Multiscale brain age prediction reveals region-specific accelerated brain aging in Parkinson's disease.

Brain biological age, which measures the aging process in the brain using neuroimaging data, has been used to assess advanced brain aging in neurodegenerative diseases, including Parkinson disease (PD). However, assuming that whole brain degeneration is uniform may not be sufficient for assessing the complex neurodegenerative processes in PD. In this study we constructed a multiscale brain age prediction models based on structural MRI of 1240 healthy participants. To assess the brain aging patterns using the brain age prediction model, 93 PD patients and 91 healthy controls matching for sex and age were included. We found increased global and regional brain age in PD patients. The advanced aging regions were predominantly noted in the frontal and temporal cortices, limbic system, basal ganglia, thalamus, and cerebellum. Furthermore, region-level rather than global brain age in PD patients was associated with disease severity. Our multiscale brain age prediction model could aid in the development of objective image-based biomarkers to detect advanced brain aging in neurodegenerative diseases.

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.

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.

Brain morphometry changes with fatigue severity in fibromyalgia.

OBJECTIVES: This study investigated brain morphometry changes associated with fatigue severity in fibromyalgia (FM). METHODS: Clinical profiles and brain-MRI data were collected in patients with FM. Patients were divided into three groups based on their fatigue severity. Using voxel-based morphometry analysis and trend analysis, neural substrates showing volumetric changes associated with fatigue severity across the three groups were identified. Their seed-to-voxel structural covariance (SC) networks with the whole brain were studied in distribution and strength. RESULTS: Among the 138 enrolled patients with FM, 23, 57, and 58 were categorised into the mild, moderate, and severe fatigue groups, respectively. The number of musculoskeletal pain regions and intensity of pain were not associated with fatigue severity, but somatic symptoms and psychiatric distress, including waking unrefreshed, depression, and anxiety, were associated with fatigue severity. After adjusting for anxiety and depression, decreased bilateral thalamic volumes were associated with higher fatigue severity. The SC distributions of the thalamic seed were more widespread to the frontal, parietal, subcortical, and limbic regions in patients with higher fatigue severity. In addition, increased right inferior temporal cortex volumes were associated with higher fatigue severity. The SC distributions of the right inferior temporal seed were more over the temporal cortex and the SC strengths of the seed were higher with the bilateral occipital cortex in patients with higher fatigue severity. CONCLUSIONS: The thalamus and the right inferior temporal cortex are implicated in the manifestation of fatigue severity in FM. Future therapeutic strategies targeting these regions are worthy of investigation.

Regional rather than global brain age mediates cognitive function in cerebral small vessel disease.

The factors and mechanisms underlying the heterogeneous cognitive outcomes of cerebral small vessel disease are largely unknown. Brain biological age can be estimated by machine learning algorithms that use large brain MRI data sets to integrate and compute neuroimaging-derived age-related features. Predicted and chronological ages difference (brain-age gap) reflects advanced or delayed brain aging in an individual. The present study firstly reports the brain aging status of cerebral small vessel disease. In addition, we investigated whether global or certain regional brain age could mediate the cognitive functions in cerebral small vessel disease. Global and regional (400 cortical, 14 subcortical and 28 cerebellum regions of interest) brain-age prediction models were constructed using grey matter features from MRI of 1482 healthy individuals (age: 18-92 years). Predicted and chronological ages differences were obtained and then applied to non-stroke, non-demented individuals, aged ≥50 years, from another community-dwelling population (I-Lan Longitudinal Aging Study cohort). Among the 734 participants from the I-Lan Longitudinal Aging Study cohort, 124 were classified into the cerebral small vessel disease group. The cerebral small vessel disease group demonstrated significantly poorer performances in global cognitive, verbal memory and executive functions than that of non-cerebral small vessel disease group. Global brain-age gap was significantly higher in the cerebral small vessel disease (3.71 ± 7.60 years) than that in non-cerebral small vessel disease (-0.43 ± 9.47 years) group (P = 0.003, η2 = 0.012). There were 82 cerebral cortical, 3 subcortical and 4 cerebellar regions showing significantly different brain-age gap between the cerebral small vessel disease and non-cerebral small vessel disease groups. Global brain-age gap failed to mediate the relationship between cerebral small vessel disease and any of the cognitive domains. In 89 regions with increased brain-age gap in the cerebral small vessel disease group, seven regional brain-age gaps were able to show significant mediation effects in cerebral small vessel disease-related cognitive impairment (we set the statistical significance P < 0.05 uncorrected in 89 mediation models). Of these, the left thalamus and left hippocampus brain-age gap explained poorer global cognitive performance in cerebral small vessel disease. We demonstrated the interconnections between cerebral small vessel disease and brain age. Strategic brain aging, i.e. advanced brain aging in critical regions, may be involved in the pathophysiology of cerebral small vessel disease-related cognitive impairment. Regional rather than global brain-age gap could potentially serve as a biomarker for predicting heterogeneous cognitive outcomes in patients with cerebral small vessel disease.

The Flexibility of Physio-Cognitive Decline Syndrome: A Longitudinal Cohort Study.

The mutual presence of impairments in physical and cognitive functions in older adults has been reported to predict incident disability, dementia, and mortality. The longitudinal transitions of phenotypes between these functional impairments, either individually or in combination, remain unclear. To investigate the natural course and prevalence of physical and/or cognitive impairments (CIs), we enrolled participants from a community-based population. Data were retrieved from the first (August 2011 and December 2012) and second wave (August 2013 and June 2015) of the I-Lan Longitudinal Aging Study (ILAS). All participants were classified into four groups: robust, mobility impairment (MI), CI, and physio-cognitive decline syndrome (PCDS). MI was diagnosed with weakness and/or slowness. CI was diagnosed if a subject met a cutoff below 1.5 standard deviations (SDs) of age-, sex-, and education-matched norms of any neuropsychological assessments. PCDS was combined with MI and CI. Our results showed that 38, 14, 30, and 18% of the participants were on the robust, MI, CI, and PCDS at the first wave, respectively. After 2.5 years, 17% robust, 29% MI, and 37% CI progressed to PCDS. In contrast, 33% of PCDS was reversed to non-PCDS. Predictors of conversion to PCDS included worse memory and language functions, older age, lower muscle mass, and the presence of diabetes. In PCDS, a stronger hand-grip strength, younger age, and better memory functions predicted reversion to non-PCDS status. In summary, we probed the transition of PCDS. The skeletal muscle mass/function and memory function are crucial factors associated with PCDS reversion or progression.

Frailty and dementia risks in asymptomatic cerebral small vessel disease: A longitudinal cohort study.

OBJECTIVES: Frailty has been shown to predict adverse outcomes in several diseases. We aimed to evaluate the associations between frailty profiles, both severity and subtype, and dementia risk in a community-based population with asymptomatic (without stroke and dementia) cerebral small vessel disease (CSVD). METHODS: Individuals with asymptomatic CSVD were recruited from the community-based I-Lan Longitudinal Aging Study between 2011 and 2014 (baseline) and were followed up between 2018 and 2019. All participants underwent CSVD assessment by 3T brain MRI, as well as physical and cognitive assessments at baseline. Univariate and multivariate logistic regression analyses were performed to evaluate the associations between each factor and dementia conversion at follow-up. RESULTS: Among 261 participants with asymptomatic CSVD (64.8 [50.0-89.1, 8.4] years; 136 [52.1%] men), 13 (5.0%) developed dementia during a mean follow-up of 5.7 (0.7) years. Dementia converters were less likely to be robust (30.8% vs. 61.5%) and more likely to be pre-frail/frail (69.2% vs. 38.5%) than non-converters (p = 0.040). Meanwhile, there was significantly more frequent mobility frailty (53.8% vs. 19.8%, p = 0.009), but a similar prevalence of non-mobility frailty in dementia converters compared with non-converters. Univariate analyses showed that neither frailty severity nor CSVD burden was associated with a higher risk of dementia; it was the frailty subtype, the mobility frailty, which was significantly associated with dementia conversion in participants with asymptomatic CSVD, with an odds-ratio of 4.8 (95% CI = 1.5-14.8, p = 0.007). The significance remained after adjusting for age, sex, education and baseline cognitive function, respectively. CONCLUSION: Mobility frailty was associated with a higher risk of incident dementia in individuals with subclinical CSVD. Mobility frailty might be involved in the pathology of cognitive decline in CSVD and potentially serve as a marker to identify people at risk of cognitive impairment at an early stage of CSVD.

Brain white matter hyperintensities-predicted age reflects neurovascular health in middle-to-old aged subjects.

BACKGROUND: age-related neurovascular structural and functional impairment is a major aetiology of dementia and stroke in older people. There is no single marker representative of neurovascular biological age yet. OBJECTIVE: this study aims to develop and validate a white matter hyperintensities (WMH)-based model for characterising individuals' neurovascular biological age. METHODS: in this prospective single-site study, the WMH-based age-prediction model was constructed based on WMH volumes of 491 healthy participants (21-89 years). In the training dataset, the constructed linear-regression model with log-transformed WMH volumes showed well-balanced complexity and accuracy (root mean squared error, RMSE = 10.20 and mean absolute error, MAE = 7.76 years). This model of neurovascular age estimation was then applied to a middle-to-old aged testing dataset (n = 726, 50-92 years) as the testing dataset for external validation. RESULTS: the established age estimator also had comparable generalizability with the testing dataset (RMSE = 7.76 and MAE = 6.38 years). In the testing dataset, the WMH-predicted age difference was negatively associated with visual executive function. Individuals with older predicted-age for their chronological age had greater cardiovascular burden and cardiovascular disease risks than individuals with normal or delayed predicted age. These associations were independent of chronological age. CONCLUSIONS: our model is easy to use in clinical practice that helps to evaluate WMH severity objective to chronological age. Current findings support our WMH-based age measurement to reflect neurovascular health and have potential diagnostic and prognostic value for clinical or research purposes in age-related neurovascular disorders.

Right anterior insula is associated with pain generalization in patients with fibromyalgia.

ABSTRACT: Despite diffuse tenderness, patients with fibromyalgia (FM) have reported a wide range of areas with musculoskeletal pain. This study investigated the neural structures and neuroanatomical networks associated with self-reported widespread pain in FM using magnetic resonance imaging. We collected clinical profiles and brain magnetic resonance imaging data of newly diagnosed patients with FM. A total of 138 patients with FM were divided into 3 subgroups based on the number of pain areas, with 3 to 8, 9 to 12, and 13 to 19 areas, respectively. Using voxel-based morphometry analysis, we first identified the neural structure that showed a trend of volumetric change across the 3 subgroups. We then used it as a candidate seed of interest with a seed-to-voxel analytical approach to explore the structural covariance (SC) networks of the whole brain. Finally, we studied the trend of changes in the distribution and strength of SC networks across subgroups of patients. We found a decreasing trend in the volumes of the right anterior insular cortex (rAIC) across the 3 subgroups that had an increased number of pain areas. An increasing trend in the number of neural substrates over the subcortical regions, especially the basal ganglion, showed SC to the rAIC, and a decreasing trend of SC strength was shown between the rAIC and the precuneus, frontal cortex, anterior and posterior cingulate, and lingual gyri, across the patient subgroups with increased pain areas. The rAIC and its altered connection with specific brain regions indicates widespread pain in patients with FM.

Cerebral small vessel disease phenotype and 5-year mortality in asymptomatic middle-to-old aged individuals.

The present study aimed to determine whether a recently proposed cerebral small vessel disease (CSVD) classification scheme could differentiate the 5-year all-cause mortality in middle-to-old aged asymptomatic CSVD. Stroke-free and non-demented participants recruited from the community-based I-Lan Longitudinal Aging Study underwent baseline brain magnetic resonance imaging (MRI) between 2011 and 2014 and were followed-up between 2018 and 2019. The study population was classified into control (non-CSVD) and CSVD type 1-4 groups based on MRI markers. We determined the association with mortality using Cox regression models, adjusting for the age, sex, and vascular risk factors. A total of 735 participants were included. During a mean follow-up of 5.7 years, 62 (8.4%) died. There were 335 CSVD type 1 (57.9 ± 5.9 years), 249 type 2 (65.6 ± 8.1 years), 52 type 3 (67.8 ± 9.2 years), and 38 type 4 (64.3 ± 9.0 years). Among the four CSVD types, CSVD type 4 individuals had significantly higher all-cause mortality (adjusted hazard ratio = 5.0, 95% confidence interval 1.6-15.3) compared to controls. This novel MRI-based CSVD classification scheme was able to identify individuals at risk of mortality at an asymptomatic, early stage of disease and might be applied for future community-based health research and policy.

Shared Patterns of Brain Functional Connectivity for the Comorbidity between Migraine and Insomnia.

Migraine is commonly comorbid with insomnia; both disorders are linked to functional disturbance of the default mode network (DMN). Evidence suggests that DMN could be segregated into multiple subnetworks with specific roles that underline different cognitive processes. However, the relative contributions of DMN subnetworks in the comorbidity of migraine and insomnia remain largely unknown. This study sought to identify altered functional connectivity (FC) profiles of DMN subnetworks in the comorbidity of migraine and insomnia. Direct group comparisons with healthy controls, followed by conjunction analyses, were used to identify shared FC alterations of DMN subnetworks. The shared FC changes of the DMN subnetworks in the migraine and insomnia groups were identified in the dorsomedial prefrontal and posteromedial cortex subnetworks. These shared FC changes were primarily associated with motor and somatosensory systems, and consistently found in patients with comorbid migraine and insomnia. Additionally, the magnitude of FC between the posteromedial cortex and postcentral gyrus correlated with insomnia duration in patients with comorbid migraine and insomnia. Our findings point to specific FC alterations of the DMN subnetwork in migraine and insomnia. The shared patterns of FC disturbance may be associated with the underlying mechanisms of the comorbidity of the two disorders.

The Potential Effects of Oxidative Stress-Related Plasma Abnormal Protein Aggregate Levels on Brain Volume and Its Neuropsychiatric Consequences in Parkinson's Disease.

BACKGROUND: Oxidative stress has been implicated in the pathogenesis of many diseases, including Parkinson's disease. Large protein aggregates may be produced after the breakdown of the proteostasis network due to overt oxidative stress. Meanwhile, brain volume loss and neuropsychiatric deficits are common comorbidities in Parkinson's disease patients. In this study, we applied a mediation model to determine the potential influences of oxidative stress-related plasma abnormal protein aggregate levels on brain volume and neuropsychiatric consequences in Parkinson's disease. METHOD: 31 patients with PD and 24 healthy controls participated in this study. The PD patients were further grouped according to the presentation of cognitive decline or not. All participants received complete examinations to determine plasma abnormal protein aggregates levels, brain volume, and neuropsychiatric performance. The results were collected and analyzed in a single-level three-variable mediation model. RESULTS: Patients with PD cognitive decline exhibited higher plasma NfL levels, decreased regional brain volume, and poor neuropsychiatric subtest results compared with PD patients with normal cognition, with several correlations among these clinical presentations. The mediation model showed that the superior temporal gyrus completely mediated the effects of elevated plasma NfL levels due to the poor psychiatric performance of picture completion and digit span. CONCLUSION: This study provides insight into the effects of oxidative stress-related plasma abnormal protein aggregate levels on regional brain volume and neuropsychiatric consequences in Parkinson's disease patients.

Brain Atrophy Mediates the Relationship between Misfolded Proteins Deposition and Cognitive Impairment in Parkinson's Disease.

Parkinson's disease is associated with cognitive decline, misfolded protein deposition and brain atrophy. We herein hypothesized that structural abnormalities may be mediators between plasma misfolded proteins and cognitive functions. Neuropsychological assessments including five domains (attention, executive, speech and language, memory and visuospatial functions), ultra-sensitive immunomagnetic reduction-based immunoassay (IMR) measured misfolded protein levels (phosphorylated-Tau, Amyloidß-42 and 40, α-synuclein and neurofilament light chain) and auto-segmented brain volumetry using FreeSurfur were performed for 54 Parkinson's disease (PD) patients and 37 normal participants. Our results revealed that PD patients have higher plasma misfolded protein levels. Phosphorylated-Tau (p-Tau) and Amyloidß-42 (Aß-42) were correlated with atrophy of bilateral cerebellum, right caudate nucleus, and right accumbens area (RAA). In mediation analysis, RAA atrophy completely mediated the relationship between p-Tau and digit symbol coding (DSC). RAA and bilateral cerebellar cortex atrophy partially mediated the Aß-42 and executive function (DSC and abstract thinking) relationship. Our study concluded that, in PD, p-Tau deposition adversely impacts DSC by causing RAA atrophy. Aß-42 deposition adversely impacts executive functions by causing RAA and bilateral cerebellum atrophy.

Classification differentiates clinical and neuroanatomic features of cerebral small vessel disease.

Age-related cerebral small vessel disease involves heterogeneous pathogenesis, such as arteriosclerosis/lipohyalinosis and cerebral amyloid angiopathy. MRI can visualize the brain lesions attributable to small vessel disease pathologies, including white-matter hyperintensities, lacune and cerebral microbleeds. However, these MRI markers usually coexist in small vessel disease of different aetiologies. Currently, there is no available classification integrating these neuroimaging markers for differentiating clinical and neuroanatomic features of small vessel disease yet. In this study, we tested whether our proposed stratification scheme could characterize specific clinical, neuroanatomic and potentially pathogenesis/aetiologies in classified small vessel disease subtypes. Cross-sectional analyses from a community-based non-demented non-stroke cohort consisting of ≥50 years old individuals were conducted. All participants were scanned 3T brain MRI for small vessel disease detection and neuroanatomic measurements and underwent physical and cognitive assessments. Study population were classified into robust and four small vessel disease groups based on imaging markers indicating (i) bleeding or non-bleeding; (ii) specific location of cerebral microbleeds; and (iii) the severity and combination of white-matter hyperintensities and lacune. We used whole-brain voxel-based morphometry analyses and tract-based spatial statistics to evaluate the regional grey-matter volume and white-matter microstructure integrity for comparisons among groups. Among the 735 participants with eligible brain MRI images, quality screening qualified 670 for grey-matter volume analyses and 617 for white-matter microstructural analyses. Common and distinct patterns of the clinical and neuroimaging manifestations were found in the stratified four small vessel disease subgroups. Hierarchical clustering analysis revealed that small vessel disease type 4 had features distinct from the small vessel disease types 1, 2 and 3. Abnormal white-matter microstructures and cognitive function but preserved physical function and grey-matter volume were found in small vessel disease type 4. Among small vessel disease types 1, 2 and 3, there were similar characteristics but different severity; the clinical features showed both physical frail and cognitive impairment and the neuroanatomic features revealed frontal-subcortical white-matter microstructures and remote, diffuse cortical abnormalities. This novel stratification scheme highlights the distinct clinical and neuroanatomic features of small vessel disease and the possible underlying pathogenesis. It could have potential application in research and clinical settings.

Improving Individual Brain Age Prediction Using an Ensemble Deep Learning Framework.

Brain age is an imaging-based biomarker with excellent feasibility for characterizing individual brain health and may serve as a single quantitative index for clinical and domain-specific usage. Brain age has been successfully estimated using extensive neuroimaging data from healthy participants with various feature extraction and conventional machine learning (ML) approaches. Recently, several end-to-end deep learning (DL) analytical frameworks have been proposed as alternative approaches to predict individual brain age with higher accuracy. However, the optimal approach to select and assemble appropriate input feature sets for DL analytical frameworks remains to be determined. In the Predictive Analytics Competition 2019, we proposed a hierarchical analytical framework which first used ML algorithms to investigate the potential contribution of different input features for predicting individual brain age. The obtained information then served as a priori knowledge for determining the input feature sets of the final ensemble DL prediction model. Systematic evaluation revealed that ML approaches with multiple concurrent input features, including tissue volume and density, achieved higher prediction accuracy when compared with approaches with a single input feature set [Ridge regression: mean absolute error (MAE) = 4.51 years, R 2 = 0.88; support vector regression, MAE = 4.42 years, R 2 = 0.88]. Based on this evaluation, a final ensemble DL brain age prediction model integrating multiple feature sets was constructed with reasonable computation capacity and achieved higher prediction accuracy when compared with ML approaches in the training dataset (MAE = 3.77 years; R 2 = 0.90). Furthermore, the proposed ensemble DL brain age prediction model also demonstrated sufficient generalizability in the testing dataset (MAE = 3.33 years). In summary, this study provides initial evidence of how-to efficiency for integrating ML and advanced DL approaches into a unified analytical framework for predicting individual brain age with higher accuracy. With the increase in large open multiple-modality neuroimaging datasets, ensemble DL strategies with appropriate input feature sets serve as a candidate approach for predicting individual brain age in the future.

Associations among Cognitive Functions, Plasma DNA, and Diffusion Tensor Image along the Perivascular Space (DTI-ALPS) in Patients with Parkinson's Disease.

BACKGROUND: Parkinson's disease (PD) is a common neurodegenerative disease associated with accumulation of misfolding proteins and increased neuroinflammation, which may further impair the glymphatic system. The purpose of this study was to utilize diffusion tensor image analysis along the perivascular space (DTI-ALPS) to evaluate glymphatic system activity and its relationship with systemic oxidative stress status in PD patients. METHODS: Magnetic resonance imaging and neuropsychological tests were conducted on 25 PD patients with normal cognition (PDN), 25 PD patients with mild cognitive impairment (PD-MCI), 38 PD patients with dementia (PDD), and 47 normal controls (NC). Oxidative stress status was assessed by plasma DNA level. Differences in ALPS-index among the subgroups were assessed and further correlated with cognitive functions and plasma DNA levels. RESULTS: The PD-MCI and PDD groups showed significantly lower ALPS-index compared to normal controls. The ALPS-index was inversely correlated with plasma nuclear DNA, mitochondrial DNA levels, and cognitive scores. CONCLUSIONS: Lower diffusivity along the perivascular space, represented by lower ALPS-index, indicates impairment of the glymphatic system in PD patients. The correlation between elevated plasma nuclear DNA levels and lower ALPS-index supports the notion that PD patients may exhibit increased oxidative stress associated with glymphatic system microstructural alterations.

Differential Patterns of Gyral and Sulcal Morphological Changes During Normal Aging Process.

The cerebral cortex is a highly convoluted structure with distinct morphologic features, namely the gyri and sulci, which are associated with the functional segregation or integration in the human brain. During the lifespan, the brain atrophy that is accompanied by cognitive decline is a well-accepted aging phenotype. However, the detailed patterns of cortical folding change during aging, especially the changing age-dependencies of gyri and sulci, which is essential to brain functioning, remain unclear. In this study, we investigated the morphology of the gyral and sulcal regions from pial and white matter surfaces using MR imaging data of 417 healthy participants across adulthood to old age (21-92 years). To elucidate the age-related changes in the cortical pattern, we fitted cortical thickness and intrinsic curvature of gyri and sulci using the quadratic model to evaluate their age-dependencies during normal aging. Our findings show that comparing to gyri, the sulcal thinning is the most prominent pattern during the aging process, and the gyrification of pial and white matter surfaces were also affected differently, which implies the vulnerability of functional segregation during aging. Taken together, we propose a morphological model of aging that may provide a framework for understanding the mechanisms underlying gray matter degeneration.

The corticolimbic structural covariance network as an early predictive biosignature for cognitive impairment in Parkinson's disease.

Structural covariance assesses similarities in gray matter between brain regions and can be applied to study networks of the brain. In this study, we explored correlations between structural covariance networks (SCNs) and cognitive impairment in Parkinson's disease patients. 101 PD patients and 58 age- and sex-matched healthy controls were enrolled in the study. For each participant, comprehensive neuropsychological testing using the Wechsler Adult Intelligence Scale-III and Cognitive Ability Screening Instrument were conducted. Structural brain MR images were acquired using a 3.0T whole body GE Signa MRI system. T1 structural images were preprocessed and analyzed using Statistical Parametric Mapping software (SPM12) running on Matlab R2016a for voxel-based morphometric analysis and SCN analysis. PD patients with normal cognition received follow-up neuropsychological testing at 1-year interval. Cognitive impairment in PD is associated with degeneration of the amygdala/hippocampus SCN. PD patients with dementia exhibited increased covariance over the prefrontal cortex compared to PD patients with normal cognition (PDN). PDN patients who had developed cognitive impairment at follow-up exhibited decreased gray matter volume of the amygdala/hippocampus SCN in the initial MRI. Our results support a neural network-based mechanism for cognitive impairment in PD patients. SCN analysis may reveal vulnerable networks that can be used to early predict cognitive decline in PD patients.

Identifying neuroanatomical signatures in insomnia and migraine comorbidity.

STUDY OBJECTIVES: While insomnia and migraine are often comorbid, the shared and distinct neuroanatomical substrates underlying these disorders and the brain structures associated with the comorbidity are unknown. We aimed to identify patterns of neuroanatomical substrate alterations associated with migraine and insomnia comorbidity. METHODS: High-resolution T1-weighted images were acquired from subjects with insomnia, migraine, and comorbid migraine and insomnia, respectively, and healthy controls (HC). Direct group comparisons with HC followed by conjunction analyses identified shared regional gray matter volume (GMV) alterations between the disorders. To further examine large-scale anatomical network changes, a seed-based structural covariance network (SCN) analysis was applied. Conjunction analyses also identified common SCN alterations in two disease groups, and we further evaluated these shared regional and global neuroanatomical signatures in the comorbid group. RESULTS: Compared with controls, patients with migraine and insomnia showed GMV changes in the cerebellum and the lingual, precentral, and postcentral gyri (PCG). The bilateral PCG were common GMV alteration sites in both groups, with decreased structural covariance integrity observed in the cerebellum. In patients with comorbid migraine and insomnia, shared regional GMV and global SCN changes were consistently observed. The GMV of the right PCG also correlated with sleep quality in these patients. CONCLUSION: These findings highlight the specific role of the PCG in the shared pathophysiology of insomnia and migraine from a regional and global brain network perspective. These multilevel neuroanatomical changes could be used as potential image markers to decipher the comorbidity of the two disorders.