Differential neuropsychiatric associations of plasma biomarkers in older adults with major depression and subjective cognitive decline.

Older adults with major depressive disorder (MDD) or early cognitive decline during the subjective cognitive decline (SCD) stage may exhibit neuropsychiatric symptoms such as anxiety, depression, and subtle cognitive impairment. The clinicopathological features and biological mechanisms of MDD differ from those of SCD among older adults; these conditions thus require different treatment strategies. This study enrolled 82 participants above 50 years old with normal cognitive levels from the communities to examine biomarker-behavior correlations between MDD (n = 23) and SCD (n = 23) relative to a normal control (NC) group (n = 36). Multidomain assessments were performed for all participants, including immunomagnetic reduction tests to detect plasma beta-amyloid (Aß), total tau (Tau), phosphorylated tau-181 (p-Tau181), neurofilament light chain, and glial fibrillary acidic protein (GFAP). This study observed that depressive symptoms in MDD were associated with amyloid pathology (plasma Aß40 vs. HADS-D: R = 0.45, p = 0.031; Aß42/Aß40 vs. HADS-D: R = -0.47, p = 0.024), which was not observed in the NC (group difference p < 0.05). Moreover, cognitive decline in MDD was distinguished by a mixed neurodegenerative process involving amyloid (plasma Aß42 vs. facial memory test: R = 0.48, p = 0.025), tau (Tau/Aß42 vs. digit symbol substitution test (DSST): R = -0.53, p = 0.01), and astrocytic injury (plasma GFAP vs. Montreal cognitive assessment score: R = -0.44, p = 0.038; plasma GFAP vs. DSST: R = -0.52, p = 0.014), findings that did not apply to the NC (group difference p < 0.05). Moreover, this study revealed different biomarker-behavior correlations between individuals with SCD and the NC. Compared with the NC, cognitive decline in the SCD group might be unrelated to amyloid pathology and instead might be early manifestations of tau pathology. This study underscores the difference in clinicopathological features between MDD and SCD among older adults, which differ from those of the NC. These findings enhance our understanding of the mechanisms underlying MDD and SCD in older individuals.

Dissociation of focal and large-scale inhibitory functions in the older adults: A multimodal MRI study.

BACKGROUND: The decline of inhibitory in cognitive aging is linked to reduced cognitive and mental capacities in older adults. However, this decline often shows inconsistent clinical presentations, suggesting varied impacts on different inhibition-related tasks. Inhibitory control, a multifaceted construct, involves various types of inhibition. Understanding these components is crucial for comprehending how aging affects inhibitory functions. Our research investigates the influences of aging on large-scale and focal-scale inhibitory and examines the relationship with brain markers. METHODS: We examined the impact of aging on inhibitory in 18 younger (20-35 years) and 17 older adults (65-85 years) using focal and large-scale inhibition tasks. The Gabor task assessed focal-scale inhibition, while the Stop Signal Task (SST) evaluated large-scale inhibition. Participants underwent neuropsychological assessments and MRI scans, including magnetic resonance spectroscopy (MRS) and structural and resting fMRI. RESULTS: Older adults exhibited a marked decline in inhibitory function, with slower SST responses indicating compromised large-scale inhibition. Conversely, the Gabor task showed no significant age-related changes. MRS findings revealed decreased levels of GABA, glutamate, glutamine, and NAA in the pre-SMA, correlating with observed large-scale inhibition in older adults. Additionally, pre-SMA seed-based functional connectivity analysis showed reduced brain network connections in older adults, potentially contributing to inhibitory control deficits. CONCLUSIONS: Our study elucidates the differential effects of aging on inhibitory functions. While large-scale inhibition is more vulnerable to aging, focal-scale inhibition is relatively preserved. These findings highlight the importance of targeted cognitive interventions and underscore the necessity of a multifaceted approach in aging research.

Neurostructural subgroup in 4291 individuals with schizophrenia identified using the subtype and stage inference algorithm.

Machine learning can be used to define subtypes of psychiatric conditions based on shared biological foundations of mental disorders. Here we analyzed cross-sectional brain images from 4,222 individuals with schizophrenia and 7038 healthy subjects pooled across 41 international cohorts from the ENIGMA, non-ENIGMA cohorts and public datasets. Using the Subtype and Stage Inference (SuStaIn) algorithm, we identify two distinct neurostructural subgroups by mapping the spatial and temporal 'trajectory' of gray matter change in schizophrenia. Subgroup 1 was characterized by an early cortical-predominant loss with enlarged striatum, whereas subgroup 2 displayed an early subcortical-predominant loss in the hippocampus, striatum and other subcortical regions. We confirmed the reproducibility of the two neurostructural subtypes across various sample sites, including Europe, North America and East Asia. This imaging-based taxonomy holds the potential to identify individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.

Efficacy of Digital Dance on Brain Imagery, Cognition, and Health: Randomized Controlled Trial.

BACKGROUND: Multidomain interventions have demonstrable benefits for promoting healthy aging, but self-empowerment strategies to sustain long-term gains remain elusive. OBJECTIVE: This study evaluated the effects of digital somatosensory dance game participation on brain imagery changes as primary outcomes and other physical and mental health measures as secondary outcomes related to healthy aging. METHODS: Between August 31, 2020, and June 27, 2021, this randomized controlled trial recruited 60 eligible participants older than 55 years with no recent engagement in digital dance games. A computer-generated randomization sequence was used to allocate participants 1:1, without stratification, to an intervention group (n=30) who underwent digital somatosensory dance game training or a control group (n=30). An anonymized code masked the intervention allocations from the investigators, and individuals who assigned the interventions were not involved in analyzing the study data. The intervention entailed two 30-minute dance game sessions per week for 6 months, and the control group received healthy aging education. Primary outcomes were brain imagery changes. All variables were measured at baseline and the 6-month follow-up, and intervention effects were estimated using t tests with intention-to-treat analyses. RESULTS: Compared with the control group, intervention participants had significantly different brain imagery in the gray matter volume (GMV) of the left putamen (estimate 0.016, 95% CI 0.008 to 0.024; P<.001), GMV of the left pallidum (estimate 0.02, 95% CI 0.006 to 0.034; P=.004), and fractional amplitude of low frequency fluctuations of the left pallidum (estimate 0.262, 95% CI 0.084 to 0.439; P=.004). Additionally, the intervention group had different imagery in the cerebellum VI GMV (estimate 0.011, 95% CI 0.003 to 0.02; P=.01). The intervention group also had improved total Montreal Cognitive Assessment scores (estimate 1.2, 95% CI 0.27 to -2.13; P<.01), quality of life (estimate 7.08, 95% CI 2.35 to 11.82; P=.004), and time spent sitting on weekdays (estimate -1.96, 95% CI -3.33 to -0.60; P=.005). Furthermore, dance performance was significantly associated with cognitive performance (P=.003), health status (P=.14), resilience (P=.007), and demoralization (P<.001). CONCLUSIONS: Digital somatosensory dance game participation for 6 months was associated with brain imagery changes in multiple regions involving somatosensory, motor, visual, and attention functions, which were consistent with phenotypic improvements associated with healthy aging. TRIAL REGISTRATION: ClinicalTrials.gov NCT05411042; https://clinicaltrials.gov/study/NCT05411042.

Neuroimaging epicenters as potential sites of onset of the neuroanatomical pathology in schizophrenia.

Schizophrenia lacks a clear definition at the neuroanatomical level, capturing the sites of origin and progress of this disorder. Using a network-theory approach called epicenter mapping on cross-sectional magnetic resonance imaging from 1124 individuals with schizophrenia, we identified the most likely "source of origin" of the structural pathology. Our results suggest that the Broca's area and adjacent frontoinsular cortex may be the epicenters of neuroanatomical pathophysiology in schizophrenia. These epicenters can predict an individual's response to treatment for psychosis. In addition, cross-diagnostic similarities based on epicenter mapping over of 4000 individuals diagnosed with neurological, neurodevelopmental, or psychiatric disorders appear to be limited. When present, these similarities are restricted to bipolar disorder, major depressive disorder, and obsessive-compulsive disorder. We provide a comprehensive framework linking schizophrenia-specific epicenters to multiple levels of neurobiology, including cognitive processes, neurotransmitter receptors and transporters, and human brain gene expression. Epicenter mapping may be a reliable tool for identifying the potential onset sites of neural pathophysiology in schizophrenia.

White matter alterations and their associations with biomarkers and behavior in subjective cognitive decline individuals: a fixel-based analysis.

BACKGROUND: Subjective cognitive decline (SCD) is an early stage of dementia linked to Alzheimer's disease pathology. White matter changes were found in SCD using diffusion tensor imaging, but there are known limitations in voxel-wise tensor-based methods. Fixel-based analysis (FBA) can help understand changes in white matter fibers and how they relate to neurodegenerative proteins and multidomain behavior data in individuals with SCD. METHODS: Healthy adults with normal cognition were recruited in the Northeastern Taiwan Community Medicine Research Cohort in 2018-2022 and divided into SCD and normal control (NC). Participants underwent evaluations to assess cognitive abilities, mental states, physical activity levels, and susceptibility to fatigue. Neurodegenerative proteins were measured using an immunomagnetic reduction technique. Multi-shell diffusion MRI data were collected and analyzed using whole-brain FBA, comparing results between groups and correlating them with multidomain assessments. RESULTS: The final enrollment included 33 SCD and 46 NC participants, with no significant differences in age, sex, or education between the groups. SCD had a greater fiber-bundle cross-section than NC (pFWE < 0.05) at bilateral frontal superior longitudinal fasciculus II (SLFII). These white matter changes correlate negatively with plasma Aß42 level (r = -0.38, p = 0.01) and positively with the AD8 score for subjective cognitive complaints (r = 0.42, p = 0.004) and the Hamilton Anxiety Rating Scale score for the degree of anxiety (Ham-A, r = 0.35, p = 0.019). The dimensional analysis of FBA metrics and blood biomarkers found positive correlations of plasma neurofilament light chain with fiber density at the splenium of corpus callosum (pFWE < 0.05) and with fiber-bundle cross-section at the right thalamus (pFWE < 0.05). Further examination of how SCD grouping interacts between the correlations of FBA metrics and multidomain assessments showed interactions between the fiber density at the corpus callosum with letter-number sequencing cognitive score (pFWE < 0.01) and with fatigue to leisure activities (pFWE < 0.05). CONCLUSION: Based on FBA, our investigation suggests white matter structural alterations in SCD. The enlargement of SLFII's fiber cross-section is linked to plasma Aß42 and neuropsychiatric symptoms, which suggests potential early axonal dystrophy associated with Alzheimer's pathology in SCD. The splenium of the corpus callosum is also a critical region of axonal degeneration and cognitive alteration for SCD.

Altered cerebellar and caudate gray-matter volumes and structural covariance networks preceding dual cognitive and mobility impairments in older people.

INTRODUCTION: The neuroanatomical changes driving both cognitive and mobility impairments, an emerging preclinical dementia syndrome, are not fully understood. We examined gray-matter volumes (GMVs) and structural covariance networks (SCNs) abnormalities in community-based older people preceding the conversion to physio-cognitive decline syndrome (PCDS). METHODS: Voxel-wise brain GMV and established SCNs were compared between PCDS and non-PCDS converters. RESULTS: The study included 343 individuals (60.2 ± 6.9 years, 49.6% men) with intact cognitive and mobility functions. Over an average 5.6-year follow-up, 116 transitioned to PCDS. Identified regions with abnormal GMVs in PCDS converters were over cerebellum and caudate, which served as seeds for SCNs establishment. Significant differences in cerebellum-based (to right frontal pole and left middle frontal gyrus) and caudate-based SCNs (to right caudate putamen, right planum temporale, left precentral gyrus, right postcentral gyrus, and left parietal operculum) between converters and nonconverters were observed. DISCUSSION: This study reveals early neuroanatomic changes, emphasizing the cerebellum's role, in dual cognitive and mobility impairments. HIGHLIGHTS: Neuroanatomic precursors of dual cognitive and mobility impairments are identified. Cerebellar GMV reductions and increased right caudate GMV precede the onset of PCDS. Altered cerebellum- and caudate-based SCNs drive PCDS transformation. This research establishes a foundation for understanding PCDS as a specific dementia syndrome.

The aging trajectories of brain functional hierarchy and its impact on cognition across the adult lifespan.

Introduction: The hierarchical network architecture of the human brain, pivotal to cognition and behavior, can be explored via gradient analysis using restingstate functional MRI data. Although it has been employed to understand brain development and disorders, the impact of aging on this hierarchical architecture and its link to cognitive decline remains elusive. Methods: This study utilized resting-state functional MRI data from 350 healthy adults (aged 20-85) to investigate the functional hierarchical network using connectome gradient analysis with a cross-age sliding window approach. Gradient-related metrics were estimated and correlated with age to evaluate trajectory of gradient changes across lifespan. Results: The principal gradient (unimodal-to-transmodal) demonstrated a significant non-linear relationship with age, whereas the secondary gradient (visual-to-somatomotor) showed a simple linear decreasing pattern. Among the principal gradient, significant age-related changes were observed in the somatomotor, dorsal attention, limbic and default mode networks. The changes in the gradient scores of both the somatomotor and frontal-parietal networks were associated with greater working memory and visuospatial ability. Gender differences were found in global gradient metrics and gradient scores of somatomotor and default mode networks in the principal gradient, with no interaction with age effect. Discussion: Our study delves into the aging trajectories of functional connectome gradient and its cognitive impact across the adult lifespan, providing insights for future research into the biological underpinnings of brain function and pathological models of atypical aging processes.

Gold nanoparticles decorated on MOF derived Cu5Zn8 hollow porous carbon nanocubes for magnetic resonance imaging guided tumor microenvironment-mediated synergistic chemodynamic and photothermal therapy.

Combining chemodynamic therapy (CDT) with photothermal therapy (PTT) has developed as a promising approach for cancer treatment, as it enhances therapeutic efficiency through redox reactions and external laser induction. In this study, we designed metal organic framework (MOF) -derived Cu5Zn8/HPCNC through a carbonization process and decorated them with gold nanoparticles (Au@Cu5Zn8/HPCNC). The resulting nanoparticles were employed as a photothermal agent and Fenton catalyst. The Fenton reaction facilitated the conversation of Cu2+ to Cu+ through reaction with local H2O2, generating reactive hydroxyl radicals (·OH) with potent cytotoxic effects. To enhance the Fenton-like reaction and achieve combined therapy, laser irradiation of the Au@Cu5Zn8/HPCNC induced efficient photothermal therapy by generating localized heat. With a significantly increased absorption of Au@Cu5Zn8/HPCNC at 808 nm, the photothermal efficiency was determined to be 57.45 %. Additionally, Au@Cu5Zn8/HPCNC demonstrated potential as a contrast agent for magnetic resonance imaging (MRI) of cancers. Furthermore, the synergistic combination of PTT and CDT significantly inhibited tumor growth. This integrated approach of PTT and CDT holds great promise for cancer therapy, offering enhanced CDT and modulation of the tumor microenvironment (TME), and opening new avenues in the fight against cancer.

Neurovascular coupling in eye-open-eye-close task and resting state: Spectral correspondence between concurrent EEG and fMRI.

Neurovascular coupling serves as an essential neurophysiological mechanism in functional neuroimaging, which is generally presumed to be robust and invariant across different physiological states, encompassing both task engagement and resting state. Nevertheless, emerging evidence suggests that neurovascular coupling may exhibit state dependency, even in normal human participants. To investigate this premise, we analyzed the cross-frequency spectral correspondence between concurrently recorded electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data, utilizing them as proxies for neurovascular coupling during the two conditions: an eye-open-eye-close (EOEC) task and a resting state. We hypothesized that given the state dependency of neurovascular coupling, EEG-fMRI spectral correspondences would change between the two conditions in the visual system. During the EOEC task, we observed a negative phase-amplitude-coupling (PAC) between EEG alpha-band and fMRI visual activity. Conversely, in the resting state, a pronounced amplitude-amplitude-coupling (AAC) emerged between EEG and fMRI signals, as evidenced by the spectral correspondence between the EEG gamma-band of the midline occipital channel (Oz) and the high-frequency fMRI signals (0.15-0.25 Hz) in the visual network. This study reveals distinct scenarios of EEG-fMRI spectral correspondence in healthy participants, corroborating the state-dependent nature of neurovascular coupling.

Exploring timescale-specific functional brain networks and their associations with aging and cognitive performance in a healthy cohort without dementia.

INTRODUCTION: Functional brain networks (FBNs) coordinate brain functions and are studied in fMRI using blood-oxygen-level-dependent (BOLD) signal correlations. Previous research links FBN changes to aging and cognitive decline, but various physiological factors influnce BOLD signals. Few studies have investigated the intrinsic components of the BOLD signal in different timescales using signal decomposition. This study aimed to explore differences between intrinsic FBNs and traditional BOLD-FBN, examining their associations with age and cognitive performance in a healthy cohort without dementia. MATERIALS AND METHODS: A total of 396 healthy participants without dementia (men = 157; women = 239; age range = 20-85 years) were enrolled in this study. The BOLD signal was decomposed into several intrinsic signals with different timescales using ensemble empirical mode decomposition, and FBNs were constructed based on both the BOLD and intrinsic signals. Subsequently, network features-global efficiency and local efficiency values-were estimated to determine their relationship with age and cognitive performance. RESULTS: The findings revealed that the global efficiency of traditional BOLD-FBN correlated significantly with age, with specific intrinsic FBNs contributing to these correlations. Moreover, local efficiency analysis demonstrated that intrinsic FBNs were more meaningful than traditional BOLD-FBN in identifying brain regions related to age and cognitive performance. CONCLUSIONS: These results underscore the importance of exploring timescales of BOLD signals when constructing FBN and highlight the relevance of specific intrinsic FBNs to aging and cognitive performance. Consequently, this decomposition-based FBN-building approach may offer valuable insights for future fMRI studies.

Mild cognitive impairment estimation based on functional near-infrared spectroscopy.

Patients with mild cognitive impairment (MCI) are at a high risk of developing future dementia. However, early identification and active intervention could potentially reduce its morbidity and the incidence of dementia. Functional near-infrared spectroscopy (fNIRS) has been proposed as a noninvasive modality for detecting oxygenation changes in the time-varying hemodynamics of the prefrontal cortex. This study sought to provide an effective method for detecting patients with MCI using fNIRS and the Wisconsin card sorting test (WCST) to evaluate changes in blood oxygenation. The results revealed that all groups with a lower mini-mental state examination grade had a higher increase in HHb concentration during a modified WCST (MCST). The increase in the change in oxygenated hemoglobin concentration in the stroke group was smaller than that in the normal group due to weak cerebrovascular reactivity.

Synergistic effect of photothermal and magnetic hyperthermia for in situ activation of Fenton reaction in tumor microenvironment for chemodynamic therapy.

Traditional cancer treatments are ineffective and cause severe adverse effects. Thus, the development of chemodynamic therapy (CDT) has the potential for in situ catalysis of endogenous molecules into highly toxic species, which would then effectively destroy cancer cells. However, the shortage of high-performance nanomaterials hinders the broad clinical application of this approach. In present study, an effective therapeutic platform was developed using a simple hydrothermal method for the in-situ activation of the Fenton reaction within the tumor microenvironment (TME) to generate substantial quantities of •OH and ultimately destroy cancer cells, which could be further synergistically increased by photothermal therapy (PHT) and magnetic hyperthermia (MHT) aided by FeMoO4 nanorods (NRs). The produced FeMoO4 NRs were used as MHT/PHT and Fenton catalysts. The photothermal conversion efficiency of the FeMoO4 NRs was 31.75 %. In vitro and \ experiments demonstrated that the synergistic combination of MHT/PHT/CDT notably improved anticancer efficacy. This work reveals the significant efficacy of CDT aided by both photothermal and magnetic hyperthermia and offers a feasible strategy for the use of iron-based nanoparticles in the field of biomedical applications.

Dopaminergic-related Anatomical Pattern of Dorsal Striatum in Schizophrenia.

Striatal dopaminergic overactivity was hypothesized as the core pathophysiology of schizophrenia. However, morphological alterations of striatum in schizophrenia remains exclusive, largely because brain regional heterogeneity limited traditional group-mean based approach. Leveraging third-party brain maps of neurotransmitter and cognition behaviours, we developed a pattern-based representation feature score (ReFS) to investigate structural spatial pattern variation in schizophrenia. Structural ReFS of subcortical regions, particularly the striatum, were linked to schizophrenia diagnosis, symptom severity, and genetic susceptibility. Dopaminergic-ReFS of striatum was increased in schizophrenia patients and reliably reproduced across 13 datasets. The pattern-based ReFS effectively captured the shared genetic pathways underlying both schizophrenia and striatum. The results provide convergent, multimodal suggest the central role of striatal spatial patterns in schizophrenia psychopathologies and and open new avenues to develop individualized treatments for psychotic disorders.

Two neurostructural subtypes: results of machine learning on brain images from 4,291 individuals with schizophrenia.

Machine learning can be used to define subtypes of psychiatric conditions based on shared clinical and biological foundations, presenting a crucial step toward establishing biologically based subtypes of mental disorders. With the goal of identifying subtypes of disease progression in schizophrenia, here we analyzed cross-sectional brain structural magnetic resonance imaging (MRI) data from 4,291 individuals with schizophrenia (1,709 females, age=32.5 years±11.9) and 7,078 healthy controls (3,461 females, age=33.0 years±12.7) pooled across 41 international cohorts from the ENIGMA Schizophrenia Working Group, non-ENIGMA cohorts and public datasets. Using a machine learning approach known as Subtype and Stage Inference (SuStaIn), we implemented a brain imaging-driven classification that identifies two distinct neurostructural subgroups by mapping the spatial and temporal trajectory of gray matter (GM) loss in schizophrenia. Subgroup 1 (n=2,622) was characterized by an early cortical-predominant loss (ECL) with enlarged striatum, whereas subgroup 2 (n=1,600) displayed an early subcortical-predominant loss (ESL) in the hippocampus, amygdala, thalamus, brain stem and striatum. These reconstructed trajectories suggest that the GM volume reduction originates in the Broca's area/adjacent fronto-insular cortex for ECL and in the hippocampus/adjacent medial temporal structures for ESL. With longer disease duration, the ECL subtype exhibited a gradual worsening of negative symptoms and depression/anxiety, and less of a decline in positive symptoms. We confirmed the reproducibility of these imaging-based subtypes across various sample sites, independent of macroeconomic and ethnic factors that differed across these geographic locations, which include Europe, North America and East Asia. These findings underscore the presence of distinct pathobiological foundations underlying schizophrenia. This new imaging-based taxonomy holds the potential to identify a more homogeneous sub-population of individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.

Functional connectome through the human life span.

The functional connectome of the human brain represents the fundamental network architecture of functional interdependence in brain activity, but its normative growth trajectory across the life course remains unknown. Here, we aggregate the largest, quality-controlled multimodal neuroimaging dataset from 119 global sites, including 33,809 task-free fMRI and structural MRI scans from 32,328 individuals ranging in age from 32 postmenstrual weeks to 80 years. Lifespan growth charts of the connectome are quantified at the whole cortex, system, and regional levels using generalized additive models for location, scale, and shape. We report critical inflection points in the non-linear growth trajectories of the whole-brain functional connectome, particularly peaking in the fourth decade of life. Having established the first fine-grained, lifespan-spanning suite of system-level brain atlases, we generate person-specific parcellation maps and further show distinct maturation timelines for functional segregation within different subsystems. We identify a spatiotemporal gradient axis that governs the life-course growth of regional connectivity, transitioning from primary sensory cortices to higher-order association regions. Using the connectome-based normative model, we demonstrate substantial individual heterogeneities at the network level in patients with autism spectrum disorder and patients with major depressive disorder. Our findings shed light on the life-course evolution of the functional connectome and serve as a normative reference for quantifying individual variation in patients with neurological and psychiatric disorders.

Vascular risk factors and astrocytic marker for the glymphatic system activity.

OBJECTIVES: Glymphatic system maintains brain fluid circulation via active transportation of astrocytic aquaporin-4 in perivascular space. The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) is an established method measuring perivascular glymphatic activity, but comprehensive investigations into its influential factors are lacking. METHODS: Community-dwelling older adults underwent brain MRI scans, neuropsychiatric, and multi-domain assessments. Blood biomarker tests included glial fibrillary acidic protein (GFAP) for astrocyte injury. RESULTS: In 71 enrolled participants, the DTI-ALPS index was associated with modifiable factors, including lipid profile (high-density lipoprotein, r = 0.396; very-low-density lipoprotein, r = - 0.342), glucose intolerance (diabetes mellitus, standardized mean difference (SMD) = 0.7662; glycated hemoglobin, r = - 0.324), obesity (body mass index, r = - 0.295; waist, r = - 0.455), metabolic syndrome (SMD = - 0.6068), cigarette-smoking (SMD = - 0.6292), and renal clearance (creatinine, r = - 0.387; blood urea nitrogen, r = - 0.303). Unmodifiable associative factors of DTI-ALPS were age (r = - 0.434) and sex (SMD = 1.0769) (all p < 0.05). A correlation of DTI-ALPS and blood GFAP was noticed (r = - 0.201, one-tailed t-test for the assumption that astrocytic injury impaired glymphatic activity, p = 0.046). Their cognitive correlations diverged, domain-specific for DTI-ALPS (Facial Memory Test, r = 0.272, p = 0.022) but global cognition-related for blood GFAP (MoCA, r = - 0.264, p = 0.026; ADAS-cog, r = 0.304, p = 0.010). CONCLUSION: This correlation analysis revealed multiple modifiable and unmodifiable association factors to the glymphatic image marker. The DTI-ALPS index correlated with various metabolic factors that are known to increase the risk of vascular diseases such as atherosclerosis. Furthermore, the DTI-ALPS index was associated with renal indices, and this connection might be a link of water regulation between the two systems. In addition, the astrocytic biomarker, plasma GFAP, might be a potential marker of the glymphatic system; however, more research is needed to confirm its effectiveness.

Advanced brain age in community-dwelling population with combined physical and cognitive impairments.

We investigated whether advanced brain biological age is associated with accelerated age-related physical and/or cognitive functional decline: mobility impairment no disability (MIND), cognitive impairment no dementia (CIND), and physio-cognitive decline syndrome (PCDS). We constructed a brain age prediction model using gray matter features from the magnetic resonance imaging of 1482 healthy individuals (aged 18-92 years). Predicted and chronological age differences were obtained (brain age gap [BAG]) and analyzed in another 1193 community-dwelling population aged ≥50 years. Among the 1193 participants, there were 501, 346, 148, and 198 in the robust, CIND, MIND, and PCDS groups, respectively. Participants with PCDS had significantly larger BAG (BAG = 2.99 ± 8.97) than the robust (BAG = -0.49 ± 9.27, p = 0.002; η2 = 0.014), CIND (BAG = 0.47 ± 9.16, p = 0.02; η2 = 0.01), and MIND (BAG = 0.36 ± 9.69, p = 0.036; η2 = 0.013) groups. Advanced brain aging is involved in the pathophysiology of the co-occurrence of physical and cognitive decline in the older people. The PCDS may be a clinical phenotype reflective of accelerated biological age in community-dwelling older individuals.

Shared alterations in hippocampal structural covariance in subjective cognitive decline and migraine.

Introduction: Subjective cognitive decline (SCD) and migraine are often comorbid. Hippocampal structural abnormalities have been observed in individuals with both SCD and migraine. Given the known structural and functional heterogeneity along the long axis (anterior to posterior) of the hippocampus, we aimed to identify altered patterns of structural covariance within hippocampal subdivisions associated with SCD and migraine comorbidities. Methods: A seed-based structural covariance network analysis was applied to examine large-scale anatomical network changes of the anterior and posterior hippocampus in individuals with SCD, migraine and healthy controls. Conjunction analyses were used to identify shared network-level alterations in the hippocampal subdivisions in individuals with both SCD and migraine. Results: Altered structural covariance integrity of the anterior and posterior hippocampus was observed in the temporal, frontal, occipital, cingulate, precentral, and postcentral areas in individuals with SCD and migraine compared with healthy controls. Conjunction analysis revealed that, in both SCD and migraine, altered structural covariance integrity was shared between the anterior hippocampus and inferior temporal gyri and between the posterior hippocampus and precentral gyrus. Additionally, the structural covariance integrity of the posterior hippocampus-cerebellum axis was associated with the duration of SCD. Conclusion: This study highlighted the specific role of hippocampal subdivisions and specific structural covariance alterations within these subdivisions in the pathophysiology of SCD and migraine. These network-level changes in structural covariance may serve as potential imaging signatures for individuals who have both SCD and migraine.