Grey-matter network disintegration as predictor of cognitive and motor function with aging

Deterioration of fine motor skills and functional disability in patients with moderate-to-advanced Parkinson disease: A longitudinal follow-up study

INTRODUCTION

Parkinson disease (PD) caused substantially disability. The impairment of fine motor skills (FMSs) is correlated with the severity of functional disability (FD) cross-sectionally in people with PD (PwP). The present study investigated the decline in FMSs and the predictive value of baseline FMSs for the progression of FD.

METHODS

People with moderate-to-advanced PD who received two evaluations within 1-5 years were identified from the Taiwan Data Bank of Persons with Disability database. The World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) was used to evaluate FD, and FMSs including pen-holding, buttoning, and knotting were assessed.

RESULTS

Our study included 2,271 people with moderate-to-advanced PD. We observed annual progression of FD in each domain of the WHODAS 2.0, with no difference between the sexes. The most significant correlation between FD and FMSs was that of decline in buttoning ability and deterioration of summary WHODAS 2.0 scores. Deterioration in FD across all domains of WHODAS 2.0 was associated with at least one FMS. The extent of disability in all three types of FMS at baseline was also correlated with deterioration of motility. Additionally, baseline disability in buttoning was significantly correlated with cognitive decline, and disability in knotting was significantly associated with the progression of FD.

CONCLUSION

FMSs may be reliable markers for further FD, particularly in the areas of cognition, motility, and life activity. Because of the significant FD observed in people with moderate-to-advanced PD, the availability of predictors is essential for applying precautionary measures and providing appropriate treatment.

Choir singing is associated with enhanced structural connectivity across the adult lifespan

The global ageing of populations calls for effective, ecologically valid methods to support brain health across adult life. Previous evidence suggests that music can promote white matter (WM) microstructure and grey matter (GM) volume while supporting auditory and cognitive functioning and emotional well-being as well as counteracting age-related cognitive decline. Adding a social component to music training, choir singing is a popular leisure activity among older adults, but a systematic account of its potential to support healthy brain structure, especially with regard to ageing, is currently missing. The present study used quantitative anisotropy (QA)-based diffusion MRI connectometry and voxel-based morphometry to explore the relationship of lifetime choir singing experience and brain structure at the whole-brain level. Cross-sectional multiple regression analyses were carried out in a large, balanced sample (N = 95; age range 21-88) of healthy adults with varying levels of choir singing experience across the whole age range and within subgroups defined by age (young, middle-aged, and older adults). Independent of age, choir singing experience was associated with extensive increases in WM QA in commissural, association, and projection tracts across the brain. Corroborating previous work, these overlapped with language and limbic networks. Enhanced corpus callosum microstructure was associated with choir singing experience across all subgroups. In addition, choir singing experience was selectively associated with enhanced QA in the fornix in older participants. No associations between GM volume and choir singing were found. The present study offers the first systematic account of amateur-level choir singing on brain structure. While no evidence for counteracting GM atrophy was found, the present evidence of enhanced structural connectivity coheres well with age-typical structural changes. Corroborating previous behavioural studies, the present results suggest that regular choir singing holds great promise for supporting brain health across the adult life span.

Neuronal ageing is promoted by the decay of the microtubule cytoskeleton

Natural ageing is accompanied by a decline in motor, sensory, and cognitive functions, all impacting quality of life. Ageing is also the predominant risk factor for many neurodegenerative diseases, including Parkinson's disease and Alzheimer's disease. We need to therefore gain a better understanding of the cellular and physiological processes underlying age-related neuronal decay. However, gaining this understanding is a slow process due to the large amount of time required to age mammalian or vertebrate animal models. Here, we introduce a new cellular model within the Drosophila brain, in which we report classical ageing hallmarks previously observed in the primate brain. These hallmarks include axonal swellings, cytoskeletal decay, a reduction in axonal calibre, and morphological changes arising at synaptic terminals. In the fly brain, these changes begin to occur within a few weeks, ideal to study the underlying mechanisms of ageing. We discovered that the decay of the neuronal microtubule (MT) cytoskeleton precedes the onset of other ageing hallmarks. We showed that the MT-binding factors Tau, EB1, and Shot/MACF1, are necessary for MT maintenance in axons and synapses, and that their functional loss during ageing triggers MT bundle decay, followed by a decline in axons and synaptic terminals. Furthermore, genetic manipulations that improve MT networks slowed down the onset of neuronal ageing hallmarks and confer aged specimens the ability to outperform age-matched controls. Our work suggests that MT networks are a key lesion site in ageing neurons and therefore the MT cytoskeleton offers a promising target to improve neuronal decay in advanced age.

Age- and sex-related differences in baboon (Papio anubis) gray matter covariation

Age-related changes in cognition, brain morphology, and behavior are exhibited in several primate species. Baboons, like humans, naturally develop Alzheimer's disease-like pathology and cognitive declines with age and are an underutilized model for studies of aging. To determine age-related differences in gray matter covariation of 89 olive baboons (Papio anubis), we used source-based morphometry (SBM) to analyze data from magnetic resonance images. We hypothesized that we would find significant age effects in one or more SBM components, particularly those which include regions influenced by age in humans and other nonhuman primates (NHPs). A multivariate analysis of variance revealed that individual weighted gray matter covariation scores differed across the age classes. Elderly baboons contributed significantly less to gray matter covariation components including the brainstem, superior parietal cortex, thalamus, and pallidum compared to juveniles, and middle and superior frontal cortex compared to juveniles and young adults (p < 0.05). Future studies should examine the relationship between the changes in gray matter covariation reported here and age-related cognitive decline.

Optimizing the Effect of tDCS on Motor Sequence Learning in the Elderly

One of the most visible effects of aging, even in healthy, normal aging, is a decline in motor performance. The range of strategies applicable to counteract this deterioration has increased. Transcranial direct current stimulation (tDCS), a non-invasive brain stimulation technique that can promote neuroplasticity, has recently gained attention. However, knowledge about optimized tDCS parameters in the elderly is limited. Therefore, in this study, we investigated the effect of different anodal tDCS intensities on motor sequence learning in the elderly. Over the course of four sessions, 25 healthy older adults (over 65 years old) completed the Serial Reaction Time Task (SRTT) while receiving 1, 2, or 3 mA of anodal or sham stimulation over the primary motor cortex (M1). Additionally, 24 h after stimulation, motor memory consolidation was assessed. The results confirmed that motor sequence learning in all tDCS conditions was maintained the following day. While increased anodal stimulation intensity over M1 showed longer lasting excitability enhancement in the elderly in a prior study, the combination of higher intensity stimulation with an implicit motor learning task showed no significant effect. Future research should focus on the reason behind this lack of effect and probe alternative stimulation protocols.

Horizons in Human Aging Neuroscience: From Normal Neural Aging to Mental (Fr)Agility

While aging is an important risk factor for neurodegenerative disorders such as Alzheimer's disease and Parkinson's disease, age-related cognitive decline can also manifest without apparent neurodegenerative changes. In this review, we discuss molecular, cellular, and network changes that occur during normal aging in the absence of neurodegenerative disease. Emerging findings reveal that these changes include metabolic alterations, oxidative stress, DNA damage, inflammation, calcium dyshomeostasis, and several other hallmarks of age-related neural changes that do not act on their own, but are often interconnected and together may underlie age-related alterations in brain plasticity and cognitive function. Importantly, age-related cognitive decline may not be reduced to a single neurobiological cause, but should instead be considered in terms of a densely connected system that underlies age-related cognitive alterations. We speculate that a decline in one hallmark of neural aging may trigger a decline in other, otherwise thus far stable subsystems, thereby triggering a cascade that may at some point also incur a decline of cognitive functions and mental well-being. Beyond studying the effects of these factors in isolation, considerable insight may be gained by studying the larger picture that entails a representative collection of such factors and their interactions, ranging from molecules to neural networks. Finally, we discuss some potential interventions that may help to prevent these alterations, thereby reducing cognitive decline and mental fragility, and enhancing mental well-being, and healthy aging.

Alterations in degree centrality and cognitive function in breast cancer patients after chemotherapy

The goal of this study was to determine the presence or absence of persistent functional impairments in specific brain regions in breast cancer patients during the recovery period after chemotherapy. We calculated degree centrality (DC) and explored the correlation between brain changes and cognitive scores in 29 female patients with breast cancer who had completed chemotherapy within 1-6 years (C + group) and in 28 age-matched patients with breast cancer who did not receive chemotherapy (C- group). All patients underwent rs-fMRI and cognitive testing. Differences in brain functional activity were explored using DC parameters. Correlations between brain features and cognitive scores were analyzed via correlation analysis. Compared with the C- group, the C + group obtained significantly lower motor and cognitive subscores on the Fatigue Scale for Motor and Cognitive Functions and four subscale scores of the Functional Assessment of Cancer Therapy-Cognitive Function (P < 0.05). Furthermore, the C + group exhibited a significantly higher DC z-score (zDC) in the right superior temporal gyrus and left postcentral gyrus (P < 0.01, FWE-corrected), and a lower zDC in the left caudate nucleus (P < 0.01, FWE-corrected). We found a positive correlation between digit symbol test (DST) scores and zDC values in the right superior temporal gyrus (r = 0.709, P < 0.001), and a negative correlation between DST scores and zDC values in the right angular gyrus (r = -0.784, P < 0.001) and left superior parietal gyrus (r = -0.739, P < 0.001). Chemotherapy can cause abnormal brain activity and cognitive decline in patients with breast cancer, and these effects are likely to persist. DC can be used as an imaging marker for chemotherapy-related cognitive impairment after chemotherapy in breast cancer patients.

Associations between childhood chronic stress and dynamic functional connectivity in drug-naïve, first-episode adolescent MDD

BACKGROUND

We explored the associations between chronic stress and dynamic working patterns of the whole brain using resting state MRI data in drug-naïve, first-episode adolescents with major depressive disorder (MDD).

METHODS

We compared dynamic functional connectivity (dyn-FC) and screen out networks with difference in whole brain between 45 healthy controls (HC) and 60 adolescent MDD patients using dynamic independent components analysis. In each of these networks with difference between groups, hub brain regions were selected as functionally connected to more than 30 brain regions at the same time. Then we extracted the dyn-FC coefficients of each hub brain region with other brain regions in each component at different time points and calculated the average value of the entire scan time. Finally, we explored correlations between these average values of the entire scan time and scores on the Childhood Chronic Stress Questionnaire (CCSQ).

RESULTS

We found three networks as well as some hub brain regions with different dyn-FC patterns between adolescent MDD and HC. Scores on the CCSQ were found to correlate with dynamic FC between hub brain areas and certain other brain areas in MDD patients.

LIMITATIONS

our cross-sectional study design does not allow us to speculate about causality between chronic stress and depression. Prospective cohort studies should explore in detail how the changes in dynamic FC appear and evolve during MDD.

CONCLUSIONS

Chronic stress is related with the brain dynamic working patterns in adolescent MDD.

Measuring age differences in executive control using rapid motor decisions in a robotic object hit and avoid task

Age-related declines in executive control are commonly assessed with neuropsychological tests that also rely on sensory and motor processes that are not typically measured in those tasks. It is therefore difficult to isolate the cognitive contributions from sensorimotor contributions to performance impairments. Rapid motor decision-making tasks may also be sensitive to age differences in executive control but allow for the measurement of sensorimotor contributors to task performance. Recently developed object hit (OH) and object hit and avoid (OHA) tasks using a robotic manipulandum are sensitive to motor and cognitive aspects of performance in stroke and Parkinson's disease. However, the impact of healthy aging, and the specific cognitive mechanisms involved in these tasks has not been assessed. We administered the OH and OHA tasks to 77 younger and 59 healthy older adults to evaluate the relative age differences in the perceptual-motor/sensory, movement coordination, and cognitive measures of performance. The Trail Making Test (TMT) Parts A and B were administered to assess the extent to which the cognitive contributors to OHA task performance are associated with executive functioning. After controlling for hand movement speed, age differences were largest for cognitive measures, with smaller differences in perceptual-motor speed and sensory measures, and little differences in bimanual and spatial coordination measures of performance. The cognitive measures were associated with executive functioning measures from the TMT task. These findings provide evidence that rapid motor decision-making tasks are sensitive to age differences in executive control and can isolate the cognitive from the sensorimotor contributions to task performance. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Disability in multiple sclerosis is related to thalamic connectivity and cortical network atrophy

BACKGROUND

Thalamic atrophy is proposed to be a major predictor of disability progression in multiple sclerosis (MS), while thalamic function remains understudied.

OBJECTIVES

To study how thalamic functional connectivity (FC) is related to disability and thalamic or cortical network atrophy in two large MS cohorts.

METHODS

Structural and resting-state functional magnetic resonance imaging (fMRI) was obtained in 673 subjects from Amsterdam (MS: N = 332, healthy controls (HC): N = 96) and Graz (MS: N = 180, HC: N = 65) with comparable protocols, including disability measurements in MS (Expanded Disability Status Scale, EDSS). Atrophy was measured for the thalamus and seven well-recognized resting-state networks. Static and dynamic thalamic FC with these networks was correlated with disability. Significant correlates were included in a backward multivariate regression model.

RESULTS

Disability was most strongly related (adjusted R² = 0.57, p < 0.001) to higher age, a progressive phenotype, thalamic atrophy and increased static thalamic FC with the sensorimotor network (SMN). Static thalamus-SMN FC was significantly higher in patients with high disability (EDSS ⩾ 4) and related to network atrophy but not thalamic atrophy or lesion volumes.

CONCLUSION

The severity of disability in MS was related to increased static thalamic FC with the SMN. Thalamic FC changes were only related to cortical network atrophy, but not to thalamic atrophy.

Age-related disintegration in functional connectivity: Evidence from Reference Ability Neural Network (RANN) cohort

Aging is typically marked by a decline in some domains of cognition. Some theories have linked this decline to a reduction in distinctiveness of processing at the neural level that in turn leads to cognitive decline. Increasing correlations with age among tasks formerly considered independent have been posited, supporting dedifferentiation, although results have been mixed. An alternative view is that tasks become more, and not less, independent of one another with increasing age, suggesting age-related differentiation, or what has also been termed disintegration. In the current study, we investigated if the aging process leads to a loss of behavioral and neural specificity within latent cognitive abilities. To this end, we tested 287 participants (20-80 years) on a battery of 12 in-scanner tests, three each tapping one of four reference abilities. We performed between-task correlations within domain (pertaining to convergent validity), and between domain (pertaining to discriminant validity) at both the behavioral and neural level and found that neural convergent validity was positively associated with behavioral convergent validity. In examining neural validity across the lifespan, we found significant reductions in both within- and between-domain task correlations, with a significant decrease in construct validity (convergent or discriminant) with age. Furthermore, the effect of age on total cognition was significantly mediated by neural construct validity. Taken together, contrary to a hypothesis of dedifferentiation, these correlation reductions suggest that tasks indeed become more independent with advancing age, favoring a differentiation/disintegration hypothesis of aging.

Differential vulnerability of the cerebellum in healthy ageing and Alzheimer's disease

Recent findings challenge the prior notion that the cerebellum remains unaffected by Alzheimer's disease (AD). Yet, it is unclear whether AD exacerbates age-related cerebellar grey matter decline or engages distinct structural and functional territories. We performed a meta-analysis of cerebellar grey matter loss in normal ageing and AD. We mapped voxels with structural decline onto established brain networks, functional parcellations, and along gradients that govern the functional organisation of the cerebellum. Importantly, these gradients track continuous changes in cerebellar specialisation providing a more nuanced measure of the functional profile of regions vulnerable to ageing and AD. Gradient 1 progresses from motor to cognitive territories; Gradient 2 isolates attentional processing; Gradient 3 captures lateralisation differences in cognitive functions. We identified bilateral and right-lateralised posterior cerebellar atrophy in ageing and AD, respectively. Age- and AD-related structural decline only showed partial spatial overlap in right lobule VI/Crus I. Despite the seemingly distinct patterns of AD- and age-related atrophy, the functional profiles of these regions were similar. Both participate in the same macroscale networks (default mode, frontoparietal, attention), support executive functions and language processing, and did not exhibit a difference in relative positions along Gradients 1 or 2. However, Gradient 3 values were significantly different in ageing vs. AD, suggesting that the roles of left and right atrophied cerebellar regions exhibit subtle functional differences despite their membership in similar macroscale networks. These findings provide an unprecedented characterisation of structural and functional differences and similarities in cerebellar grey matter loss between normal ageing and AD.

Brain Structural-Behavioral Correlates Underlying Grooved Pegboard Test Performance Across Lifespan

This study aims to provide the first brain structural-behavioral correlates underlying age differences in Grooved Pegboard Test (GPT) performance after adjusting for gender, education, quality of life, mental health, and anthropometric variables. We report the data of 210 right-handed participants (20- to 80-year old), who underwent behavioral assessments including GPT, Trail Making Test, and stop-signal task. Magnetic resonance images were acquired to investigate the structural-behavioral correlates of age differences in GPT performance. Age differences in GPT performance were positively associated with visuomotor tracking performance and negatively associated with widespread brain structural measures, including white matter tracts (e.g., commissure, radiation, and association fibers) and gray matter regions in frontal and cingulate regions, parietal, and temporal lobes.

Large-Scale Structural Covariance Networks Predict Age in Middle-to-Late Adulthood: A Novel Brain Aging Biomarker

The aging process is accompanied by changes in the brain's cortex at many levels. There is growing interest in summarizing these complex brain-aging profiles into a single, quantitative index that could serve as a biomarker both for characterizing individual brain health and for identifying neurodegenerative and neuropsychiatric diseases. Using a large-scale structural covariance network (SCN)-based framework with machine learning algorithms, we demonstrate this framework's ability to predict individual brain age in a large sample of middle-to-late age adults, and highlight its clinical specificity for several disease populations from a network perspective. A proposed estimator with 40 SCNs could predict individual brain age, balancing between model complexity and prediction accuracy. Notably, we found that the most significant SCN for predicting brain age included the caudate nucleus, putamen, hippocampus, amygdala, and cerebellar regions. Furthermore, our data indicate a larger brain age disparity in patients with schizophrenia and Alzheimer's disease than in healthy controls, while this metric did not differ significantly in patients with major depressive disorder. These findings provide empirical evidence supporting the estimation of brain age from a brain network perspective, and demonstrate the clinical feasibility of evaluating neurological diseases hypothesized to be associated with accelerated brain aging.

Hand Fine Motor Skill Disability Correlates with Cognition in Patients with Moderate-to-Advanced Parkinson's Disease

In older individuals, hand fine motor skill disability is associated with cognitive levels. Similarly, patients with moderate-to-advanced Parkinson's disease (PD) often have cognitive dysfunction. Here, we investigated the association between hand fine motor skill and cognitive dysfunction in patients with moderate-to-advanced PD. Moderate and advanced PD patients with and without dementia were identified from the Taiwan Data Bank of Persons with Disability. Hand fine motor capacities, namely pen holding, buttoning, and knotting, were assessed with the World Health Organization Disability Assessment Schedule 2.0. Statistical analyses were performed on Statistical Analysis System (SAS) and a p value of <0.05 was considered significant. In total, 3440 patients with PD were enrolled, of which 612 had dementia, exhibiting significant disability in all three tasks. After adjustments for age, sex, and PD severity, pen holding and knotting were significantly associated with PD dementia. The presence of any disability in either task was not only sensitive to the presence of dementia but also associated with cognitive disability in moderate and advanced PD patients without dementia. In conclusion, hand fine motor skill disability was associated with cognitive disability in patients with moderate-to-advanced PD. These simple hand fine motor skills may thus be applicable in screening tests for the early identification of cognitive dysfunction in patients with moderate-to-advanced PD.

Functional Brain Network Connectivity Patterns Associated With Normal Cognition at Old-Age, Local β-amyloid, Tau, and APOE4

Background: Integrity of functional brain networks is closely associated with maintained cognitive performance at old age. Consistently, both carrier status of Apolipoprotein E ε4 allele (APOE4), and age-related aggregation of Alzheimer’s disease (AD) pathology result in altered brain network connectivity. The posterior cingulate and precuneus (PCP) is a node of particular interest due to its role in crucial memory processes. Moreover, the PCP is subject to the early aggregation of AD pathology. The current study aimed at characterizing brain network properties associated with unimpaired cognition in old aged adults. To determine the effects of age-related brain change and genetic risk for AD, pathological proteins β-amyloid and tau were measured by Positron-emission tomography (PET), PCP connectivity as a proxy of cognitive network integrity, and genetic risk by APOE4 carrier status.

Methods: Fifty-seven cognitively unimpaired old-aged adults (MMSE = 29.20 ± 1.11; 73 ± 8.32 years) were administered 11C Pittsburgh Compound B and 18F Flutemetamol PET for assessing β-amyloid, and 18F AV-1451 PET for tau. Individual functional connectivity seed maps of the PCP were obtained by resting-state multiband BOLD functional MRI at 3-Tesla for increased temporal resolution. Voxelwise correlations between functional connectivity, β-amyloid- and tau-PET were explored by Biological Parametric Mapping (BPM).

Results: Local β-amyloid was associated with increased connectivity in frontal and parietal regions of the brain. Tau was linked to increased connectivity in more spatially distributed clusters in frontal, parietal, occipital, temporal, and cerebellar regions. A positive interaction was observable for APOE4 carrier status and functional connectivity with brain regions characterized by increased local β-amyloid and tau tracer retention.

Conclusions: Our data suggest an association between spatially differing connectivity systems and local β-amyloid, and tau aggregates in cognitively normal, old-aged adults, which is moderated by APOE4. Additional longitudinal studies may determine protective connectivity patterns associated with healthy aging trajectories of AD-pathology aggregation.

Morphometric network differences in ageing versus Alzheimer's disease dementia

Age being the main risk factor for Alzheimer's disease, it is particularly challenging to disentangle structural changes related to normal brain ageing from those specific to Alzheimer's disease. Most studies aiming to make this distinction focused on older adults only and on a priori anatomical regions. Drawing on a large, multi-cohort dataset ranging from young adults (n = 468; age range 18-35 years), to older adults with intact cognition (n = 431; age range 55-90 years) and with Alzheimer's disease (n = 50 with late mild cognitive impairment and 71 with Alzheimer's dementia, age range 56-88 years), we investigated grey matter organization and volume differences in ageing and Alzheimer's disease. Using independent component analysis on all participants' structural MRI, we first derived morphometric networks and extracted grey matter volume in each network. We also derived a measure of whole-brain grey matter pattern organization by correlating grey matter volume in all networks across all participants from the same cohort. We used logistic regressions and receiver operating characteristic analyses to evaluate how well grey matter volume in each network and whole-brain pattern could discriminate between ageing and Alzheimer's disease. Because increased heterogeneity is often reported as one of the main features characterizing brain ageing, we also evaluated interindividual heterogeneity within morphometric networks and across the whole-brain organization in ageing and Alzheimer's disease. Finally, to investigate the clinical validity of the different grey matter features, we evaluated whether grey matter volume or whole-brain pattern was related to clinical progression in cognitively normal older adults. Ageing and Alzheimer's disease contributed additive effects on grey matter volume in nearly all networks, except frontal lobe networks, where differences in grey matter were more specific to ageing. While no networks specifically discriminated Alzheimer's disease from ageing, heterogeneity in grey matter volumes across morphometric networks and in the whole-brain grey matter pattern characterized individuals with cognitive impairments. Preservation of the whole-brain grey matter pattern was also related to lower risk of developing cognitive impairment, more so than grey matter volume. These results suggest both ageing and Alzheimer's disease involve widespread atrophy, but that the clinical expression of Alzheimer's disease is uniquely associated with disruption of morphometric organization.

Gray Matter Covariance Networks as Classifiers and Predictors of Cognitive Function in Alzheimer's Disease

The study of shared variation in gray matter morphology may define neurodegenerative diseases beyond what can be detected from the isolated assessment of regional brain volumes. We, therefore, aimed to (1) identify SCNs (structural covariance networks) that discriminate between Alzheimer's disease (AD) patients and healthy controls (HC), (2) investigate their diagnostic accuracy in comparison and above established markers, and (3) determine if they are associated with cognitive abilities. We applied a random forest algorithm to identify discriminating networks from a set of 20 SCNs. The algorithm was trained on a main sample of 104 AD patients and 104 age-matched HC and was then validated in an independent sample of 28 AD patients and 28 controls from another center. Only two of the 20 SCNs contributed significantly to the discrimination between AD and controls. These were a temporal and a secondary somatosensory SCN. Their diagnostic accuracy was 74% in the original cohort and 80% in the independent samples. The diagnostic accuracy of SCNs was comparable with that of conventional volumetric MRI markers including whole brain volume and hippocampal volume. SCN did not significantly increase diagnostic accuracy beyond that of conventional MRI markers. We found the temporal SCN to be associated with verbal memory at baseline. No other associations with cognitive functions were seen. SCNs failed to predict the course of cognitive decline over an average of 18 months. We conclude that SCNs have diagnostic potential, but the diagnostic information gain beyond conventional MRI markers is limited.

Deep Learning and Multiplex Networks for Accurate Modeling of Brain Age

Recent works have extensively investigated the possibility to predict brain aging from T1-weighted MRI brain scans. The main purposes of these studies are the investigation of subject-specific aging mechanisms and the development of accurate models for age prediction. Deviations between predicted and chronological age are known to occur in several neurodegenerative diseases; as a consequence, reaching higher levels of age prediction accuracy is of paramount importance to develop diagnostic tools. In this work, we propose a novel complex network model for brain based on segmenting T1-weighted MRI scans in rectangular boxes, called patches, and measuring pairwise similarities using Pearson's correlation to define a subject-specific network. We fed a deep neural network with nodal metrics, evaluating both the intensity and the uniformity of connections, to predict subjects' ages. Our model reaches high accuracies which compare favorably with state-of-the-art approaches. We observe that the complex relationships involved in this brain description cannot be accurately modeled with standard machine learning approaches, such as Ridge and Lasso regression, Random Forest, and Support Vector Machines, instead a deep neural network has to be used.