OBJECTIVE:To evaluate the action selection, production, and execution of familiar tool use in patients with cognitive impairment, and to report the prevalence of Familiar Tool-Use Impairment (FTUI) and its association with cognitive impairment and functional disability. DESIGN:Cross-sectional, observational study. SETTING:The outpatient memory clinic in a tertiary care hospital. PARTICIPANTS:A total of 66 patients with mild cognitive impairment and 80 patients with dementia were consecutively recruited, along with 85 healthy controls. INTERVENTION:Not applicable. MAIN OUTCOME MEASURES:Familiar tool test (FTT) and their association with multiple cognitive domains and basic activity of daily living in patients with cognitive impairment. RESULTS:Early signs of FTUI were observed in the mild cognitive impairment stage, with an FTUI rate of 6.0%. The prevalence of FTUI increased significantly from the healthy controls to the dementia group to approximately 65.0% (P<.001). Canonical correlation analysis (r=0.529, P=.006) indicated that semantic and visuospatial impairment contributed substantially to the canonical variables and showed a prominent association with the Selection Scale in FTT deficits. Multiple linear regression analyses revealed that tool selection was associated with the degree of bathing dependency (R²=0.471; 95% CI, -0.49 to -0.13) and dressing (R²=0.429; 95% CI, -0.35 to -0.05). CONCLUSIONS:This study characterized the prevalence of FTUI in patients with cognitive impairment and its underlying cognitive factors, based on the FTT. The findings provided a new perspective for understanding the cognitive impairment and functional disability, suggesting that FTUI may serve as an early warning indicator of functional decline, and offer a practical clinical tool for individualized cognitive rehabilitation in apraxia.

BACKGROUND:Late-life depression (LLD) is the major risk factor for elderly suicide, and suicidal ideation (SI) is a crucial stage for prevention. However, LLD are less likely openly express SI. Gamma oscillations, closely linked to cognition and mental processes, may contribute to the pathophysiology of LLD and suicidal behavior through their dysregulation within large-scale brain networks. The aim of our research was to investigate the cortical functional networks in the gamma band to better understand the neurobiological mechanisms underlying SI in LLD. METHODS:Electroencephalography (EEG) was recorded from 30 LLD with SI (LLD-SI), 32 LLD without SI (LLD-NSI), and 34 normal controls. We applied source-level graph theory based on functional connectivity in gamma band and utilized machine learning to differentiate between LLD-SI and LLD-NSI groups using network features. RESULTS:Significant diminished gamma functional connectivity, particularly involving the orbitofrontal cortex, was observed in both subtypes of the LLD group. In graph theory analysis, LLD-SI showed decreased average clustering coefficient (p < 0.001) and characteristic path length (p = 0.021), along with increased global efficiency (p = 0.015) compared to LLD-NSI. Compared to NC, LLD-SI also demonstrated reduced average clustering coefficient (p = 0.004), characteristic path length (p = 0.004), and higher global efficiency (p = 0.004). We also found several nodal metrics, which suggested potential hubs related to SI. The graph theorical method effectively distinguished SI in LLD, with an accuracy of 69.35%, sensitivity of 73.33%, and specificity of 65.63% based on gamma-band network features. LIMITATIONS:The sample sizes are relatively small. Higher-density EEG systems and interventional study designs should be included in future research. Future studies should incorporate external validation datasets to confirm the clinical utility of the proposed classification framework. CONCLUSION:Our research provides valuable insights into the brain connectome in gamma band of SI in LLD. Gamma-band network indices may serve as potential biomarkers for detecting SI and offer frequency-specific targets for neuromodulation in suicide prevention and treatment strategies for LLD patients.

The interplay between glymphatic function and sleep quality is crucial for brain health and cognitive longevity in late adulthood. Beyond chronological age, whether brain age has specific effects on the associations between glymphatic function, sleep quality, and cognition are understudied in cognitive unimpaired adults. Structural and diffusion magnetic resonance imaging (MRI) data from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) project (N = 582, age range 18–87 years) were used to calculate brain age metrics and the diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index. Brain age metrics comprised estimated brain age and the brain predicted age difference (brain-PAD). Subjective sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). Cognitive assessments included accuracy, reaction time, intraindividual variability of reaction time, and fluid intelligence. Leftward asymmetry of the DTI-ALPS index was consistently observed across brain age-specific groups. The brain-PAD score was significantly correlated with a lower left DTI-ALPS index. Adults with a positive brain-PAD score exhibited a robust correlation between DTI-ALPS indices and sleep quality features, whereas those with a negative brain-PAD score showed a reliable correlation between DTI-ALPS indices and cognition. Mediation analyses further revealed that the relationship between left DTI-ALPS index and sleep efficiency was mediated by brain age. This study provides the first demonstration that lateral differences in the DTI-ALPS index vary according to brain ageing statuses. The two distinct profiles of the sleep–glymphatic function–cognition connections observed in relation to brain-PAD scores suggest that a preserved brain age may serve as a protective factor against age-related decline in glymphatic function. These findings may underscore the translational potential of brain age models as both clinical biomarkers and modifiable targets for interventions aimed at promoting healthy longevity and brain resilience.

Background:Late-life depression (LLD) and odor identification (OI) dysfunction are risk factors for dementia, but the underlying neural mechanisms remain unclear. This study investigated dynamic functional connectivity (dFC) in olfactory brain regions of LLD patients with and without OI dysfunction and examined how dFC moderates the OI-cognition link. Methods:Resting-state functional magnetic resonance imaging data were acquired from 51 LLD patients with OI deficits (LLD-OID), 59 LLD patients without deficits (LLD-noOID), and 51 healthy controls (HC). A sliding-window approach (50 TR width, 1 TR step) was used to estimate dFC variability between the orbitofrontal cortex (OFC) and the whole brain. Bayesian regression and moderation analyses assessed associations among OFC dFC, OI scores, and cognitive measures. Results were robust across window sizes. Results:Compared to LLD-noOID and HC, LLD-OID showed decreased OFC-left inferior frontal gyrus dFC variability (P < 0.01) and increased OFC-right middle frontal gyrus (MFG) variability (P < 0.001). Higher OFC-MFG variability was associated with worse OI and cognitive performance and significantly moderated the OI-global cognition relationship (β = 1.06, P = 0.027, 95% CI [0.12, 2.0]). No group differences were found in primary olfactory regions. Conclusion:LLD patients with OI dysfunction exhibited more disrupted dFC in secondary olfactory regions compared with those without OI dysfunction. Dynamic OFC-MFG disconnectivity may index vulnerability to cognitive decline and dementia risk in LLD patients.

Alzheimer's disease (AD) diagnosis remains challenging because current molecular biomarkers, though sensitive, lack spatial specificity. Radiomics offers a promising alternative by quantifying subtle microstructural alterations from routine MRI that are invisible to traditional volumetric analyses. Given that olfactory-related regions are preferentially affected in AD with olfactory dysfunction preceding cognitive decline, this study aimed to develop and validate radiomics signatures from these regions as imaging biomarkers linking molecular pathology to clinical phenotypes. Radiomics models were developed and validated across 834 participants from three independent cohorts: in-house cohort of 278 participants (122 AD, 156 cognitively unimpaired controls) ADNI and OASIS validation (each with n = 278). A total of 1,502 radiomics features were extracted from each bilateral olfactory-related brain region using structural MRI, with optimal features selected through correlation analysis, recursive feature elimination, and LASSO regression. Six machine learning algorithms were systematically trained and validated, with associations between radiomics signatures, plasma pTau217, olfactory identification, and cognitive performance examined through partial correlation and mediation analyses. Direct comparisons with volumetric models and hierarchical regression analyses were performed to quantify the added value of radiomics. The hippocampus-amygdala radiomics signatures achieved superior diagnostic performance across all six algorithms and three cohorts (AUC: 0.86–0.92, accuracy: 0.81–0.88). These signatures significantly outperformed those from piriform cortex, entorhinal cortex, and orbitofrontal cortex. Radiomics models demonstrated marginally higher AUC values and superior sensitivity (0.776–0.869 vs 0.505–0.811) compared to volumetric models. Hierarchical regression analyses revealed that radiomics features showed significant incremental contributions in 75.7

Late-life depression (LLD) is associated with cognitive impairment, olfactory dysfunction and dementia risk, while brain iron accumulation has been linked to depressive symptoms, cognitive impairment and Alzheimer’s disease progression. We examined whether regional brain iron accumulation is altered in patients with LLD and how it relates to cognitive, olfactory and olfactory-task fMRI measures. Among 38 patients with LLD and 42 healthy controls, the LLD group showed greater iron accumulation in the bilateral amygdala and entorhinal cortex, alongside broad cognitive impairment and poorer olfactory performance. Greater regional iron accumulation was related to worse cognitive performance, lower olfactory scores and higher olfactory-task activation, although most associations did not survive false-discovery-rate correction. Exploratory analyses suggested that odor discrimination may be involved in the iron-cognition relationship. These findings suggest that regional iron accumulation in the amygdala and entorhinal cortex may represent a candidate imaging feature of cognitive and olfactory vulnerability in LLD.

BACKGROUND The role of cardiovascular markers in Alzheimer's disease (AD) pathology is incompletely understood. We investigated whether serum α-hydroxybutyrate dehydrogenase (α-HBDH) is associated with amyloid and tau pathology and influences cognition in AD. METHODS In 245 participants categorized by amyloid-PET status, blood levels of α-HBDH and AD biomarkers (p-tau217, p-tau181, Aβ42/40) were measured. Cognitive function was assessed across multiple domains. RESULTS Higher α-HBDH correlated with greater amyloid positivity. Moreover, α-HBDH levels negatively correlate with cognitive performance in the Aβ − group. α-HBDH levels positively correlate with tau pathology and amyloid deposition in all participants, and specifically with p-tau181 in the Aβ + group. Notably, α-HBDH interacts with p-tau217 to exacerbate cognitive decline in all participants and the Aβ + group. DISCUSSION α-HBDH is linked to both amyloid and tau pathology and interacts with p-tau217 to worsen cognition, highlighting its potential as a cardiovascular modulator in AD and supporting multi-target therapeutic strategies.

Background: Odor identification (OI) impairment in mild cognitive impairment (MCI) elevates the risk for Alzheimer's disease (AD). The present study was designed to clarify the underlying neural mechanisms by investigating brain network aberrations in MCI patients with OI impairment using dynamic resting-state functional magnetic resonance imaging (rs-fMRI). Objective: This study aimed to delineate the profile of dynamic intrinsic brain activity in MCI patients with and without OI impairment. It further aimed to establish the clinical relevance of these dynamic neural signatures by linking them to cognitive and olfactory function. Methods: In 194 participants (97 MCI and 97 healthy controls [HC]), we analyzed dynamic metrics including dynamic fractional Amplitude of Low-Frequency Fluctuations (dfALFF), dynamic Amplitude of Low-Frequency Fluctuations (dALFF), dynamic Degree Centrality (dDC), and dynamic Regional Homogeneity (dReHo), and their correlation with cognitive performance and OI. Results: The MCI with OI impairment (MCI-OII) group (n = 22) performed worse across all cognitive domains than both HC and the MCI without OI impairment (MCI-NOII) group (n = 75, p < 0.001). These patients exhibited elevated dALFF, dfALFF, dDC, and dReHo variability in regions including the fusiform gyrus, insula, precuneus, and cingulate cortex (p < 0.001). These dynamic metrics correlated with olfactory and cognitive scores (p < 0.05). Additionally, dReHo in the right precuneus partially mediated the relationship between olfactory function and delayed recall memory. Conclusions: This study demonstrates that MCI patients with OI impairment exhibit widespread disruptions in dynamic brain activity. These alterations correlate with clinical deficits, and precuneus dReHo may partially link olfactory and cognitive decline in MCI.

Mild cognitive impairment (MCI) is widely recognized as an early stage of dementia. Epidemiological studies suggest that MCI is more prevalent in females than in males. Notably, there are sex differences in MCI-related brain changes. Resting-state functional magnetic resonance imaging (rs-fMRI) offers a valuable method for assessing brain activity during rest. This study aims to explore sex-specific regional brain activity in participants with MCI during resting states. 86 MCI participants (21 males and 65 females) and 107 normal controls (NCs) (38 males and 69 females) were included in the present study. Regional homogeneity (ReHo), degree centrality (DC), amplitude of low frequency fluctuations (ALFF), and fractional ALFF (fALFF) were used to assess brain activity. MCI females showed increased ReHo values in the right cerebellum inferior compared to NC females and MCI males. However, MCI males exhibited increased ReHo values in the left hippocampus compared to NC males and MCI females. ReHo values in the right cerebellum inferior were associated with visuospatial skills in MCI males, and language function in MCI females. Additionally, ReHo values in the left hippocampus were associated with attention function in MCI females but not in MCI males. In MCI participants, sex moderated the relationship between ReHo values in the right cerebellum inferior and cognitive function (visuospatial skills and language function), as well as the association between ReHo values in the left hippocampus and attention function. In conclusions, this study revealed sex differences in ReHo of right inferior cerebellum and left hippocampus in MCI, and the association between ReHo and cognitive impairment in MCI differs by sex. These sex-specific patterns of regional brain activity can aid in the development of sex-specific precision medicine.

Presenilin 1 (PSEN1) plays a pivotal role in early-onset Alzheimer's disease (EOAD). The clinical phenotype of EOAD is typically marked by cognitive decline, with ataxia rarely reported. We identified mutations at different positions of PSEN1 in two Chinese patients with EOAD. Interestingly, one patient carrying the PSEN1 p.P117L mutation manifested symptoms of ataxia, while another patient harboring the PSEN1 p.P264L mutation did not exhibit any signs of this disorder. Computational analyses using PolyPhen-2, SIFT, Provean, and Mutation Taster predicted both mutations as pathogenic, while structural predictions using SOPMA, TMHMM 2.0, and PyMOL. Our findings expand the spectrum of atypical PSEN1-associated phenotypes.

Background Late-life depression (LLD) often co-occurs with mild cognitive impairment (MCI), and patients with LLD and MCI (LLD-MCI) have an increased risk of progression to Alzheimer's disease (AD). However, differences in resting-state neural oscillation and cognitive impairment in LLD patients remain unclear. In this cross-sectional study, electroencephalography (EEG) was used to analyse, local rhythm activity and large-scale network communication to differentiate LLD patients with and without MCI.Methods We enrolled 113 participants: 74 with LLD (50 with LLD-MCI and 24 with LLD-non-MCI) and 39 healthy older adults (HOAs). All participants underwent comprehensive neuropsychological assessments. Spectral power and source-level functional connectivity (Phase-Locking Value, PLV) were analysed across multiple frequency bands. A machine learning framework using nested stratified cross-validation was implemented to evaluate the potential of EEG features in classifying LLD clinical subtypes.Results LLD-MCI patients exhibited a distinct dissociation in the beta band: significantly reduced spectral power in the left frontal cortex contrasted with extensive hyperconnectivity primarily centred on the right lateral orbitofrontal cortex (rLOFC). Complementary analyses also revealed widespread hyperconnectivity in the theta band in the LLD-MCI group. The Linear Discriminant Analysis (LDA) model achieved superior performance in distinguishing LLD-MCI patients from LLD-non-MCI patients, with an area under the curve (AUC) of 0.82 and an accuracy of 78.38%. Feature importance analysis revealed rLOFC-mediated beta synchronisation as the most discriminative biomarker.Conclusion Our findings suggest that beta-band oscillatory disruption-characterised by local power deficits and network hyperconnectivity-may represent a potential neurobiological signature of cognitive vulnerability in LLD patients. Whether this hyperconnectivity reflects a compensatory or pathological process remains a hypothesis for further validation. EEG metrics provide significant diagnostic value for the precise clinical subtyping and early identification of cognitive decline in the LLD population.Clinical trial number Not applicable.

BACKGROUND:Late-life depression (LLD) features recurrent episodes and frequently co-exists with cognitive impairment, which predicts worse outcomes and progression to dementia. Evidence indicates a bidirectional depression-cognition relationship, but objective biological tools to capture severity and this interplay are scarce. METHODS:This study compared 110 patients with LLD between depressive episodes and remission phases. Based on untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics analysis of plasma samples, we identified key metabolites and developed four predictive machine learning models (GLMNet, LDA, Naive Bayes, and cTree). Additionally, Spearman rank correlation analysis and mediation analysis were conducted to further investigate the relationships and mediating effects of the key metabolites. RESULTS:The diagnostic model based on key metabolites selected by the random forest algorithm showed good discriminatory performance in distinguishing LLD Episodes (AUC = 0.824). Tridecanoylcarnitine (Car(13:0)), PC(P-16:0/22:6), and SM(d18:1/22:0) were significantly downregulated during the depressive episode. Tridecanoylcarnitine (Car(13:0)) negatively correlated with depressive severity (p < 0.001) and positively with Mini-Mental State Examination scores. PC(P-16:0/22:6) was associated with both emotional and cognitive impairments. Mediation analysis supported that Tridecanoylcarnitine (Car(13:0)) and PC(P-16:0/22:6) partially mediated the depression-cognition relationship, explaining 22.0 % and 26.9 % of the variance, respectively (p < 0.05). CONCLUSION:This study reveals specific lipid metabolic dysregulation in LLD and identifies key metabolites significantly associated with both depressive severity and cognitive function. It further supports their mediating role in the comorbidity between depression and cognitive impairment. These metabolites may serve as potential targets for simultaneously regulating depression and cognition. The machine learning model developed provides a new auxiliary tool for the objective assessment of LLD.

Background Odor identification (OI) deficits are observed in both individuals with subjective cognitive decline (SCD) and mild cognitive impairment (MCI), and serve as risk factors for dementia. Compared with males, females typically demonstrate superior OI performance and different risks of dementia. However, the role of sex in the relationship between OI dysfunction and cognitive impairment remains uncertain. Methods In total, 121 subjects with SCD (41 males and 80 females), and 169 subjects with MCI (59 males and 110 females) underwent the Sniffin’ Sticks Screen 16 test and comprehensive neuropsychological examination. The relationships between olfactory and cognitive impairment were analyzed via partial correlation, multiple linear regression and moderating effects. Results In both SCD and MCI subjects, males performed better in language and females performed better in memory. The correlation between OI and cognition tended to be stronger in MCI subjects than in SCD subjects. In MCI subjects, the correlation tended to be stronger in females. For MCI females, better OI performance was correlated with higher short-term memory and attention scores. For MCI males, better OI performance was correlated with higher short-term memory scores. The OI was correlated with language in SCD males and with attention in SCD females. Sex played a moderating role in the relationship between OI dysfunction and language in MCI subjects and the relationship between OI dysfunction and short-term delayed recall memory and language in SCD subjects. Conclusion These findings revealed significant sex differences between OI dysfunction and cognitive impairment in SCD and MCI subjects. Sex differences should be considered when utilizing OI in clinical settings to predict cognitive function.

There are significant sex differences in the prevalence, symptom presentation, treatment response and brain abnormalities of patients with late-life depression (LLD). The functional connectivity of the habenula has been associated with depressive symptoms and cognitive impairments in patients with LLD. However, sex differences in habenular functional connectivity patterns among LLD patients remain unclear. One hundred and fourteen patients with LLD and 75 healthy controls (HCs) were included in the present study. Resting-state functional magnetic resonance imaging was used to analyse the static and dynamic functional connectivity (sFC and dFC) of the habenula. There were significant interactions between diagnosis (LLD vs. HCs) and sex for the dFC of the left habenula with the left insula, precentral gyrus, angular gyrus, and middle frontal gyrus and for the right habenula with the right middle temporal gyrus. Pairwise comparisons revealed a trend of HC males > HC females and LLD males < HC males for the connections between the left habenula and the left precentral gyrus, angular gyrus and middle frontal gyrus. Conversely, a trend of HC males < HC females and LLD males > HC males was found for the connections between the right habenula and right middle temporal pole. Furthermore, there was a significant interaction for the sFC of the right habenula with the right fusiform gyrus, with trends of HC males > HC females, LLD males < HC males, and LLD females > HC females. Regression analysis revealed that left habenular-left insular dFC was associated with long-delay memory in females and working memory in males; right habenular-right middle temporal pole dFC was associated with information processing speed in females. Sex moderated the relationships between cognitive function (global cognition, delay-recalled memory and working memory) and dFC between the left habenula and left insula. In conclusions, this study revealed sex-specific alterations in the functional connectivity patterns of the habenula in LLD patients, and these alterations were associated with various cognitive functions in a sex-specific manner. These findings provide a neurobiological basis for understanding sex differences in LLD patients.

INTRODUCTION: With the advancement of disease-modifying therapies for Alzheimer's disease (AD), validating plasma biomarkers against cerebrospinal fluid (CSF) and positron emission tomography (PET) standards is crucial in both research and real-world settings. METHODS: We measured plasma phosphorylated tau (p-tau)217, p-tau181, amyloid beta (A beta)1-40, A beta 1-42, and neurofilament light chain in research and real-world cohorts. Participants were categorized by brain amyloid status using US Food and Drug Administration/European Medicines Agency-approved CSF or PET methods. RESULTS: Plasma p-tau217 and p-tau217/A beta 1-42 demonstrated superior accuracy in detecting brain amyloid pathologies, with area under the curve from 0.94 to 0.97 in all cohorts. Specificity was lower in the real-world cohort but improved significantly by integrating demographic and clinical factors, aligning performance with research cohorts. Additionally, plasma biomarkers exhibited strong correlations with their CSF counterparts and PET standardized uptake value ratios, with significant associations in amyloid-positive participants. DISCUSSION: Plasma p-tau217 and p-tau217/A beta 1-42 are effective diagnostic tools. However, patient demographics, apolipoprotein E epsilon 4 status, and cognitive condition must be considered to improve specificity in the clinical practice.

BACKGROUND:Patients with late-life depression (LLD) with suicidal ideation (SI) often have more explicit suicide plans, and suicide attempts among older adults are more highly lethal than in other age groups. Increasing evidence suggests that people with SI in depression exhibit abnormal brain network connectivity; however, the relationship between suicidal ideation in LLD and brain network dynamics is still unclear. METHODS:We recruited patients with LLD and SI (LLD-SI), patients with LLD without SI (LLD-NSI), and age-matched healthy older adults. We collected 64-channel resting state electroencephalography (EEG) recordings of all participants and used microstate analysis to explore large-scale brain network dynamics. RESULTS:We included 33 patients with LLD-SI, 29 patients with LLD-NSI, and 31 controls. We observed abnormal microstate parameters in the LLD-SI group, characterized by higher duration (p = 0.04), occurrence (p = 0.009), and contribution (p = 0.001) of microstate C (reflecting activity of the salience network), compared with the LLD-NSI group, as well as higher occurrence (p = 0.03) and contribution (p = 0.009) of microstate C compared with the control group. Furthermore, transition probabilities from microstate class A to D (r = -0.466, p = 0.04) and class D to A (r = -0.506, p = 0.02) (involving coupling and sequential activation of auditory and executive control network) were negatively correlated with completion time of Stroop Colour and Word Test Part C (a neuropsychological test of executive function) in the LLD-SI group. LIMITATIONS:The sample size was relatively small, the cross-sectional nature of this study prohibited exploring the causal relationship between abnormal microstate dynamics and suicidal ideation, and we did not include medication-naive patients with first-episode LLD. CONCLUSION:The study reveals altered microstate dynamics among patients with LLD-SI, compared with patients with LLD-NSI and controls. Our findings suggest that microstate dynamics could serve as potential neurobiomarkers for identifying SI in LLD.

Background Late-onset depression (LOD) is featured by disrupted cognitive performance, which is refractory to conventional treatments and increases the risk of dementia. Aberrant functional connectivity among various brain regions has been reported in LOD, but their abnormal patterns of functional network connectivity remain unclear in LOD.Methods A total of 82 LOD and 101 healthy older adults (HOA) accepted functional magnetic resonance imaging scanning and a battery of neuropsychological tests. Static functional network connectivity (sFNC) and dynamic functional network connectivity (dFNC) were analyzed using independent component analysis, with dFNC assessed via a sliding window approach. Both sFNC and dFNC contributions were classified using a support vector machine.Results LOD exhibited decreased sFNC among the default mode network (DMN), salience network (SN), sensorimotor network (SMN), and language network (LAN), along with reduced dFNC of DMN-SN and SN-SMN. The sFNC of SMN-LAN and dFNC of DMN-SN contributed the most in differentiating LOD and HOA by support vector machine. Additionally, abnormal sFNC of DMN-SN and DMN-SMN both correlated with working memory, with DMN-SMN mediating the relationship between depression and working memory. The dFNC of SN-SMN was associated with depressive severity and multiple domains of cognition, and mediated the impact of depression on memory and semantic function.Conclusions This study displayed the abnormal connectivity among DMN, SN, and SMN that involved the relationship between depression and cognition in LOD, which might reveal mutual biomarkers between depression and cognitive decline in LOD.

Background:There are notable sex differences in the symptoms and treatment response of late-life depression (LLD); however, the underlying static and dynamic abnormalities in brain function that may drive these disparities remain unclear. This study was to investigate sex-specific aberrant brain activity in LLD. Methods:We recruited 75 LLD patients and 164 healthy controls (HCs). Static and dynamic metrics of amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), and functional connectivity (FC) were compared across four groups (LLD-female, LLD-male, HC-female, and HC-male). Correlation and moderation analyses were then used to examine whether sex moderated the associations between brain activity, cognitive impairment, and depressive symptoms. Results:First, significant interaction effects between diagnosis (LLD vs. HCs) and sex were found for ALFF in the left paracentral lobule, ReHo in the right superior temporal gyrus, and static FC (sFC) between the right superior temporal gyrus and left middle frontal gyrus. Second, in LLD-female, ReHo (right superior temporal gyrus) and sFC (right superior temporal gyrus-left middle frontal gyrus) correlated with weight, and ALFF (left paracentral lobule) correlated with visuospatial skills. Third, sex significantly moderated the relationships between ReHo (right superior temporal gyrus) and cognition, ALFF (left paracentral lobule) and depressive symptoms, and sFC (right superior temporal gyrus-left middle frontal gyrus) and depressive symptoms in the LLD group. Conclusion:Our study highlights sex differences in static brain activity related to cognitive impairment and depressive symptoms in LLD, indicating sex-specific neurobiological underpinnings for this disorder.

INTRODUCTION:The relationships between blood homocysteine (Hcy), amyloid beta (Aβ), tau pathology, and their combined effects on cortical thinning and cognitive impairment in Alzheimer's disease (AD) remain poorly understood. METHODS:Participants were stratified into Aβ+ and Aβ- groups by positron emission tomography. Blood levels of Hcy, AD biomarkers (phosphorylated tau-217 [p-tau217], p-tau181, and Aβ), cortical thickness (via magnetic resonance imaging), and cognitive performance were assessed. RESULTS:Aβ+ individuals exhibited increased blood Hcy and p-tau217 levels, which negatively correlated with temporal cortical thickness and cognitive function. A significant interaction between Hcy and p-tau217 was observed in Aβ+ participants, with high Hcy exacerbating the detrimental effects of p-tau217 on temporal cortical thinning and cognitive deficits. In Aβ- individuals, Hcy levels were independently associated with tau pathology. DISCUSSION:Increased Hcy and p-tau217 levels synergistically contribute to cortical thinning and cognitive impairment, highlighting that Hcy may be a modifiable risk factor for AD progression. Highlights Increased blood levels of homocysteine (Hcy) and phosphorylated tau-217 (p-tau217) are independently and interactively associated with temporal cortical thinning and cognitive deficits. Hcy may predominantly affect tau pathology compared to amyloid aggregation, with its impact on neurodegeneration depending on coexisting pathologies. Hcy may serve as a supplementary indicator rather than as an independent predictor in Alzheimer's disease. Hcy may serve as a modifiable risk factor, whereas p-tau217 as a superior diagnostic biomarker and potential therapeutic target.