Increase of posterior connectivity in aging within the Ventral Attention Network: A functional connectivity analysis using independent component analysis

Relationships between brain structure-function coupling in normal aging and cognition: A cross-ethnicity population-based study

Increased efforts in neuroscience seek to understand how macro-anatomical and physiological connectomes cooperatively work to generate cognitive behaviors. However, the structure-function coupling characteristics in normal aging individuals remain unclear. Here, we developed an index, the Coupling in Brain Structural connectome and Functional connectome (C-BSF) index, to quantify regional structure-function coupling in a large community-based cohort. C-BSF used diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI) data from the Polyvascular Evaluation for Cognitive Impairment and Vascular Events study (PRECISE) cohort (2007 individuals, age: 61.15 ± 6.49 years) and the Sydney Memory and Ageing Study (MAS) cohort (254 individuals, age: 83.45 ± 4.33 years). We observed that structure-function coupling was the strongest in the visual network and the weakest in the ventral attention network. We also observed that the weaker structure-function coupling was associated with increased age and worse cognitive level of the participant. Meanwhile, the structure-function coupling in the visual network was associated with the visuospatial performance and partially mediated the connections between age and the visuospatial function. This work contributes to our understanding of the underlying brain mechanisms by which aging affects cognition and also help establish early diagnosis and treatment approaches for neurological diseases in the elderly.

Alpha oscillations during visual selective attention are aberrant in youth and adults with cerebral palsy

Our understanding of the neurobiology underlying cognitive dysfunction in persons with cerebral palsy is very limited, especially in the neurocognitive domain of visual selective attention. This investigation utilized magnetoencephalography and an Eriksen arrow-based flanker task to quantify the dynamics underlying selective attention in a cohort of youth and adults with cerebral palsy (n = 31; age range = 9 to 47 yr) and neurotypical controls (n = 38; age range = 11 to 49 yr). The magnetoencephalography data were transformed into the time-frequency domain to identify neural oscillatory responses and imaged using a beamforming approach. The behavioral results indicated that all participants exhibited a flanker effect (greater response time for the incongruent compared to congruent condition) and that individuals with cerebral palsy were slower and less accurate during task performance. We computed interference maps to focus on the attentional component and found aberrant alpha (8 to 14 Hz) oscillations in the right primary visual cortices in the group with cerebral palsy. Alpha and theta (4 to 7 Hz) oscillations were also seen in the left and right insula, and these oscillations varied with age across all participants. Overall, persons with cerebral palsy exhibit deficiencies in the cortical dynamics serving visual selective attention, but these aberrations do not appear to be uniquely affected by age.

Exploration of resting-state brain functional connectivity as preclinical markers for arousal prediction in prolonged disorders of consciousness: A pilot study based on functional near-infrared spectroscopy

BACKGROUND

There is no diagnostic assessment procedure with moderate or strong evidence of use, and evidence for current means of treating prolonged disorders of consciousness (pDOC) is sparse. This may be related to the fact that the mechanisms of pDOC have not been studied deeply enough and are not clear enough. Therefore, the aim of this study was to explore the mechanism of pDOC using functional near-infrared spectroscopy (fNIRS) to provide a basis for the treatment of pDOC, as well as to explore preclinical markers for determining the arousal of pDOC patients.

METHODS

Five minutes resting-state data were collected from 10 pDOC patients and 13healthy adults using fNIRS. Based on the concentrations of oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) in the time series, the resting-state cortical brain functional connectivity strengths of the two groups were calculated, and the functional connectivity strengths of homologous and heterologous brain networks were compared at the sensorimotor network (SEN), dorsal attention network (DAN), ventral attention network (VAN), default mode network (DMN), frontoparietal network (FPN), and visual network (VIS) levels. Univariate binary logistic regression analyses were performed on brain networks with statistically significant differences to identify brain networks associated with arousal in pDOC patients. The receiver operating characteristic (ROC) curves were further analyzed to determine the cut-off value of the relevant brain networks to provide clinical biomarkers for the prediction of arousal in pDOC patients.

RESULTS

The results showed that the functional connectivity strengths of oxyhemoglobin (HbO)-based SEN∼SEN, VIS∼VIS, DAN∼DAN, DMN∼DMN, SEN∼VIS, SEN∼FPN, SEN∼DAN, SEN∼DMN, VIS∼FPN, VIS∼DAN, VIS∼DMN, HbR-based SEN∼SEN, and SEN∼DAN were significantly reduced in the pDOC group and were factors that could reflect the participants' state of consciousness. The cut-off value of resting-state functional connectivity strength calculated by ROC curve analysis can be used as a potential preclinical marker for predicting the arousal state of subjects.

CONCLUSION

Resting-state functional connectivity strength of cortical networks is significantly reduced in pDOC patients. The cut-off values of resting-state functional connectivity strength are potential preclinical markers for predicting arousal in pDOC patients.

Presurgical microstructural coherence predicts cognitive change for bariatric surgery patients

OBJECTIVE

This observational study examined the relationship between presurgical white matter microstructural coherence and cognitive change after weight loss. It was hypothesized that higher baseline fractional anisotropy (FA) would predict greater baseline and change cognition.

METHODS

A sample of 24 adults (BMI ≥ 35 kg/m2 ) underwent neuropsychological assessment at baseline and 12 weeks after bariatric surgery. A magnetic resonance imaging brain scan was administered at baseline and processed through Tract-Based Spatial Statistics to compute FA in white matter tracts of interest. Composite scores for attention, learning, processing speed, executive function, verbal fluency, working memory, and overall cognition were calculated.

RESULTS

As expected, FA in some tracts of interest was significantly (p < 0.05) positively associated with change in cognition. Inverse relationships were observed between baseline FA and presurgical cognition, which may be explained by increased medial and radial diffusivity and preserved axonal diffusivity. Cognition generally improved after surgery; however, relative but clinically nonsignificant deterioration was observed on learning measures. Poorer baseline cognitive performance was associated with greater postsurgical cognitive improvement.

CONCLUSIONS

Presurgical microstructural coherence is associated with magnitude of cognitive change after weight loss. An observed reduction in learning suggests that bariatric surgery may lead to negative outcomes in some cognitive domains, at least temporarily.

Connectome-based predictive models using resting-state fMRI for studying brain aging

Changes in the brain with age can provide useful information regarding an individual's chronological age. studies have suggested that functional connectomes identified via resting-state functional magnetic resonance imaging (fMRI) could be a powerful feature for predicting an individual's age. We applied connectome-based predictive modeling (CPM) to investigate individual chronological age predictions via resting-state fMRI using open-source datasets. The significant feature for age prediction was confirmed in 168 subjects from the Southwest University Adult Lifespan Dataset. The higher contributing nodes for age production included a positive connection from the left inferior parietal sulcus and a negative connection from the right middle temporal sulcus. On the network scale, the subcortical-cerebellum network was the dominant network for age prediction. The generalizability of CPM, which was constructed using the identified features, was verified by applying this model to independent datasets that were randomly selected from the Autism Brain Imaging Data Exchange I and the Open Access Series of Imaging Studies 3. CPM via resting-state fMRI is a potential robust predictor for determining an individual's chronological age from changes in the brain.

Comparison of machine learning models for predicting the risk of breast cancer-related lymphedema in Chinese women

Objective

Predictive models for the occurrence of cancer symptoms by using machine learning (ML) algorithms could be used to aid clinical decision-making in order to enhance the quality of cancer care. This study aimed to develop and validate a selection of classification models that used ML algorithms to predict the occurrence of breast cancer-related lymphedema (BCRL) among Chinese women.

Methods

This was a retrospective cohort study of consecutive cases that had been diagnosed with breast cancer, stages I-IV. Forty-eight variables were grouped into five feature sets. Five classification models with ML algorithms were developed, and the models' performance and the variables’ relative importance were assessed accordingly.

Results

Of 370 eligible female participants, 91 had BCRL (24.6%). The mean age of this study sample was 49.89 (SD ​= ​7.45). All participants had had breast cancer surgery, and more than half of them had had a modified radical mastectomy (n ​= ​206, 55.5%). The mean follow-up time after breast cancer surgery was 28.73 months (SD ​= ​11.71). Most of the tumors were either stage I (n ​= ​49, 31.2%) or stage II (n ​= ​252, 68.1%). More than half of the sample had had postoperative chemotherapy (n ​= ​227, 61.4%). Overall, the logistic regression model achieved the best performance in terms of accuracy (91.6%), precision (82.1%), and recall (91.4%) for BCRL. Although this study included 48 predicting variables, we found that the five models required only 22 variables to achieve predictive performance. The most important variable was the number of positive lymph nodes, followed in descending order by the BCRL occurring on the same side as the surgery, a history of sentinel lymph node biopsy, a dietary preference for meat and fried food, and an exercise frequency of less than three times per week. These factors were the most influential predictors for enhancing the ML models’ performance.

Conclusions

This study found that in the ML training dataset, the multilayer perceptron model and the logistic regression model were the best discrimination models for predicting the outcome of BCRL, and the k-nearest neighbors and support vector machine models demonstrated good calibration performance in the ML validation dataset. Future research will need to use large-sample datasets to establish a more robust ML model for predicting BCRL deeply and reliably.

Still Wanting to Win: Reward System Stability in Healthy Aging

Healthy aging is accompanied by multi-faceted changes. Especially within the brain, healthy aging exerts substantial impetus on core parts of cognitive and motivational networks. Rewards comprise basic needs, such as food, sleep, and social contact. Thus, a functionally intact reward system remains indispensable for elderly people to cope with everyday life and adapt to their changing environment. Research shows that reward system function is better preserved in the elderly than most cognitive functions. To investigate the compensatory mechanisms providing reward system stability in aging, we employed a well-established reward paradigm (Monetary Incentive Delay Task) in groups of young and old participants while undergoing EEG measurement. As a new approach, we applied EEG connectivity analyses to assess cortical reward-related network connectivity. At the behavioral level, our results confirm that the function of the reward system is preserved in old age. The mechanisms identified for maintaining reward system function in old age do not fit into previously described models of cognitive aging. Overall, older adults exhibit lower reward-related connectivity modulation, higher reliance on posterior and right-lateralized brain areas than younger adults, and connectivity modulation in the opposite direction than younger adults, with usually greater connectivity during non-reward compared to reward conditions. We believe that the reward system has unique compensatory mechanisms distinct from other cognitive functions, probably due to its etymologically very early origin. In summary, this study provides important new insights into cortical reward network connectivity in healthy aging.

Resting state functional connectivity provides mechanistic predictions of future changes in sedentary behavior

Sedentary behaviors are increasing at the cost of millions of dollars spent in health care and productivity losses due to physical inactivity-related deaths worldwide. Understanding the mechanistic predictors of sedentary behaviors will improve future intervention development and precision medicine approaches. It has been posited that humans have an innate attraction towards effort minimization and that inhibitory control is required to overcome this prepotent disposition. Consequently, we hypothesized that individual differences in the functional connectivity of brain regions implicated in inhibitory control and physical effort decision making at the beginning of an exercise intervention in older adults would predict the change in time spent sedentary over the course of that intervention. In 143 healthy, low-active older adults participating in a 6-month aerobic exercise intervention (with three conditions: walking, dance, stretching), we aimed to use baseline neuroimaging (resting state functional connectivity of two a priori defined seed regions), and baseline accelerometer measures of time spent sedentary to predict future pre-post changes in objectively measured time spent sedentary in daily life over the 6-month intervention. Our results demonstrated that functional connectivity between (1) the anterior cingulate cortex and the supplementary motor area and (2) the right anterior insula and the left temporoparietal/temporooccipital junction, predicted changes in time spent sedentary in the walking group. Functional connectivity of these brain regions did not predict changes in time spent sedentary in the dance nor stretch and tone conditions, but baseline time spent sedentary was predictive in these conditions. Our results add important knowledge toward understanding mechanistic associations underlying complex out-of-session sedentary behaviors within a walking intervention setting in older adults.

Sleep, Noninvasive Brain Stimulation, and the Aging Brain: Challenges and Opportunities

As we age, sleep patterns undergo severe modifications of their micro and macrostructure, with an overall lighter and more fragmented sleep structure. In general, interventions targeting sleep represent an excellent opportunity not only to maintain life quality in the healthy aging population, but also to enhance cognitive performance and, when pathology arises, to potentially prevent/slow down conversion from e.g. Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD). Sleep abnormalities are, in fact, one of the earliest recognizable biomarkers of dementia, being also partially responsible for a cascade of cortical events that worsen dementia pathophysiology, including impaired clearance systems leading to build-up of extracellular amyloid-β (Aβ) peptide and intracellular hyperphosphorylated tau proteins. In this context, Noninvasive Brain Stimulation (NiBS) techniques, such as transcranial electrical stimulation (tES) and transcranial magnetic stimulation (TMS), may help investigate the neural substrates of sleep, identify sleep-related pathology biomarkers, and ultimately help patients and healthy elderly individuals to restore sleep quality and cognitive performance. However, brain stimulation applications during sleep have so far not been fully investigated in healthy elderly cohorts, nor tested in AD patients or other related dementias. The manuscript discusses the role of sleep in normal and pathological aging, reviewing available evidence of NiBS applications during both wakefulness and sleep in healthy elderly individuals as well as in MCI/AD patients. Rationale and details for potential future brain stimulation studies targeting sleep alterations in the aging brain are discussed, including enhancement of cognitive performance, overall quality of life as well as protein clearance.

Functional brain connectivity changes across the human life span: From fetal development to old age

The dynamic of the temporal correlations between brain areas, called functional connectivity (FC), undergoes complex transformations through the life span. In this review, we aim to provide an overview of these changes in the nonpathological brain from fetal life to advanced age. After a brief description of the main methods, we propose that FC development can be divided into four main phases: first, before birth, a strong change in FC leads to the emergence of functional proto-networks, involving mainly within network short-range connections. Then, during the first years of life, there is a strong widespread organization of networks which starts with segregation processes followed by a continuous increase in integration. Thereafter, from adolescence to early adulthood, a refinement of existing networks in the brain occurs, characterized by an increase in integrative processes until about 40 years. Middle age constitutes a pivotal period associated with an inversion of the functional brain trajectories with a decrease in segregation process in conjunction to a large-scale reorganization of between network connections. Studies suggest that these processes are in line with the development of cognitive and sensory functions throughout life as well as their deterioration. During aging, results support the notion of dedifferentiation processes, which refer to the decrease in functional selectivity of the brain regions, resulting in more diffuse and less specialized FC, associated with the disruption of cognitive functions with age. The inversion of developmental processes during aging is in accordance with the developmental models of neuroanatomy for which the latest matured regions are the first to deteriorate.

Age-related modulations of alpha and gamma brain activities underlying anticipation and distraction

Attention operates through top-down (TD) and bottom-up (BU) mechanisms. Recently, it has been shown that slow (alpha) frequencies index facilitatory and suppressive mechanisms of TD attention and faster (gamma) frequencies signal BU attentional capture. Ageing is characterized by increased behavioral distractibility, resulting from either a reduced efficiency of TD attention or an enhanced triggering of BU attention. However, only few studies have investigated the impact of ageing upon the oscillatory activities involved in TD and BU attention. MEG data were collected from 14 elderly and 14 matched young healthy human participants while performing the Competitive Attention Task. Elderly participants displayed (1) exacerbated behavioral distractibility, (2) altered TD suppressive mechanisms, indexed by a reduced alpha synchronization in task-irrelevant regions, (3) less prominent alpha peak-frequency differences between cortical regions, (4) a similar BU system activation indexed by gamma activity, and (5) a reduced activation of lateral prefrontal inhibitory control regions. These results show that the ageing-related increased distractibility is of TD origin.

Altered Functional Connectivity of the Executive Functions Network During a Stroop Task in Children with Reading Difficulties

Children with reading difficulties (RDs) often receive related accommodations in schools, such as additional time for examinations and reading aloud written material. Existing data suggest that these readers share challenges in executive functions (EFs). Our study was designed to determine whether children with RDs have specific challenges in EFs and define neurobiological signatures for such difficulties using magnetic resonance imaging (MRI) data. Reading and EFs abilities were assessed in 8-12-year-old children with RDs and age-matched typical readers. Functional MRI data were acquired during a Stroop task, and functional connectivity of the EFs defined network was calculated in both groups and related to reading ability. Children with RDs showed lower reading and EFs abilities and demonstrated greater functional connectivity between the EFs network and visual, language, and cognitive control regions during the Stroop task, compared to typical readers. Our results suggest that children with RDs utilize neural circuits supporting EFs more so than do typical readers to perform a cognitive task. These results also provide a neurobiological explanation for the challenges in EFs shared by children with RDs and explain challenges this group shares outside of the reading domain.

Cardiovascular disease risk factors, tract-based structural connectomics, and cognition in older adults

Cardiovascular disease risk factors (CVD-RFs) are associated with decreased gray and white matter integrity and cognitive impairment in older adults. Less is known regarding the interplay between CVD-RFs, brain structural connectome integrity, and cognition. We examined whether CVD-RFs were associated with measures of tract-based structural connectivity in 94 non-demented/non-depressed older adults and if alterations in connectivity mediated associations between CVD-RFs and cognition. Participants (age = 68.2 years; 52.1% female; 46.8% Black) underwent CVD-RF assessment, MRI, and cognitive evaluation. Framingham 10-year stroke risk (FSRP-10) quantified CVD-RFs. Graph theory analysis integrated T1-derived gray matter regions of interest (ROIs; 23 a-priori ROIs associated with CVD-RFs and dementia), and diffusion MRI-derived white matter tractography into connectivity matrices analyzed for local efficiency and nodal strength. A principal component analysis resulted in three rotated factor scores reflecting executive function (EF; FAS, Trail Making Test (TMT) B-A, Letter-Number Sequencing, Matrix Reasoning); attention/information processing (AIP; TMT-A, TMT-Motor, Digit Symbol); and memory (CVLT-II Trials 1-5 Total, Delayed Free Recall, Recognition Discriminability). Linear regressions between FSRP-10 and connectome ROIs adjusting for word reading, intracranial volume, and white matter hyperintensities revealed negative associations with nodal strength in eight ROIs (p-values<.05) and negative associations with efficiency in two ROIs, and a positive association in one ROI (p-values<.05). There was mediation of bilateral hippocampal strength on FSRP-10 and AIP, and left rostral middle frontal gyrus strength on FSRP-10 and AIP and EF. Stroke risk plays differential roles in connectivity and cognition, suggesting the importance of multi-modal neuroimaging biomarkers in understanding age-related CVD-RF burden and brain-behavior.

Big Data Blind Separation

Data or signal separation is one of the critical areas of data analysis. In this work, the problem of non-negative data separation is considered. The problem can be briefly described as follows: given X∈Rm×N, find A∈Rm×n and S∈R+n×N such that X=AS. Specifically, the problem with sparse locally dominant sources is addressed in this work. Although the problem is well studied in the literature, a test to validate the locally dominant assumption is not yet available. In addition to that, the typical approaches available in the literature sequentially extract the elements of the mixing matrix. In this work, a mathematical modeling-based approach is presented that can simultaneously validate the assumption, and separate the given mixture data. In addition to that, a correntropy-based measure is proposed to reduce the model size. The approach presented in this paper is suitable for big data separation. Numerical experiments are conducted to illustrate the performance and validity of the proposed approach.

Pseudoneglect is maintained in aging but not in mild Alzheimer's disease: new insights from an enumeration task

Neurologically healthy young adults display a behavioral bias, called pseudoneglect, which favors the processing of stimuli appearing in the left visual field. Pseudoneglect arises from the right hemisphere dominance for visuospatial attention. Previous studies investigating the effects of normal aging on pseudoneglect in line bisection and greyscale tasks have produced divergent results. In addition, scarce systematic investigations of visual biases in dementia have been reported. The aim of the present study was to evaluate whether the leftward bias appearing during an enumeration task in young adults would be preserved in normal aging and at different stages of severity of Alzheimer's disease. In Experiment 1, young and older healthy adults showed a comparable pseudoneglect, performing better when targets appeared in the left visual field. In Experiment 2, the leftward bias was maintained in amnesic mild cognitive impairment patients (aMCI), but it vanished in mild Alzheimer's disease patients (AD). The maintenance of pseudoneglect in normal aging and in aMCI patients is consistent with compensatory phenomena involving the right fronto-parietal network, which allow maintaining the right hemisphere dominance. Conversely, the lack of pseudoneglect in the sample of AD patients likely results from a loss of the right hemisphere dominance, caused by the selective degeneration of the right fronto-parietal network. These results highlight the need of further systematic investigations of visuospatial biases along the continuum of normal and pathological aging, both for a better understanding of the changes characterizing cognitive aging and for improvements in the evaluation of neglect in Alzheimer's disease.

Predicting Long-Term Cognitive Outcome Following Breast Cancer with Pre-Treatment Resting State fMRI and Random Forest Machine Learning

We aimed to determine if resting state functional magnetic resonance imaging (fMRI) acquired at pre-treatment baseline could accurately predict breast cancer-related cognitive impairment at long-term follow-up. We evaluated 31 patients with breast cancer (age 34–65) prior to any treatment, post-chemotherapy and 1 year later. Cognitive testing scores were normalized based on data obtained from 43 healthy female controls and then used to categorize patients as impaired or not based on longitudinal changes. We measured clustering coefficient, a measure of local connectivity, by applying graph theory to baseline resting state fMRI and entered these metrics along with relevant patient-related and medical variables into random forest classification. Incidence of cognitive impairment at 1 year follow-up was 55% and was predicted by classification algorithms with up to 100% accuracy (p < 0.0001). The neuroimaging-based model was significantly more accurate than a model involving patient-related and medical variables (p = 0.005). Hub regions belonging to several distinct functional networks were the most important predictors of cognitive outcome. Characteristics of these hubs indicated potential spread of brain injury from default mode to other networks over time. These findings suggest that resting state fMRI is a promising tool for predicting future cognitive impairment associated with breast cancer. This information could inform treatment decision making by identifying patients at highest risk for long-term cognitive impairment.