impairments.

Objectives Social Cognition (SC) can be impaired in Parkinson’s Disease (PD), yet its longitudinal evolution relative to cognitive status is unclear. This study examined whether SC deficits in PD patients suffers different changes based on baseline cognitive status and cognitive progression. Methods In this observational study 48 non‐demented PD patients (32 with normal cognition [PD‐CN], 16 with mild cognitive impairment [PD‐MCI]), and 22 healthy controls (HC) were assessed at baseline and after three years. SC was assessed for facial emotion recognition (FER), affective and cognitive Theory of Mind (ToM), and social behavior. A comprehensive neuropsychological battery provided domain-specific z-scores. Cognitive classification followed MDS Level II criteria. Adjusted linear mixed models examined SC changes. Delta scores for SC tasks and z-score changes were correlated. Results At baseline, PD-MCI patients scored lower on cognitive ToM than PD-CN and HC, with no significant group differences in affective ToM, FER, or social behavior. Over three years, PD-MCI patients experienced a significant decline in cognitive ToM compared to PD-CN and HC, while affective ToM and emotion recognition declined only relative to HC. The converters (n = 16) to a worse cognitive state (PD-CN to PD-MCI or PD-MCI to PDD) showed lower baseline cognitive ToM and steeper decline than stable patients. All SC changes correlated with visuospatial ability; affective ToM also correlated with memory, language and attention, and FER with memory and executive function. Conclusions Cognitive ToM declines in parallel with cognitive deterioration in PD, while remaining stable in PD-CN. SC measures may help identify patients at higher risk of cognitive decline.

Spinocerebellar ataxia type 32 (SCAR32) is a rare autosomal neurodegenerative disorder caused by mutations in the peroxiredoxin 3 (PRDX3) gene, which encodes a mitochondria-specific antioxidant enzyme critical for maintaining cellular redox homeostasis. Here, we reported a case of a homozygous nonsense mutation (c.619C > T; p.Arg207*) in PRDX3 of a 12-year-old Chinese boy. This mutation was predicted to result in premature termination of protein translation. Consequently, the patient presented with slowly progressive gait ataxia and cerebellar vermis atrophy. Mild cognitive impairment was also observed, with deficits in perceptual reasoning and processing speed. Additionally, he showed increased thyroid autoantibodies and thyroid enlargement. This case broadens the phenotypic spectrum of PRDX3-related disease, highlights its genetic and clinical heterogeneity, and reinforces the importance of early genetic testing in paediatric ataxia.

Alzheimer’s disease is associated with lower circumpapillary retinal nerve fibre layer thickness (cpRNFLT). It remains unclear if dementia risk states, i.e. mild cognitive impairment (MCI) and mild neurocognitive disorder (NCD) might associate with cpRNFLT and whether specific domains of cognitive function are related. The present study compared systematically all cognitive domains as defined in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) with pointwise analyses of the cpRNFLT and whether cpRNFLT variation can predict MCI and mild NCD. Spectral domain optical coherence tomography scans (768 A-scans of cpRNFLT) were analysed from 1300 participants with reliable measurements, without eye diseases, and further exclusion due to brain disorders. The study was conducted in the framework of the population-based Leipzig Research Centre for Civilization Diseases-(LIFE)-Adult study. The six DSM-5 domains were operationalized by means of both (sub-)scales of the ‘Consortium to Establish a Registry for Alzheimer Disease’ (CERAD-Plus) neuropsychological test battery and the ‘Reading the Mind in the Eyes’ test. Age, sex, education and scanning radius were used as additional regressors to adjust for demographics and eye anatomy. 2133 eyes of 1300 subjects were selected (age range 60–79 years). After adjustment for multiple comparisons, in the domain ‘attention’, worse performance was related to significantly thinner cpRNFL, especially in male participants, most pronounced for temporal and nasal-superior locations. For the domain ‘executive function’ significantly thicker cpRNFL was found nasally in female participants. There were no significant (P < 0.05) cpRNFLT locations for the DSM-5 domains ‘learning/memory’, ‘perceptual-motor abilities’, ‘language’ and ‘social cognition’. Subjects with MCI had thinner cpRNFL temporal-superior compared to subjects with normal cognition. Furthermore, alterations of cpRNFLT in MCI and mild NCD, and subgroups amnestic MCI and amnestic mild NCD existed, for the latter mainly in temporal regions. Compared to cognitively unimpaired, analyses revealed hippocampal volume decreases in MCI and mild NCD groups, and comparable white matter lesion volume, compatible with Alzheimer aetiology. cpRNFL fibre thinning was most prominently associated with lower performance in the attention domain. Highly location specific thinning involved predominantly retinal locations superior and temporal to the optic disc. Thinning in temporal-superior segment was associated with MCI. Temporal thinning indicated amnestic MCI and amnestic mild NCD. Executive function, MCI, and mild NCD presented a concordantly negative association of cognition and RNFLT nasally. As cpRNFLT is obtained conveniently within seconds, our results might assist clinicians by earlier identification of patients at risk for developing cognitive decline associated with diseases like Alzheimer’s disease.

BackgroundAccurate, short, cognitive screening instruments are important for identifying mild cognitive impairment (MCI) and dementia including Alzheimer's disease. The Quick Mild Cognitive Impairment (Qmci) screen, given its brevity, may be useful in this setting but it has not been validated in Spanish.ObjectiveTo compare the diagnostic accuracy of the Spanish version (Qmci-S) to the Montreal Cognitive Assessment (MoCA).MethodsPatients aged ≥55 years were recruited from six Primary Care Teams in Barcelona, Spain, between 2018-2019. Dementia, MCI, and normal cognition (NC) were classified following neuropsychological testing, independent of the Qmci-S and MoCA scores. Diagnostic accuracy was determined by the area under receiver operating characteristic curves (AUC), adjusted for age and education.ResultsIn total, 337 patients were included, mean 77.9 years, standard deviation (SD) ± 6.9. Most were female (57.3%). The intraclass correlation coefficient for the Qmci-S was excellent (0.98). The Qmci-S had good to excellent diagnostic accuracy for separating NC from MCI and dementia (AUC 0.91) and was statistically greater than the MoCA (AUC 0.86), p = 0.029. Accuracy was similar for differentiating NC from MCI and MCI from dementia. The Qmci-S had a shorter administer time, mean 8.6 (±3.2) versus 12.75 (SD ±5.5) minutes, respectively, p < 0.001.ConclusionsThe newly developed Qmci-S screen showed excellent criterion, construct and concurrent validity versus the widely used MoCA and had statistically similar, good to excellent, diagnostic accuracy for cognitive impairment. Its significantly shorter administration time suggests it may be the better screen in a Spanish speaking population in primary care, though further research is required.

This study aimed to update normative data and establish cut-off scores for a fruit-based semantic verbal fluency (SVF) task among older Taiwanese adults as a method for detecting mild cognitive impairment (MCI). The task was chosen due to its familiarity and cultural neutrality for Mandarin-speaking populations. SVF performance was evaluated in 245 healthy control participants and 360 individuals diagnosed with MCI. The influence of demographic variables was examined, and regression-based correction formulas were developed. Receiver operating characteristic (ROC) analyses determined optimal cut-off values according to established clinical classifications of MCI. Age, education, and sex significantly influenced SVF performance. A demographically corrected 15th percentile threshold of 10 words was proposed for community screening. An optimal ROC-derived cut-off of 11.5 words yielded an AUC of .716 (95% CI: .68-.76), with sensitivity of 57.8% and specificity of 73.9%. SVF scores were significantly correlated with global cognition, memory, and processing speed. The fruit-based SVF task is a quick, culturally relevant tool for detecting early cognitive impairment. Revised norms and cut-off scores can improve MCI identification in Mandarin-speaking seniors.

Conventional methods of functional assessment include subjective self/informant- report, which may be biased by personal characteristics, cognitive abilities, and lack of standardization (e.g., influenced by idiosyncratic task demands). Traditional performance-based assessments offer some advantages over self/informant reports but are time consuming to administer and score. To evaluate the validity and reliability of the Virtual Kitchen Challenge -Version 2 (VKC-2), an objective, standardized, and highly efficient alternative to current functional assessments for older adults across the spectrum of cognitive aging, from preclinical to mild dementia. 236 community-dwelling diverse older adults completed a comprehensive neuropsychological evaluation to classify their cognitive abilities as healthy, mild cognitive impairment or mild dementia, after adjustment of demographic variables (age, education, sex, estimated IQ). Participants completed two everyday tasks (breakfast, lunch) in a virtual kitchen (VKC-2) using a touch-screen interface to select objects and sequence steps. Automated scoring reflected completion time and performance efficiency (e.g., number of screen interactions, % time spent off screen, interactions with distractor objects). Participants also completed the VKC-2 tasks using real objects (Real Kitchen). All participants and informants for 219 participants completed questionnaires regarding everyday function. A subsample of participants (n = 143) performed the VKC-2 again in a second session 4-6 weeks after the baseline for retest analyses. Analyses evaluated construct and convergent validity and retest and internal reliability of VKC-2 automated scores. A principal component analysis showed that the primary VKC-2 automated scores captured a single dimension and could be combined into a composite score reflecting task efficiency. Construct validity was supported by ANCOVA results showing participants with healthy cognition obtained significantly better VKC-2 scores than participants with cognitive impairment (all Ps < .001), even after controlling for demographics and general computer visuomotor dexterity. Convergent validity was supported by significant correlations between VKC-2 scores and performance on the Real Kitchen (r values = -.58 to .64, Ps < .001), conventional cognitive test scores (r values = -.50 to -.22, Ps < .001), and self and informant questionnaires evaluating everyday function (r values = .25 to .43, Ps < .001). Intraclass correlations (ICC) indicated moderate to excellent retest reliability (ICC =.70 - .90) for VKC-2 scores after 4-6 weeks. Reliability improved in analyses including only participants who reported no change in cognitive status between time 1 and time 2 (n=123). Spearman-Brown correlations showed acceptable to good internal consistency between the VKC-2 tasks (breakfast, lunch) for all scores (.77 to .84) supporting the use of total scores. The VKC-2 is an efficient, valid, and sensitive measure of everyday function for diverse older adults and holds promise to improve the status quo of functional assessment in aging particularly when informants are unavailable or unreliable.

Kinematic handwriting impairments in olfactory dysfunction-related post-acute covid syndrome: short and long-term neurophysiological considerations.

Beyond brain scans: verbal memory testing as an efficient cognitive biomarker for preclinical Alzheimer's disease.

Nerve growth factor (NGF) is one of several neurotrophic proteins necessary for normal development and function of the mammalian nervous system. Nerve growth factor is necessary for normal brain cholinergic function, and reduced brain cholinergic activity is a hallmark pathological feature of human Alzheimer's disease (AD). In both aging humans and transgenic rodent models, disruption of the normal NGF metabolic pathway (NGF dysmetabolism) leads to brain neuronal damage, loss of synaptic plasticity, and cognitive decline. Nerve growth factor dysmetabolism in AD patients is a gradual process, beginning years prior to the development of mild cognitive impairment. In addition to changes in the levels of specific molecular regulators of the NGF pathway, there are changes in the proportions of the 2 major receptors for NGF and its precursor (proNGF) in the brain: the tropomyosin kinase A (TrkA) receptor and the p75 neurotrophin (p75NTR) receptor. Nerve growth factor has high affinity for TrkA receptors, the stimulation of which has neuroprotective effects. The precursor of NGF has higher affinity than NGF for p75NTR receptors; stimulation of p75NTR receptors by proNGF has deleterious effects on neurons. With NGF dysmetabolism, the respective ratios of available NGF/proNGF and TrkA/p75NTR receptors are decreased, favoring neuronal damage. In rodent models genetically engineered to produce monoclonal antibodies against NGF, neuronal damage and cognitive decline occur, even when the antibodies are targeted specifically against peripheral (ie, not CNS) NGF. Because canine cognitive dysfunction is a naturally occurring model of human AD, NGF dysmetabolism may be relevant to aging dogs. This article will review details of NGF dysmetabolism and how this aberrant pathway contributes to cognitive decline.

Background Early detection of mild cognitive impairment (MCI) due to Alzheimer’s disease (AD) is essential for the timely initiation of anti-amyloid β (Aβ) therapy. In clinical practice, cerebrospinal fluid (CSF) testing and amyloid positron emission tomography (PET) are not feasible for all patients; therefore, initial screening sometimes relies on imaging-based neurodegeneration (N) markers within the amyloid-tau-neurodegeneration AT(N) framework. However, the diagnostic accuracy of imaging-based (N) markers remains uncertain in real-world MCI cohorts being evaluated for anti-Aβ antibody therapy. We aimed to assess the utility of imaging-based (N) markers in predicting AD pathology in patients with MCI who may be eligible for lecanemab. Methods Thirty-six patients with MCI who were potentially eligible for lecanemab underwent CSF biomarker testing and were subsequently classified as MCI unlikely due to AD (MCI non-AD; n = 14) or MCI due to AD (MCI-AD; n = 22). Demographics, general risk factors, neuropsychological test scores, and imaging-based (N) markers—medial temporal atrophy (MTA) on magnetic resonance imaging (MRI) and regional cerebral blood flow (CBF) reductions on single-photon emission computed tomography (SPECT)—were compared between groups. Results MCI-AD was diagnosed in 22 patients (61%). Demographics, neuropsychological test scores, and most general risk factors did not differ between groups, except for diabetes, which was more frequent in the MCI non-AD group. Overall, 34 of 36 patients (94%) were classified as (N) + based on MRI or SPECT (91% MCI-AD; 100% MCI non-AD). There were no significant differences in mean MTA scores or the degree of CBF reduction between groups. In contrast, the proportion of MRI (N) + patients was significantly higher in the MCI non-AD group than in the MCI-AD group, and two patients with MCI-AD were (N) − on both MRI and SPECT. Conclusion Relying on imaging-based (N) markers to select MCI patients who may be eligible for lecanemab prior to CSF biomarker testing may lead to inefficient diagnostic pathways and may fail to identify patients who could benefit from anti-Aβ therapy.

BackgroundSpatial navigation impairment is prevalent in people with Alzheimer disease (AD) and may appear in its initial clinical stage. Detecting this deficit in people at risk may not only help prevent them from getting lost or going missing but also provide a useful clinical aid to accurate diagnosis. Traditional assessments for spatial navigation impairment include questionnaires, paper-and-pencil and maze tests, or video games. While a real-world setting is more valid, direct, and accurate, it is plagued by unpredictable conditions such as weather, obstacles, or accidents. Owing to modern technology, virtual reality (VR) offers a new way to test spatial navigation impairment.ObjectiveThe aims of this study were to test the feasibility of a VR setting to assess sense of location in people with mild cognitive impairment (MCI) and the power of VR to discriminate among groups with different clinical conditions.MethodsWe used the Pai-Jan virtual reality (PJVR) device to test spatial navigation ability in those who were cognitively unimpaired (CU) and those who experienced subjective cognitive decline (SCD) and MCI. The PJVR device is the VR version (VIVE Pro Eye head-mounted display) of the Pai-Jan device, which has demonstrated its power to discriminate among CU, AD MCI, and mild AD dementia. With a map provided and using joysticks or handles, participants were asked to reach 5 points on a 660-m path. Linear deviation (LD; in meters) from each target point and vector deviation (in degrees) from the direction to the start point at each location were treated as the variables for comparison.ResultsA total of 113 participants provided informed consent to initiate the study. Of these 113 participants, 93 (82.3%) completed the trials, including 22 (24%) who were CU, 39 (42%) with SCD, and 32 (34%) with MCI. In total, 17.7% (20/113) failed the trials due to cybersickness. The mean LD of the CU, SCD, and MCI groups was 38.2 (SD 39.5), 50.4 (SD 40.7), and 100.4 (SD 46.2) meters, respectively (P<.001). The MCI group showed greater vector deviation (mean 63.2, SD 42.4 degrees) than either the SCD (mean 39.4, SD 33.0 degrees) or CU (mean 38.6, SD 37.4 degrees; P=.02) group. The LD of the PJVR device was correlated with the total scores on the caregiver version of the Questionnaire on Everyday Navigational Ability (P<.001), indicating good ecological validity.ConclusionsThe PJVR device is feasible for older adults and participants with MCI. It can detect spatial navigation deficits related to AD pathology, and the results show a close correlation with real-world navigation ability.

Early and accurate diagnosis of Alzheimer’s disease is critical for effective clinical intervention, particularly in distinguishing it from mild cognitive impairment, a prodromal stage marked by subtle structural changes. In this study, we propose a hybrid deep learning ensemble framework for Alzheimer’s disease classification using structural magnetic resonance imaging. Gray and white matter slices are used as inputs to three pretrained convolutional neural networks: ResNet50, NASNet, and MobileNet, each fine-tuned through an end-to-end process. To further enhance performance, we incorporate a stacked ensemble learning strategy with a meta-learner and weighted averaging to optimally combine the base models. Evaluated on the Alzheimer’s Disease Neuroimaging Initiative dataset, the proposed method achieves state-of-the-art accuracy of 99.21% for Alzheimer’s disease vs. mild cognitive impairment and 91.02% for mild cognitive impairment vs. normal controls, outperforming conventional transfer learning and baseline ensemble methods. To improve interpretability in image-based diagnostics, we integrate Explainable AI techniques by Gradient-weighted Class Activation Mapping, which generates heatmaps and attribution maps that highlight critical regions in gray and white matter slices, revealing structural biomarkers that influence model decisions. These results highlight the framework’s potential for robust and scalable clinical decision support in neurodegenerative disease diagnostics.

BackgroundThere is a growing interest in dementia prevention and scalable cognitive enhancement strategies for individuals at-risk, with or without Alzheimer's disease. Board games have shown potential cognitive and mood benefits, but randomized controlled evidence remains limited and heterogeneous.ObjectiveWe aimed at assessing whether chess and/or Go could improve cognition, mood, and quality of life in individuals with mild cognitive impairment (MCI) and subjective cognitive decline (SCD).MethodsIndividuals with MCI or SCD aged ≥55 years were randomized to one of three arms: chess, Go (each consisting of 12 weekly group sessions), or a waitlist control group. Montreal Cognitive Assessment, digit span, trail making test, categorical fluency, Geriatric Depression Scale, and the World Health Organization Quality of Life scale were administered at baseline and follow-up. We also updated our previously published meta-analysis including these new results.Results69 subjects completed the study. Categorical fluency improved significantly in the games groups (p < 0.05). No between-group differences were found in overall cognition. A significant group × diagnosis × time interaction showed improved quality of life in MCI participants in the games groups (p = 0.002). A group × gender × time interaction revealed reduced depression in females in the games groups (p = 0.013). The updated meta-analysis confirmed a significant effect on depression (standardized mean differences -0.48, p = 0.013), but not on cognition.ConclusionsThe improvements in mood and quality of life, particularly among females and MCI subjects, underscore the psychological value of board games interventions, possibly through their social component. These activities may foster emotional well-being in older adults at risk for Alzheimer's disease, even without cognitive benefits.Clinicaltrials.gov Identifier: NCT06281652.

ObjectiveNeuropsychiatric disorders are characterized by high complexity and comorbidity, imposing a substantial burden on both patients and society. However, their elusive pathogenic mechanisms impede accurate clinical diagnosis and effective interventions. To overcome this challenge, the present study proposes a novel framework to quantify and characterize these disorders.MethodsRoutine electroencephalogram (EEG) recordings are acquired from 236 subjects, including patients with Alzheimer’s disease (AD), mild cognitive impairment (MCI), major depressive disorder (MDD), schizophrenia, and healthy controls (HCs). Time-varying functional brain networks are constructed by phase locking value (PLV) analysis on band-pass filtered EEG signals. Subsequently, the nodal behavior characteristics within these dynamic brain networks are quantified by integrating robust dynamic community detection algorithms and network reconfiguration metrics.ResultsSignificant intergroup differences in network reconfiguration metrics are identified based on the dynamic community structures (FDR-corrected p < 0.001). Lower cohesion strength is observed across all neuropsychiatric disorders compared to healthy controls, consistent across all frequency bands and recording sites. When six machine learning classifiers are trained on these metrics, the maximum classification accuracies exceeded 80%. Since lower cohesion strength is a prominent potential biomarker for neuropsychiatric disorders, it was then selected as the independent input feature for random forest classifier, and the classification accuracy achieved 0.85 for schizophrenia group, 0.88 for both the MCI and MDD group, and 0.82 for the AD group.ConclusionsOur findings indicate that the framework based on dynamic network reconfiguration metrics effectively captures both the shared and disorder-specific alterations in brain network dynamics among neuropsychiatric disorders.SignificanceDynamic community structure advances our understanding of the pathological mechanisms underlying neuropsychiatric disorders. This study provides novel insights that may inform the development of more targeted and effective therapeutic strategies.

Background:The Rowland Universal Dementia Assessment Scale (RUDAS) is a validated cognitive screening tool for illiterate and low-educated individuals, adaptable across languages and cultures. In Peru, we adapted it for Quechua speakers (Q-RUDAS) to assess cognitive status in older adults. Objective: We aimed to estimate the prevalence of neurocognitive disorders—mild cognitive impairment (MCI) and dementia—among Quechua-speaking older adults in one of the most socially vulnerable districts of Peru using the Quechua version of the Rowland Universal Dementia Assessment Scale (Q-RUDAS), a brief cognitive screening tool validated in Peru. Methods: We studied 511 participants from Puno a region in the southern Peruvian Andes (mean age 65.04 ± 6.73 years; 80.4% females), collecting sociodemographic data and Q-RUDAS scores. After excluding 18 individuals with medical conditions that could affect cognitive performance, such as neurological, psychiatric, or cerebrovascular disorders, 493 completed the test. Results: All Q-RUDAS items were well understood, although over 50% of participants struggled with visuospatial construction. The mean Q-RUDAS score was 26.01 ± 2.71. Of the participants, 446 (90.5%) scored within normal ranges (26.67 ± 1.92), 41 (8.3%) were classified as having mild cognitive impairment (MCI) (21.49 ± 1.92), and 6 (1.2%) as having dementia (17.00 ± 2.71) based on established Q-RUDAS cut-offs. Urban participants scored higher. The prevalence of MCI and dementia combined was 9.52%. Conclusions: The Q-RUDAS is a culturally sensitive tool that can support the identification of cognitive impairment in Indigenous populations. Our findings highlight the need for further cross-validation studies to refine diagnostic accuracy in Quechua-speaking populations.