Advanced brain aging in Parkinson's disease with cognitive impairment.

Patients with Parkinson's disease and cognitive impairment (PD-CI) deteriorate faster than those without cognitive impairment (PD-NCI), suggesting an underlying difference in the neurodegeneration process. We aimed to verify brain age differences in PD-CI and PD-NCI and their clinical significance. A total of 94 participants (PD-CI, n = 27; PD-NCI, n = 34; controls, n = 33) were recruited. Predicted age difference (PAD) based on gray matter (GM) and white matter (WM) features were estimated to represent the degree of brain aging. Patients with PD-CI showed greater GM-PAD (7.08 ± 6.64 years) and WM-PAD (8.82 ± 7.69 years) than those with PD-NCI (GM: 1.97 ± 7.13, Padjusted = 0.011; WM: 4.87 ± 7.88, Padjusted = 0.049) and controls (GM: -0.58 ± 7.04, Padjusted = 0.004; WM: 0.88 ± 7.45, Padjusted = 0.002) after adjusting demographic factors. In patients with PD, GM-PAD was negatively correlated with MMSE (Padjusted = 0.011) and MoCA (Padjusted = 0.013) and positively correlated with UPDRS Part II (Padjusted = 0.036). WM-PAD was negatively correlated with logical memory of immediate and delayed recalls (Padjusted = 0.003 and Padjusted < 0.001). Also, altered brain regions in PD-CI were identified and significantly correlated with brain age measures, implicating the neuroanatomical underpinning of neurodegeneration in PD-CI. Moreover, the brain age metrics can improve the classification between PD-CI and PD-NCI. The findings suggest that patients with PD-CI had advanced brain aging that was associated with poor cognitive functions. The identified neuroimaging features and brain age measures can serve as potential biomarkers of PD-CI.

MISPEL: A supervised deep learning harmonization method for multi-scanner neuroimaging data.

Large-scale data obtained from aggregation of already collected multi-site neuroimaging datasets has brought benefits such as higher statistical power, reliability, and robustness to the studies. Despite these promises from growth in sample size, substantial technical variability stemming from differences in scanner specifications exists in the aggregated data and could inadvertently bias any downstream analyses on it. Such a challenge calls for data normalization and/or harmonization frameworks, in addition to comprehensive criteria to estimate the scanner-related variability and evaluate the harmonization frameworks. In this study, we propose MISPEL (Multi-scanner Image harmonization via Structure Preserving Embedding Learning), a supervised multi-scanner harmonization method that is naturally extendable to more than two scanners. We also designed a set of criteria to investigate the scanner-related technical variability and evaluate the harmonization techniques. As an essential requirement of our criteria, we introduced a multi-scanner matched dataset of 3T T1 images across four scanners, which, to the best of our knowledge is one of the few datasets of this kind. We also investigated our evaluations using two popular segmentation frameworks: FSL and segmentation in statistical parametric mapping (SPM). Lastly, we compared MISPEL to popular methods of normalization and harmonization, namely White Stripe, RAVEL, and CALAMITI. MISPEL outperformed these methods and is promising for many other neuroimaging modalities.

Atypical development in white matter microstructures in ADHD: A longitudinal diffusion imaging study.

BACKGROUND: In cross-sectional studies, alterations in white matter microstructure are evident in children with attention-deficit/hyperactivity disorder (ADHD) but not so prominent in adults with ADHD compared to typically-developing controls (TDC). Moreover, the developmental trajectories of white matter microstructures in ADHD are unclear, given the limited longitudinal imaging studies that characterize developmental changes in ADHD vs. TDC. METHODS: This longitudinal study acquired diffusion spectrum imaging (DSI) at two time points. The sample included 55 participants with ADHD and 61 TDC. The enrollment/first DSI age ranged from 7 to 18 years, with a five-year mean follow-up time. We examined time-by-diagnosis interaction on the generalized fractional anisotropy (GFA) of 45 white matter tracts, adjusting for confounding factors and correcting for multiple comparisons. We also tested whether the longitudinal changes of microstructures were associated with ADHD symptoms and attention performance in a computerized continuous performance test. RESULTS: Participants with ADHD showed more rapid development of GFA in the arcuate fasciculus, superior longitudinal fasciculus, frontal aslant tract, cingulum, inferior fronto-occipital fasciculus (IFOF), frontostriatal tract connecting the prefrontal cortex (FS-PFC), thalamic radiation, corticospinal tract, and corpus callosum. Within participants with ADHD, more rapid GFA increases in cingulum and FS-PFC were associated with slower decreases in inattention symptoms. In addition, in all participants, more rapid GFA increases in cingulum and IFOF were associated with greater improvement in attention performance. CONCLUSION: Our findings suggest atypical developmental trajectories of white matter tracts in ADHD, characterized by normalization and possible compensatory neuroplastic processes with age from childhood to early adulthood.

Neurodevelopmental model of schizophrenia revisited: similarity in individual deviation and idiosyncrasy from the normative model of whole-brain white matter tracts and shared brain-cognition covariation with ADHD and ASD.

The neurodevelopmental model of schizophrenia is supported by multi-level impairments shared among schizophrenia and neurodevelopmental disorders. Despite schizophrenia and typical neurodevelopmental disorders, i.e., autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), as disorders of brain dysconnectivity, no study has ever elucidated whether whole-brain white matter (WM) tracts integrity alterations overlap or diverge between these three disorders. Moreover, whether the linked dimensions of cognition and brain metrics per the Research Domain Criteria framework cut across diagnostic boundaries remains unknown. We aimed to map deviations from normative ranges of whole-brain major WM tracts for individual patients to investigate the similarity and differences among schizophrenia (281 patients subgrouped into the first-episode, subchronic and chronic phases), ASD (175 patients), and ADHD (279 patients). Sex-specific WM tract normative development was modeled from diffusion spectrum imaging of 626 typically developing controls (5-40 years). There were three significant findings. First, the patterns of deviation and idiosyncrasy of WM tracts were similar between schizophrenia and ADHD alongside ASD, particularly at the earlier stages of schizophrenia relative to chronic stages. Second, using the WM deviation patterns as features, schizophrenia cannot be separated from neurodevelopmental disorders in the unsupervised machine learning algorithm. Lastly, the canonical correlation analysis showed schizophrenia, ADHD, and ASD shared linked cognitive dimensions driven by WM deviations. Together, our results provide new insights into the neurodevelopmental facet of schizophrenia and its brain basis. Individual's WM deviations may contribute to diverse arrays of cognitive function along a continuum with phenotypic expressions from typical neurodevelopmental disorders to schizophrenia.

Advanced brain aging in multiple system atrophy compared to Parkinson's disease.

Multiple system atrophy (MSA) and Parkinson's disease (PD) belong to alpha-synucleinopathy, but they have very different clinical courses and prognoses. An imaging biomarker that can differentiate between the two diseases early in the disease course is desirable for appropriate treatment. Neuroimaging-based brain age paradigm provides an individualized marker to differentiate aberrant brain aging patterns in neurodegenerative diseases. In this study, patients with MSA (N = 23), PD (N = 33), and healthy controls (N = 34; HC) were recruited. A deep learning approach was used to estimate brain-predicted age difference (PAD) of gray matter (GM) and white matter (WM) based on image features extracted from T1-weighted and diffusion-weighted magnetic resonance images, respectively. Spatial normative models of image features were utilized to quantify neuroanatomical impairments in patients, which were then used to estimate the contributions of image features to brain age measures. For PAD of GM (GM-PAD), patients with MSA had significantly older brain age (9.33 years) than those with PD (0.75 years; P = 0.002) and HC (-1.47 years; P < 0.001), and no significant difference was found between PD and HC (P = 1.000). For PAD of WM (WM-PAD), it was significantly greater in MSA (9.27 years) than that in PD (1.90 years; P = 0.037) and HC (-0.74 years; P < 0.001); there was no significant difference between PD and HC (P = 0.087). The most salient image features that contributed to PAD in MSA and PD were different. For GM, they were the orbitofrontal regions and the cuneus in MSA and PD, respectively, and for WM, they were the central corpus callosum and the uncinate fasciculus in MSA and PD, respectively. Our results demonstrated that MSA revealed significantly greater PAD than PD, which might be related to markedly different neuroanatomical contributions to brain aging. The image features with distinct contributions to brain aging might be of value in the differential diagnosis of MSA and PD.

Validation of neuroimaging-based brain age gap as a mediator between modifiable risk factors and cognition.

Neuroimaging-based brain age gap (BAG) is presumably a mediator linking modifiable risk factors to cognitive changes, but this has not been verified yet. To address this hypothesis, modality-specific brain age models were constructed and applied to a population-based cohort (N = 326) to estimate their BAG. Structural equation modeling was employed to investigate the mediation effect of BAG between modifiable risk factors (assessed by 2 cardiovascular risk scores) and cognitive functioning (examined by 4 cognitive assessments). The association between higher burden of modifiable risk factors and poorer cognitive functioning can be significantly mediated by a larger BAG (multimodal: p = 0.014, 40.8% mediation proportion; white matter-based: p = 0.023, 15.7% mediation proportion), which indicated an older brain. Subgroup analysis further revealed a steeper slope (p = 0.019) of association between cognitive functioning and multimodal BAG in the group of higher modifiable risks. The results confirm that BAG can serve as a mediating indicator linking risk loadings to cognitive functioning, implicating its potential in the management of cognitive aging and dementia.

Detection of advanced brain aging in schizophrenia and its structural underpinning by using normative brain age metrics.

Conceptualizing mental disorders as deviations from normative functioning provides a statistical perspective for understanding the individual heterogeneity underlying psychiatric disorders. To broaden the understanding of the idiosyncrasy of brain aging in schizophrenia, we introduced an imaging-derived brain age paradigm combined with normative modeling as novel brain age metrics. We constructed brain age models based on GM, WM, and their combination (multimodality) features of 482 normal participants. The normalized predicted age difference (nPAD) was estimated in 147 individuals with schizophrenia and their 130 demographically matched controls through normative models of brain age metrics and compared between the groups. Regression analyses were also performed to investigate the associations of nPAD with illness duration, onset age, symptom severity, and intelligence quotient. Finally, regional contributions to advanced brain aging in schizophrenia were investigated. The results showed that the individuals exhibited significantly higher nPAD (P < 0.001), indicating advanced normative brain age than the normal controls in GM, WM, and multimodality models. The nPAD measure based on WM was positively associated with the negative symptom score (P = 0.009), and negatively associated with the intelligence quotient (P = 0.039) and onset age (P = 0.006). The imaging features that contributed to nPAD mostly involved the prefrontal, temporal, and parietal lobes, especially the precuneus and uncinate fasciculus. This study demonstrates that normative brain age metrics could detect advanced brain aging and associated clinical and neuroanatomical features in schizophrenia. The proposed nPAD measures may be useful to investigate aberrant brain aging in mental disorders and their brain-phenotype relationships.

Differential Associations of White Matter Brain Age With Language-Related Mechanisms in Word-Finding Ability Across the Adult Lifespan.

Research on cognitive aging has established that word-finding ability declines progressively in late adulthood, whereas semantic mechanism in the language system is relatively stable. The aim of the present study was to investigate the associations of word-finding ability and language-related components with brain aging status, which was quantified by using the brain age paradigm. A total of 616 healthy participants aged 18-88 years from the Cambridge Centre for Ageing and Neuroscience databank were recruited. The picture-naming task was used to test the participants' language-related word retrieval ability through word-finding and word-generation processes. The naming response time (RT) and accuracy were measured under a baseline condition and two priming conditions, namely phonological and semantic priming. To estimate brain age, we established a brain age prediction model based on white matter (WM) features and estimated the modality-specific predicted age difference (PAD). Mass partial correlation analyses were performed to test the associations of WM-PAD with the cognitive performance measures under the baseline and two priming conditions. We observed that the domain-specific language WM-PAD and domain-general WM-PAD were significantly correlated with general word-finding ability. The phonological mechanism, not the semantic mechanism, in word-finding ability was significantly correlated with the domain-specific WM-PAD. In contrast, all behavioral measures of the conditions in the picture priming task were significantly associated with chronological age. The results suggest that chronological aging and WM aging have differential effects on language-related word retrieval functions, and support that cognitive alterations in word-finding functions involve not only the domain-specific processing within the frontotemporal language network but also the domain-general processing of executive functions in the fronto-parieto-occipital (or multi-demand) network. The findings further indicate that the phonological aspect of word retrieval ability declines as cerebral WM ages, whereas the semantic aspect is relatively resilient or unrelated to WM aging.

Whole Brain White Matter Tract Deviation and Idiosyncrasy From Normative Development in Autism and ADHD and Unaffected Siblings Link With Dimensions of Psychopathology and Cognition.

OBJECTIVE: The heterogeneity of autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) preclude definitive identification of neurobiomarkers and biological risks. High clinical overlap suggests multifaceted circuit-level alterations across diagnoses, which remains elusive. This study investigated whether individuals with ADHD or ASD and their unaffected siblings constitute a spectrum of neurodevelopmental conditions in terms of white matter etiology. METHODS: Sex-specific white matter tract normative development was modeled from diffusion MRI of 626 typically developing control subjects (ages 5-40 years; 376 of them male). Individualized metrics estimating white matter tract deviation from the age norm were derived for 279 probands with ADHD, 175 probands with ASD, and their unaffected siblings (ADHD, N=121; ASD, N=72). RESULTS: ASD and ADHD shared diffuse white matter tract deviations in the commissure and association tracts (rho=0.54; p<0.001), while prefrontal corpus callosum deviated more remarkably in ASD (effect size=-0.36; p<0.001). Highly correlated deviance patterns between probands and unaffected siblings were found in both ASD (rho=0.69; p<0.001) and ADHD (rho=0.51; p<0.001), but only unaffected sisters of ASD probands showed a potential endophenotype in long-range association fibers and projection fibers connecting prefrontal regions. ADHD and ASD shared significant white matter tract idiosyncrasy (rho=0.55; p<0.001), particularly in tracts connecting prefrontal regions, not identified in either sibling group. Canonical correlation analysis identified multiple dimensions of psychopathology/cognition across categorical entities; autistic, visual memory, intelligence/planning/inhibition, nonverbal-intelligence/attention, working memory/attention, and set-shifting/response-variability were associated with distinct sets of white matter tract deviations. CONCLUSIONS: When conceptualizing neurodevelopmental disorders as white matter tract deviations from normative patterns, ASD and ADHD are more alike than different. The modest white matter tract alterations in siblings suggest potential endophenotypes in these at-risk populations. This study further delineates brain-driven dimensions of psychopathology/cognition, which may help clarify within-diagnosis heterogeneity and high between-diagnosis co-occurrence.

Microstructural differences in white matter tracts across middle to late adulthood: a diffusion MRI study on 7167 UK Biobank participants.

White matter fiber tracts demonstrate heterogeneous vulnerabilities to aging effects. Here, we estimated age-related differences in tract properties using UK Biobank diffusion magnetic resonance imaging data of 7167 47- to 76-year-old neurologically healthy people (3368 men and 3799 women). Tract properties in terms of generalized fractional anisotropy, axial diffusivity, radial diffusivity, and mean diffusivity were sampled on 76 fiber tracts; for each tract, age-related differences were estimated by fitting these indices against age in a linear model. This cross-sectional study demonstrated 4 age-difference patterns. The dominant pattern was lower generalized fractional anisotropy and higher axial diffusivity, radial diffusivity, and mean diffusivity with age, constituting 45 of 76 tracts, mostly involving the association, projection, and commissure fibers connecting the prefrontal lobe. The other 3 patterns constituted only 14 tracts, with atypical age differences in diffusion indices, and mainly involved parietal, occipital, and temporal cortices. By analyzing the large volume of diffusion magnetic resonance imaging data available from the UK Biobank, the study has provided a detailed description of heterogeneous age-related differences in tract properties over the whole brain which generally supports the myelodegeneration hypothesis.

Generalization of diffusion magnetic resonance imaging-based brain age prediction model through transfer learning.

Brain age prediction models using diffusion magnetic resonance imaging (dMRI) and machine learning techniques enable individual assessment of brain aging status in healthy people and patients with brain disorders. However, dMRI data are notorious for high intersite variability, prohibiting direct application of a model to the datasets obtained from other sites. In this study, we generalized the dMRI-based brain age model to different dMRI datasets acquired under different imaging conditions. Specifically, we adopted a transfer learning approach to achieve domain adaptation. To evaluate the performance of transferred models, brain age prediction models were constructed using a large dMRI dataset as the source domain, and the models were transferred to three target domains with distinct acquisition scenarios. The experiments were performed to investigate (1) the tuning data size needed to achieve satisfactory performance for brain age prediction, (2) the feature types suitable for different dMRI acquisition scenarios, and (3) performance of the transfer learning approach compared with the statistical covariate approach. By tuning the models with relatively small data size and certain feature types, optimal transferred models were obtained with significantly improved prediction performance in all three target cohorts (p â€‹< â€‹0.001). The mean absolute error of the predicted age was reduced from 13.89 to 4.78 years in Cohort 1, 8.34 to 5.35 years in Cohort 2, and 8.74 to 5.64 years in Cohort 3. The test-retest reliability of the transferred model was verified using dMRI data acquired at two timepoints (intraclass correlation coefficient â€‹= â€‹0.950). Clinical sensitivity of the brain age prediction model was investigated by estimating the brain age in patients with schizophrenia. The prediction made by the transferred model was not significantly different from that made by the reference model. Both models predicted significant brain aging in patients with schizophrenia as compared with healthy controls (p â€‹< â€‹0.001); the predicted age difference of the transferred model was 4.63 and 0.26 years for patients and controls, respectively, and that of the reference model was 4.39 and -0.09 years, respectively. In conclusion, transfer learning approach is an efficient way to generalize the dMRI-based brain age prediction model. Appropriate transfer learning approach and suitable tuning data size should be chosen according to different dMRI acquisition scenarios.

Fluid intelligence is associated with cortical volume and white matter tract integrity within multiple-demand system across adult lifespan.

BACKGROUND: Fluid intelligence (Gf) is the innate ability of an individual to respond to complex and unexpected situations. Although some studies have considered that the multiple-demand (MD) system of the brain was the biological foundation for Gf, further characterization of their relationships in the context of aging is limited. The present study hypothesized that the structural metrics of the MD system, including cortical thickness, cortical volumes, and white matter (WM) tract integrity, was the brain correlates for Gf across the adult life span. Partial correlation analysis was performed to investigate whether the MD system could still explain Gf independent of the age effect. Moreover, the partial correlations between Gf and left/right structural metrics within the MD regions were compared to test whether the correlations displayed distinct lateralization. METHODS: The participants were recruited from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) databank, comprising the images of 603 healthy participants aged 18-88 years acquired on a 3-T system. The MRI data included high-resolution T1-weighted and diffusion-weighted images, from which gray matter and WM structural metrics of the MD system were analyzed, respectively. The structural metrics of gray matter were quantified in terms of cortical volume/thickness of five pairs of cortical regions, and those of WM were quantified in terms of the mean axial diffusivity (DA), radial diffusivity (DR), mean diffusivity (DM), and generalized fractional anisotropy (GFA) on five pairs of tracts. Partial correlation controlling for age and sex effects, was performed to investigate the associations of Gf scores with the mean DA, DR, DM and GFA of all tracts in the MD system, those of left and right hemispheric tracts, and those of each tract. Fisher's exact test was used to compare the partial correlations between left and right MD regions. RESULTS: The linear relationship between cortical volumes and Gf was evident across all levels of the MD system even after controlling for age and sex. For the WM integrity, diffusion indices including DA, DR, DM and GFA displayed linear relationships with Gf scores at various levels of the MD system. Among the 10 WM tracts connecting the MD regions, bilateral superior longitudinal fasciculus I and bilateral frontal aslant tracts exhibited the strongest and significant associations. Our results did not show significant inter-hemispheric differences in the associations between structural metrics of the MD system and Gf. CONCLUSION: Our results demonstrate significant associations between Gf and both cortical volumes and tract integrity of the MD system across the adult lifespan in a population-based cohort. We found that the association remained significant in the entire adult lifespan despite simultaneous decline of Gf and the MD system. Our results suggest that the MD system might be a structural underpinning of Gf and support the fronto-parietal model of cognitive aging. However, we did not find hemispheric differences in the Gf-MD correlations, not supporting the hemi-aging hypothesis.

Premature white matter aging in patients with right mesial temporal lobe epilepsy: A machine learning approach based on diffusion MRI data.

Brain age prediction based on machine learning has been applied to various neurological diseases to discover its clinical values. By this innovative approach, it has been reported that the patients with refractory epilepsy had premature brain aging. Of refractory epilepsy, right and left subtypes of mesial temporal lobe epilepsy (MTLE) are the most common forms and exhibit distinct patterns in white matter alterations. So far, it is unclear whether these two subtypes of MTLE would have difference in white matter aging due to distinct white matter alterations. To address this issue, a machine learning based brain age model using diffusion MRI data was established to investigate biological age of white matter tracts. All diffusion MRI datasets were obtained from the same 3-Tesla MRI scanner. To build the brain age prediction model, diffusion MRI datasets of 300 healthy participants were processed to extract age-relevant diffusion indices from 76 major white matter tracts. The extracted diffusion indices underwent Gaussian process regression to build the prediction model for white matter brain age. The model was validated in an independent testing set (N = 40) to ensure no overfitting of the model. The model was then applied to patients with right and left MTLE and matched controls (right MTLE: N = 17, left MTLE: N = 18, controls: N = 37), and predicted age difference (PAD) was obtained by calculating the difference between each individual's predicted brain age and chronological age. The higher PAD score indicated older brain age. The results showed that right MTLE exhibited older predicted brain age than the other two groups (PAD of right MTLE = 10.9 years [p < 0.05 against left MTLE; p < 0.001 against control]; PAD of left MTLE = 2.2 years [p > 0.1 against control]; PAD of controls = 0.82 years). Patients with right and left MTLE showed strong correlations of the PAD scores with age of onset and duration of illness, but both groups showed opposite directions of correlations. In right MTLE, positive correlation of PAD with seizure frequency was found, and the right uncinate fasciculus was the most attributable tract to the increase in PAD. In conclusion, the present study found that patients with right MTLE exhibited premature white matter brain aging and their PAD scores were correlated with seizure frequency. Therefore, PAD is a potentially useful indicator of white matter impairment and disease severity in patients with right MTLE.

Doped graphene for metal-free catalysis.

Graphene has attracted increasing attention in different scientific fields including catalysis. Via modification with foreign metal-free elements such as nitrogen, its unique electronic and spin structure can be changed and these doped graphene sheets have been successfully employed in some catalytic reactions recently, showing them to be promising catalysts for a wide range of reactions. In this review, we summarize the recent advancements of these new and interesting catalysts, with an emphasis on the universal origin of their catalytic mechanisms. We are full of hope for future developments, such as more precisely controlled doping methods, atom-scale surface characterization technology, generating more active catalysts via doping, and finding wide applications in many different fields.

[Study of new XeCl laser with short duration time and preionzation at high pressure].

New short duration time XeCI excimer laser has been generated at high pressure within a large volume in order to apply it to he interaction between laser and material, and material plasma study. The laser spectrum exhibits two laser lines at 307.98 and 308.19 nm, which is realized in the proportion of HCl:Xe: He = 0.1% :1% : 98.9% through UV preionization. Theoretic analysis indicated that the maximum intensity loop is B to X grade. Not only UV preionization, glow discharge and the calculation of dynamic equation, but also the laser spectrum and pulse duration time measurement were carried out. It is shown that the duration time decreases and pulse energy rises with the increase in the pressure and discharge voltage. The minimum duration time exceeds 13 ns, the pulse energy is 450 mJ, and the beam divergence angle is 3 mrad.