Connectome-based models can predict processing speed in older adults

Connectome-based prediction of decreased trust propensity in older adults with mild cognitive impairment: A resting-state functional magnetic resonance imaging study

Trust propensity (TP) relies more on social than economic rationality to transform the perceived probability of betrayal into positive reciprocity expectations in older adults with normal cognition. While deficits in social rationality have been observed in older adults with mild cognitive impairment (MCI), there is limited research on TP and its associated resting-state functional connectivity (RSFC) mechanisms in this population. To measure TP and related psychological functions (affect, motivation, executive cognition, and social cognition), MCI (n = 42) and normal healthy control (NHC, n = 115) groups completed a one-shot trust game and additional assessments of related psychological functions. RSFC associated with TP was analyzed using connectome-based predictive modeling (CPM) and lesion simulations. Our behavioral results showed that the MCI group trusted less (i.e., had lower TP) than the NHC group, with lower TP associated with higher sensitivity to the probability of betrayal in the MCI group. In the MCI group, only negative CPM models (RSFC negatively correlated with TP) significantly predicted TP, with a high salience network (SN) contribution. In contrast, in the NHC group, positive CPM models (RSFC positively correlated with TP) significantly predicted TP, with a high contribution from the default mode network (DMN). In addition, the total network strength of the NHC-specific positive network was lower in the MCI group than in the NHC group. Our findings demonstrated a decrease in TP in the MCI group compared to the NHC group, which is associated with deficits in social rationality (social cognition, associated with DMN) and increased sensitivity to betrayal (affect, associated with SN) in a trust dilemma. In conclusion, our study contributes to understanding MCI-related alterations in trust and their underlying neural mechanisms.

Behaviorally meaningful functional networks mediate the effect of Alzheimer's pathology on cognition

Tau pathology is associated with cognitive impairment in both aging and Alzheimer's disease, but the functional and structural bases of this relationship remain unclear. We hypothesized that the integrity of behaviorally meaningful functional networks would help explain the relationship between tau and cognitive performance. Using resting state fMRI, we identified unique networks related to episodic memory and executive function cognitive domains. The episodic memory network was particularly related to tau pathology measured with positron emission tomography in the entorhinal and temporal cortices. Further, episodic memory network strength mediated the relationship between tau pathology and cognitive performance above and beyond neurodegeneration. We replicated the association between these networks and tau pathology in a separate cohort of older adults, including both cognitively unimpaired and mildly impaired individuals. Together, these results suggest that behaviorally meaningful functional brain networks represent a functional mechanism linking tau pathology and cognition.

Prediction of cognitive performance differences in older age from multimodal neuroimaging data

Differences in brain structure and functional and structural network architecture have been found to partly explain cognitive performance differences in older ages. Thus, they may serve as potential markers for these differences. Initial unimodal studies, however, have reported mixed prediction results of selective cognitive variables based on these brain features using machine learning (ML). Thus, the aim of the current study was to investigate the general validity of cognitive performance prediction from imaging data in healthy older adults. In particular, the focus was with examining whether (1) multimodal information, i.e., region-wise grey matter volume (GMV), resting-state functional connectivity (RSFC), and structural connectivity (SC) estimates, may improve predictability of cognitive targets, (2) predictability differences arise for global cognition and distinct cognitive profiles, and (3) results generalize across different ML approaches in 594 healthy older adults (age range: 55-85 years) from the 1000BRAINS study. Prediction potential was examined for each modality and all multimodal combinations, with and without confound (i.e., age, education, and sex) regression across different analytic options, i.e., variations in algorithms, feature sets, and multimodal approaches (i.e., concatenation vs. stacking). Results showed that prediction performance differed considerably between deconfounding strategies. In the absence of demographic confounder control, successful prediction of cognitive performance could be observed across analytic choices. Combination of different modalities tended to marginally improve predictability of cognitive performance compared to single modalities. Importantly, all previously described effects vanished in the strict confounder control condition. Despite a small trend for a multimodal benefit, developing a biomarker for cognitive aging remains challenging.

Tau trajectory in Alzheimer's disease: Evidence from the connectome-based computational models

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with an impairment of cognition and memory. Current research on connectomics have now related changes in the network organization in AD to the patterns of accumulation and spread of amyloid and tau, providing insights into the neurobiological mechanisms of the disease. In addition, network analysis and modeling focus on particular use of graphs to provide intuition into key organizational principles of brain structure, that stipulate how neural activity propagates along structural connections. The utility of connectome-based computational models aids in early predicting, tracking the progression of biomarker-directed AD neuropathology. In this article, we present a short review of tau trajectory, the connectome changes in tau pathology, and the dependent recent connectome-based computational modelling approaches for tau spreading, reproducing pragmatic findings, and developing significant novel tau targeted therapies.

Facial attractiveness in the eyes of men with high arousal

INTRODUCTION

Individuals differ in how they judge facial attractiveness. However, little is known about the role of arousal level and gender differences in individuals' facial attractiveness judgments.

METHODS

We used resting-state electroencephalogram (EEG) to investigate this issue. A total of 48 men (aged 22.5 ± 3.03 years [mean ± SD], range: 18-30 years) and 27 women (aged 20.3 ± 2.03 years [mean ± SD], range: 18-25 years) participated in the experiment. After the EEG was collected, participants were instructed to complete a facial attractiveness judgment task. Connectome-based predictive modeling was used to predict individual judgment of facial attractiveness.

RESULTS

Men with high arousal judged female faces as more attractive (M = 3.85, SE = 0.81) than did men with low arousal (M = 3.33, SE = 0.81) and women (M = 3.24, SE = 1.02). Functional connectivity of the alpha band predicted judgment of female facial attractiveness in men but not in women. After controlling for the age and variability, the prediction effect was still significant.

CONCLUSION

Our results provide neural evidence for the enhancement of the judgment of facial attractiveness in men with high arousal levels, which supports the hypothesis that individuals' spontaneous arousal contributes to variations in facial attractiveness preferences.

Connectome-based prediction of eating disorder-associated symptomatology

BACKGROUND

Despite increasing knowledge on the neuroimaging patterns of eating disorder (ED) symptoms in non-clinical populations, studies using whole-brain machine learning to identify connectome-based neuromarkers of ED symptomatology are absent. This study examined the association of connectivity within and between large-scale functional networks with specific symptomatic behaviors and cognitions using connectome-based predictive modeling (CPM).

METHODS

CPM with ten-fold cross-validation was carried out to probe functional networks that were predictive of ED-associated symptomatology, including body image concerns, binge eating, and compensatory behaviors, within the discovery sample of 660 participants. The predictive ability of the identified networks was validated using an independent sample of 821 participants.

RESULTS

The connectivity predictive of body image concerns was identified within and between networks implicated in cognitive control (frontoparietal and medial frontal), reward sensitivity (subcortical), and visual perception (visual). Crucially, the set of connections in the positive network related to body image concerns identified in one sample was generalized to predict body image concerns in an independent sample, suggesting the replicability of this effect.

CONCLUSIONS

These findings point to the feasibility of using the functional connectome to predict ED symptomatology in the general population and provide the first evidence that functional interplay among distributed networks predicts body shape/weight concerns.

Functional connectome stability and optimality are markers of cognitive performance

Patterns of whole-brain fMRI functional connectivity, or connectomes, are unique to individuals. Previous work has identified subsets of functional connections within these patterns whose strength predicts aspects of attention and cognition. However, overall features of these connectomes, such as how stable they are over time and how similar they are to a group-average (typical) or high-performance (optimal) connectivity pattern, may also reflect cognitive and attentional abilities. Here, we test whether individuals who express more stable, typical, optimal, and distinctive patterns of functional connectivity perform better on cognitive tasks using data from three independent samples. We find that individuals with more stable task-based functional connectivity patterns perform better on attention and working memory tasks, even when controlling for behavioral performance stability. Additionally, we find initial evidence that individuals with more typical and optimal patterns of functional connectivity also perform better on these tasks. These results demonstrate that functional connectome stability within individuals and similarity across individuals predicts individual differences in cognition.

Age-related intrinsic functional connectivity underlying emotion utilization

Previous studies investigated the age-related positivity effect in terms of emotion perception and management, whereas little is known about whether the positivity effect is shown in emotion utilization (EU). If yes, the EU-related intrinsic functional connectivity and its age-associated alterations remain to be elucidated. In this study, we collected resting-state functional magnetic resonance imaging data from 62 healthy older adults and 72 undergraduates as well as their self-ratings of EU. By using the connectome-based predictive modeling (CPM) method, we constructed a predictive model of the positive relationship between EU self-ratings and resting-state functional connectivity. Lesion simulation analyses revealed that the medial-frontal network, default mode network, frontoparietal network, and subcortical regions played key roles in the EU-related CPM. Older subjects showed significantly higher EU self-ratings than undergraduates, which was associated with strengthened connectivity between the left dorsolateral prefrontal cortex and bilateral frontal poles, and between the left frontal pole and thalamus. A mediation analysis indicated that the age-related EU network mediated the age effect on EU self-ratings. Our findings extend previous research on the age-related "positivity effect" to the EU domain, suggesting that the positivity effect on the self-evaluation of EU is probably associated with emotion knowledge which accumulates with age.

Association of functional connectivity of the executive control network or default mode network with cognitive impairment in older adults with remitted major depressive disorder or mild cognitive impairment

Major depressive disorder (MDD) is associated with an increased risk of developing dementia. The present study aimed to better understand this risk by comparing resting state functional connectivity (rsFC) in the executive control network (ECN) and the default mode network (DMN) in older adults with MDD or mild cognitive impairment (MCI). Additionally, we examined the association between rsFC in the ECN or DMN and cognitive impairment transdiagnostically. We assessed rsFC alterations in ECN and DMN in 383 participants from five groups at-risk for dementia-remitted MDD with normal cognition (MDD-NC), non-amnestic mild cognitive impairment (naMCI), remitted MDD + naMCI, amnestic MCI (aMCI), and remitted MDD + aMCI-and from healthy controls (HC) or individuals with Alzheimer's dementia (AD). Subject-specific whole-brain functional connectivity maps were generated for each network and group differences in rsFC were calculated. We hypothesized that alteration of rsFC in the ECN and DMN would be progressively larger among our seven groups, ranked from low to high according to their risk for dementia as HC, MDD-NC, naMCI, MDD + naMCI, aMCI, MDD + aMCI, and AD. We also regressed scores of six cognitive domains (executive functioning, processing speed, language, visuospatial memory, verbal memory, and working memory) on the ECN and DMN connectivity maps. We found a significant alteration in the rsFC of the ECN, with post hoc testing showing differences between the AD group and the HC, MDD-NC, or naMCI groups, but no significant alterations in rsFC of the DMN. Alterations in rsFC of the ECN and DMN were significantly associated with several cognitive domain scores transdiagnostically. Our findings suggest that a diagnosis of remitted MDD may not confer functional brain risk for dementia. However, given the association of rs-FC with cognitive performance (i.e., transdiagnostically), rs-FC may help in stratifying this risk among people with MDD and varying degrees of cognitive impairment.

Connectome-based predictive modelling of cognitive reserve using task-based functional connectivity

Cognitive reserve supports cognitive function in the presence of pathology or atrophy. Functional neuroimaging may enable direct and accurate measurement of cognitive reserve which could have considerable clinical potential. The present study aimed to develop and validate a measure of cognitive reserve using task-based fMRI data that could then be applied to independent resting-state data. Connectome-based predictive modelling with leave-one-out cross-validation was applied to predict a residual measure of cognitive reserve using task-based functional connectivity from the Cognitive Reserve/Reference Ability Neural Network studies (n = 220, mean age = 51.91 years, SD = 17.04 years). This model generated summary measures of connectivity strength that accurately predicted a residual measure of cognitive reserve in unseen participants. The theoretical validity of these measures was established via a positive correlation with a socio-behavioural proxy of cognitive reserve (verbal intelligence) and a positive correlation with global cognition, independent of brain structure. This fitted model was then applied to external test data: resting-state functional connectivity data from The Irish Longitudinal Study on Ageing (TILDA, n = 294, mean age = 68.3 years, SD = 7.18 years). The network-strength predicted measures were not positively associated with a residual measure of cognitive reserve nor with measures of verbal intelligence and global cognition. The present study demonstrated that task-based functional connectivity data can be used to generate theoretically valid measures of cognitive reserve. Further work is needed to establish if, and how, measures of cognitive reserve derived from task-based functional connectivity can be applied to independent resting-state data.

Classification and prediction of cognitive performance differences in older age based on brain network patterns using a machine learning approach

Age-related cognitive decline varies greatly in healthy older adults, which may partly be explained by differences in the functional architecture of brain networks. Resting-state functional connectivity (RSFC) derived network parameters as widely used markers describing this architecture have even been successfully used to support diagnosis of neurodegenerative diseases. The current study aimed at examining whether these parameters may also be useful in classifying and predicting cognitive performance differences in the normally aging brain by using machine learning (ML). Classifiability and predictability of global and domain-specific cognitive performance differences from nodal and network-level RSFC strength measures were examined in healthy older adults from the 1000BRAINS study (age range: 55-85 years). ML performance was systematically evaluated across different analytic choices in a robust cross-validation scheme. Across these analyses, classification performance did not exceed 60% accuracy for global and domain-specific cognition. Prediction performance was equally low with high mean absolute errors (MAEs ≥ 0.75) and low to none explained variance (R2 ≤ 0.07) for different cognitive targets, feature sets, and pipeline configurations. Current results highlight limited potential of functional network parameters to serve as sole biomarker for cognitive aging and emphasize that predicting cognition from functional network patterns may be challenging.

Causal influences of salience/cerebellar networks on dorsal attention network subserved age-related cognitive slowing

Age-related cognitive slowing is a prominent precursor of cognitive decline. Functional neuroimaging studies found that cognitive processing speed is associated with activation and coupling among frontal, parietal and cerebellar brain networks. However, how the reciprocal influences of inter- and intra-network coupling mediate age-related decline in processing speed remains insufficiently studied. This study examined how inter- and intra-brain network influences mediate age-related slowing. We were interested in the fronto-insular salience network (SN), frontoparietal dorsal attention network (DAN), cerebellar network (CN) and default mode network (DMN). Reaction time (RT) and functional MRI data from 84 participants (aged 18-75) were collected while they were performing the Arrow Task in visual or audial forms. At the subject level, effective connectivities (ECs) were estimated with regression dynamic causal modelling. At the group level, structural equation models (SEMs) were used to model latent speed based on age and the EC mediators. Age was associated with decreased speed and increased inter-network effective connectivity. The CN exerting influence on the DAN (CN → DAN EC) mediated, while the SN → DAN EC suppressed age-related slowing. The DMN and intra-network ECs did not seem to play significant roles in slowing due to ageing. Inter-network connectivity from the CN and SN to the DAN contributes to age-related slowing. The seemingly antagonizing influences of the CN and SN indicate that increased task-related automaticity and decreased effortful control on top-down attention would promote greater speed in older individuals.

Individualized morphometric similarity predicts body mass index and food approach behavior in school-age children

Childhood obesity is associated with alterations in brain structure. Previous studies generally used a single structural index to characterize the relationship between body mass index(BMI) and brain structure, which could not describe the alterations of structural covariance between brain regions. To cover this research gap, this study utilized two independent datasets with brain structure profiles and BMI of 155 school-aged children. Connectome-based predictive modeling(CPM) was used to explore whether children’s BMI is reliably predictable by the novel individualized morphometric similarity network(MSN). We revealed the MSN can predict the BMI in school-age children with good generalizability to unseen dataset. Moreover, these revealed significant brain structure covariant networks can further predict children’s food approach behavior. The positive predictive networks mainly incorporated connections between the frontoparietal network(FPN) and the visual network(VN), between the FPN and the limbic network(LN), between the default mode network(DMN) and the LN. The negative predictive network primarily incorporated connections between the FPN and DMN. These results suggested that the incomplete integration of the high-order brain networks and the decreased dedifferentiation of the high-order networks to the primary reward networks can be considered as a core structural basis of the imbalance between inhibitory control and reward processing in childhood obesity.

Preoperative brain connectome predicts postoperative changes in processing speed in moyamoya disease

Moyamoya disease is a rare cerebrovascular disorder associated with cognitive dysfunction. It is usually treated by surgical revascularization, but research on the neurocognitive outcomes of revascularization surgery is controversial. Given that neurocognitive impairment could affect the daily activities of patients with moyamoya disease, early detection of postoperative neurocognitive outcomes has the potential to improve patient management. In this study, we applied a well-established connectome-based predictive modelling approach to develop machine learning models that used preoperative resting-state functional connectivity to predict postoperative changes in processing speed in patients with moyamoya disease. Twelve adult patients with moyamoya disease (age range: 23–49 years; female/male: 9/3) were recruited prior to surgery and underwent follow-up at 1 and 6 months after surgery. Twenty healthy controls (age range: 24–54 years; female/male: 14/6) were recruited and completed the behavioural test at baseline, 1-month follow-up and 6-month follow-up. Behavioural results indicated that the behavioural changes in processing speed at 1 and 6 months after surgery compared with baseline were not significant. Importantly, we showed that preoperative resting-state functional connectivity significantly predicted postoperative changes in processing speed at 1 month after surgery (negative network: ρ = 0.63, P_corr = 0.017) and 6 months after surgery (positive network: ρ = 0.62, P_corr = 0.010; negative network: ρ = 0.55, P_corr = 0.010). We also identified cerebro-cerebellar and cortico-subcortical connectivities that were consistently associated with processing speed. The brain regions identified from our predictive models are not only consistent with previous studies but also extend previous findings by revealing their potential roles in postoperative neurocognitive functions in patients with moyamoya disease. Taken together, our findings provide preliminary evidence that preoperative resting-state functional connectivity might predict the post-surgical longitudinal neurocognitive changes in patients with moyamoya disease. Given that processing speed is a crucial cognitive ability supporting higher neurocognitive functions, this study’s findings offer important insight into the clinical management of patients with moyamoya disease.

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.

A Neuroimaging Signature of Cognitive Aging from Whole-Brain Functional Connectivity

Cognitive decline is amongst one of the most commonly reported complaints during normal aging. Despite evidence that age and cognition are linked with similar neural correlates, no previous studies have directly ascertained how these two constructs overlap in the brain in terms of neuroimaging-based prediction. Based on a long lifespan healthy cohort (CamCAN, aged 19-89 years, n = 567), it is shown that both cognitive function (domains spanning executive function, emotion processing, motor function, and memory) and human age can be reliably predicted from unique patterns of functional connectivity, with models generalizable in two external datasets (n = 533 and n = 453). Results show that cognitive decline and normal aging both manifest decrease within-network connections (especially default mode and ventral attention networks) and increase between-network connections (somatomotor network). Whereas dorsal attention network is an exception, which is highly predictive on cognitive ability but is weakly correlated with aging. Further, the positively weighted connections in predicting fluid intelligence significantly mediate its association with age. Together, these findings offer insights into why normal aging is often associated with cognitive decline in terms of brain network organization, indicating a process of neural dedifferentiation and compensational theory.

Asymmetric generalizability of multimodal brain-behavior associations across age-groups

Machine learning methods have increasingly been used to map out brain-behavior associations (BBA), and to predict out-of-scanner behavior of unseen subjects. Given the brain changes that occur in the context of aging, the accuracy of these predictions is likely to depend on how similar the training and testing data sets are in terms of age. To this end, we examined how well BBAs derived from an age-group generalize to other age-groups. We partitioned the CAM-CAN data set (N = 550) into the young, middle, and old age-groups, then used the young and old age-groups to construct prediction models for 11 behavioral outcomes using multimodal neuroimaging features (i.e., structural and resting-state functional connectivity, and gray matter volume/cortical thickness). These models were then applied to all three age-groups to predict their behavioral scores. When the young-derived models were used, a graded pattern of age-generalization was generally observed across most behavioral outcomes-predictions are the most accurate in the young subjects in the testing data set, followed by the middle and then old-aged subjects. Conversely, when the old-derived models were used, the disparity in the predictive accuracy across age-groups was mostly negligible. These findings hold across different imaging modalities. These results suggest the asymmetric age-generalization of BBAs-old-derived BBAs generalized well to all age-groups, however young-derived BBAs generalized poorly beyond their own age-group.

Speed of Processing (SoP) Training Plus α-tACS in People With Mild Cognitive Impairment: A Double Blind, Parallel, Placebo Controlled Trial Study Protocol

Several cognitive training programs, alone or in combination with non-invasive brain stimulation have been tested in order to ameliorate age-related cognitive impairments, such as the ones found in Mild Cognitive Impairment (MCI). However, the effects of Cognitive Training (CT)—combined or not—with several forms of non-invasive brain stimulation have been modest at most. We aim to assess if Speed of Processing (SoP) training combined with alpha transcranial alternating current stimulation (α-tACS) is able to increase speed of processing as assessed by the Useful Field of View (UFOV), when comparing to SoP training or active α-tACS alone. Moreover, we want to assess if those changes in speed of processing transfer to other cognitive domains, such as memory, language and executive functioning by using the NIH EXAMINER. We also want to test the mechanisms underlying these interventions, namely brain connectivity and coherence as assessed by electroencephalography (EEG). To that purpose, our proposal is to enroll 327 elders diagnosed with MCI in a double-blinded, parallel randomized clinical trial assessing the effects of combining SoP with alpha endogenous tACS (either active or sham) in people with MCI. Participants will perform an intervention that will last for 15 sessions. For the first 3 weeks, participants will receive nine sessions of the intervention, and then will receive two sessions per week (i.e., booster) for the following 3 weeks. They will then be assessed at 1, 3, and 6 months after the intervention has ended. This will allow us to detect the immediate, and long-term effects of the interventions, as well as to probe the mechanisms underlying its effects.

Clinical Trial Registration:Clinicaltrials.gov, Identifier: NCT05198726.

What do neuroanatomical networks reveal about the ontology of human cognitive abilities?

Over the last decades, cognitive psychology has come to a fair consensus about the human intelligence ontological structure. However, it remains an open question whether anatomical properties of the brain support the same ontology. The present study explored the ontological structure derived from neuroanatomical networks associated with performance on 15 cognitive tasks indicating various abilities. Results suggest that the brain-derived (neurometric) ontology partly agrees with the cognitive performance-derived (psychometric) ontology complemented with interpretable differences. Moreover, the cortical areas associated with different inferred abilities are segregated, with little or no overlap. Nevertheless, these spatially segregated cortical areas are integrated via denser white matter structural connections as compared with the general brain connectome. The integration of ability-related cortical networks constitutes a neural counterpart to the psychometric construct of general intelligence, while the consistency and difference between psychometric and neurometric ontologies represent crucial pieces of knowledge for theory building, clinical diagnostics, and treatment.

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Psychometric and neurometric cognitive ontologies are partly equivalent

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Ability-related brain areas are ontologically segregated with little to no overlap

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However, ability-related brain areas are densely interconnected by fiber tracts

Functional connectivity dynamics as a function of the fluctuation of tension during film watching

To advance the understanding of the dynamic relationship between brain activities and emotional experiences, we examined the neural patterns of tension, a unique emotion that highly depends on how an event unfolds. Specifically, the present study explored the temporal relationship between functional connectivity patterns within and between different brain functional modules and the fluctuation in tension during film watching. Due to the highly contextualized and time-varying nature of tension, we expected that multiple neural networks would be involved in the dynamic tension experience. Using the neuroimaging data of 546 participants, we conducted a dynamic brain analysis to identify the intra- and inter-module functional connectivity patterns that are significantly correlated with the fluctuation of tension over time. The results showed that the inter-module connectivity of cingulo-opercular network, fronto-parietal network, and default mode network is involved in the dynamic experience of tension. These findings demonstrate a close relationship between brain functional connectivity patterns and emotional dynamics, which supports the importance of functional connectivity dynamics in understanding our cognitive and emotional processes.

Methodological evaluation of individual cognitive prediction based on the brain white matter structural connectome

An emerging trend is to use regression‐based machine learning approaches to predict cognitive functions at the individual level from neuroimaging data. However, individual prediction models are inherently influenced by the vast options for network construction and model selection in machine learning pipelines. In particular, the brain white matter (WM) structural connectome lacks a systematic evaluation of the effects of different options in the pipeline on predictive performance. Here, we focused on the methodological evaluation of brain structural connectome‐based predictions. For network construction, we considered two parcellation schemes for defining nodes and seven strategies for defining edges. For the regression algorithms, we used eight regression models. Four cognitive domains and brain age were targeted as predictive tasks based on two independent datasets (Beijing Aging Brain Rejuvenation Initiative [BABRI]: 633 healthy older adults; Human Connectome Projects in Aging [HCP‐A]: 560 healthy older adults). Based on the results, the WM structural connectome provided a satisfying predictive ability for individual age and cognitive functions, especially for executive function and attention. Second, different parcellation schemes induce a significant difference in predictive performance. Third, prediction results from different data sets showed that dMRI with distinct acquisition parameters may plausibly result in a preference for proper fiber reconstruction algorithms and different weighting options. Finally, deep learning and Elastic‐Net models are more accurate and robust in connectome‐based predictions. Together, significant effects of different options in WM network construction and regression algorithms on the predictive performances are identified in this study, which may provide important references and guidelines to select suitable options for future studies in this field.

Predicting response time variability from task and resting-state functional connectivity in the aging brain

Aging is associated with declines in a host of cognitive functions, including attentional control, inhibitory control, episodic memory, processing speed, and executive functioning. Theoretical models attribute the age-related decline in cognitive functioning to deficits in goal maintenance and attentional inhibition. Despite these well-documented declines in executive control resources, older adults endorse fewer episodes of mind-wandering when assessed using task-embedded thought probes. Furthermore, previous work on the neural basis of mind-wandering has mostly focused on young adults with studies predominantly focusing on the activity and connectivity of a select few canonical networks. However, whole-brain functional networks associated with mind-wandering in aging have not yet been characterized. In this study, using response time variability-the trial-to-trial fluctuations in behavioral responses-as an indirect marker of mind-wandering or an "out-of-the-zone" attentional state representing suboptimal behavioral performance, we show that brain-based predictive models of response time variability can be derived from whole-brain task functional connectivity. In contrast, models derived from resting-state functional connectivity alone did not predict individual response time variability. Finally, we show that despite successful within-sample prediction of response time variability, our models did not generalize to predict response time variability in independent cohorts of older adults with resting-state connectivity. Overall, our findings provide evidence for the utility of task-based functional connectivity in predicting individual response time variability in aging. Future research is needed to derive more robust and generalizable models.

Age-specificity and generalization of behavior-associated structural and functional networks and their relevance to behavioral domains

Behavior-associated structural connectivity (SC) and resting-state functional connectivity (rsFC) networks undergo various changes in aging. To study these changes, we proposed a continuous dimension where at one end networks generalize well across age groups in terms of behavioral predictions (age-general) and at the other end, they predict behaviors well in a specific age group but fare poorly in another age group (age-specific). We examined how age generalizability/specificity of multimodal behavioral associated brain networks varies across behavioral domains and imaging modalities. Prediction models consisting of SC and/or rsFC networks were trained to predict a diverse range of 75 behavioral outcomes in a young adult sample (N = 92). These models were then used to predict behavioral outcomes in unseen young (N = 60) and old (N = 60) subjects. As expected, behavioral prediction models derived from the young age group, produced more accurate predictions in the unseen young than old subjects. These behavioral predictions also differed significantly across behavioral domains, but not imaging modalities. Networks associated with cognitive functions, except for a few mostly relating to semantic knowledge, fell toward the age-specific end of the spectrum (i.e., poor young-to-old generalizability). These findings suggest behavior-associated brain networks are malleable to different degrees in aging; such malleability is partly determined by the nature of the behavior.

Neurophysiological and behavioral effects of multisession prefrontal tDCS and concurrent cognitive remediation training in patients with autism spectrum disorder (ASD): A double-blind, randomized controlled fNIRS study

BACKGROUND

The clinical effects and neurophysiological mechanisms of prefrontal tDCS and concurrent cognitive remediation training in individuals with autism spectrum disorder (ASD) remain unclear.

OBJECTIVE

This two-armed, double-blind, randomized, sham-controlled trial aimed to investigate the beneficial effects of tDCS combined with concurrent cognitive remediation training on adolescents and young adults with ASD.

METHODS

Participants were randomly assigned to either active or sham tDCS groups and received 1.5 mA prefrontal tDCS with left dorsolateral prefrontal cortex cathode placement and right supraorbital region anode placement for 20 min over two consecutive weeks. tDCS was delivered concurrently with a computerized cognitive remediation training program. Social functioning and its underlying cognitive processes, as well as prefrontal resting-state functional connectivity (rsFC), were measured.

RESULTS

The results from 41 participants indicated that multisession prefrontal tDCS, compared to sham tDCS, significantly enhanced the social functioning of ASD individuals [F(1,39) = 4.75, p = .035, η_p² = 0.11]. This improvement was associated with enhanced emotion recognition [F(1,39) = 8.34, p = .006, η_p² = 0.18] and cognitive flexibility [F(1,39) = 4.91, p = .033, η_p² = 0.11]. Specifically, this tDCS protocol optimized information processing efficiency [F(1,39) = 4.43, p = .042, η_p² = 0.10], and the optimization showed a trend to be associated with enhanced rsFC in the right medial prefrontal cortex (ρ = 0.339, pFDR = .083).

CONCLUSION

Multisession tDCS with left dlPFC cathode placement and right supraorbital region anode placement paired with concurrent cognitive remediation training promoted social functioning in individuals with ASD. This appeared to be associated with the enhancement of the functional connectivity of the right medial PFC, a major hub for flexible social information processing, allowing these individuals to process information more efficiently in response to different social situations.

TRIAL REGISTRATION

ClinicalTrials.gov (ID: NCT03814083).

Identifying a whole-brain connectome-based model in drug-naïve Parkinson's disease for predicting motor impairment

Identifying a whole‐brain connectome‐based predictive model in drug‐naïve patients with Parkinson's disease and verifying its predictions on drug‐managed patients would be useful in determining the intrinsic functional underpinnings of motor impairment and establishing general brain–behavior associations. In this study, we constructed a predictive model from the resting‐state functional data of 47 drug‐naïve patients by using a connectome‐based approach. This model was subsequently validated in 115 drug‐managed patients. The severity of motor impairment was assessed by calculating Unified Parkinson's Disease Rating Scale Part III scores. The predictive performance of model was evaluated using the correlation coefficient (r_true) between predicted and observed scores. As a result, a connectome‐based model for predicting individual motor impairment in drug‐naïve patients was identified with significant performance (r_true = .845, p < .001, p_permu = .002). Two patterns of connection were identified according to correlations between connection strength and the severity of motor impairment. The negative motor‐impairment‐related network contained more within‐network connections in the motor, visual‐related, and default mode networks, whereas the positive motor‐impairment‐related network was constructed mostly with between‐network connections coupling the motor‐visual, motor‐limbic, and motor‐basal ganglia networks. Finally, this predictive model constructed around drug‐naïve patients was confirmed with significant predictive efficacy on drug‐managed patients (r = .209, p = .025), suggesting a generalizability in Parkinson's disease patients under long‐term drug influence. In conclusion, this study identified a whole‐brain connectome‐based model that could predict the severity of motor impairment in Parkinson's patients and furthers our understanding of the functional underpinnings of the disease.

Qigong exercise enhances cognitive functions in the elderly via an interleukin-6-hippocampus pathway: A randomized active-controlled trial

BACKGROUND

Evidence has suggested that exercise protects against cognitive decline in aging, but the recent lockdown measures associated with the COVID-19 pandemic have limited the opportunity for outdoor exercise. Herein we tested the effects of an indoor exercise, Qigong, on neurocognitive functioning as well as its potential neuro-immune pathway.

METHODS

We conducted a 12-week randomized active-controlled trial with two study arms in cognitively healthy older people. We applied Wu Xing Ping Heng Gong (Qigong), which was designed by an experienced Daoist Qigong master, to the experimental group, whereas we applied the physical stretching exercise to the control group. The Qigong exercise consisted of a range of movements involving the stretching of arms and legs, the turning of the torso, and relaxing, which would follow the fundamental principles of Daoism and traditional Chinese medicine (e.g., Qi). We measured aging-sensitive neurocognitive abilities, serum interleukin-6 (IL-6) levels, and brain structural volumes in the experimental (Qigong, n = 22) and control groups (stretching, n = 26) before and after the 12-week training.

RESULTS

We observed that Qigong caused significant improvement in processing speed (t (46) = 2.03, p = 0.048) and sustained attention (t (46) = -2.34, p = 0.023), increased hippocampal volume (t (41) = 3.94, p < 0.001), and reduced peripheral IL-6 levels (t (46) = -3.17, p = 0.003). Moreover, following Qigong training, greater reduction of peripheral IL-6 levels was associated with a greater increase of processing speed performance (bootstrapping CI: [0.16, 3.30]) and a more significant training-induced effect of hippocampal volume on the improvement in sustained attention (bootstrapping CI: [-0.35, -0.004]).

CONCLUSION

Overall, these findings offer significant insight into the mechanistic role of peripheral IL-6-and its intricate interplay with neural processes-in the beneficial neurocognitive effects of Qigong. The findings have profound implications for early identification and intervention of older individuals vulnerable to cognitive decline, focusing on the neuro-immune pathway. The trial was registered at clinicaltrials.gov (identifier: NCT04641429).

Fronto-cerebellar connectivity mediating cognitive processing speed

Processing speed is an important construct in understanding cognition. This study was aimed to control task specificity for understanding the neural mechanisms underlying cognitive processing speed. Forty young adult subjects performed attention tasks of two modalities (auditory and visual) and two levels of task rules (compatible and incompatible). Block-design fMRI captured BOLD signals during the tasks. Thirteen regions of interest were defined with reference to publicly available activation maps for processing speed tasks. Cognitive speed was derived from task reaction times, which yielded six sets of connectivity measures. Mixed-effect LASSO regression revealed six significant paths suggestive of a cerebello-frontal network predicting the cognitive speed. Among them, three are long range (two fronto-cerebellar, one cerebello-frontal), and three are short range (fronto-frontal, cerebello-cerebellar, and cerebello-thalamic). The long-range connections are likely to relate to cognitive control, and the short-range connections relate to rule-based stimulus-response processes. The revealed neural network suggests that automaticity, acting on the task rules and interplaying with effortful top-down attentional control, accounts for cognitive speed.