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Obesity represents a significant public health concern and is linked to various comorbidities and cognitive impairments. Previous research indicates that elevated body mass index (BMI) is associated with structural changes in white matter (WM). However, a deeper characterization of body composition is required, especially considering the links between abdominal obesity and metabolic dysfunction. This study aims to enhance our understanding of the relationship between obesity and WM connectivity by directly assessing the amount and distribution of fat tissue. Whole‐body magnetic resonance imaging (MRI) was employed to evaluate total adipose tissue (TAT), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT), while MR liver spectroscopy measured liver fat content in 63 normal‐weight, overweight, and obese males. WM connectivity was quantified using microstructure‐informed tractography. Connectome‐based predictive modeling was used to predict body composition metrics based on WM connectomes. Our analysis revealed a positive dependency between BMI, TAT, SAT, and WM connectivity in brain regions involved in reward processing and appetite regulation, such as the insula, nucleus accumbens, and orbitofrontal cortex. Increased connectivity was also observed in cognitive control and inhibition networks, including the middle frontal gyrus and anterior cingulate cortex. No significant associations were found between WM connectivity and VAT or liver fat. Our findings suggest that altered neural communication between these brain regions may affect cognitive processes, emotional regulation, and reward perception in individuals with obesity, potentially contributing to weight gain. While our study did not identify a link between WM connectivity and VAT or liver fat, further investigation of the role of various fat depots and metabolic factors in brain networks is required to advance obesity prevention and treatment approaches. [ABSTRACT FROM AUTHOR]

Mild traumatic brain injury (mTBI) is a known risk factor for neurodegenerative diseases, yet the precise pathophysiological mechanisms remain poorly understand, often obscured by group-level analysis in non-invasive neuroimaging studies. Individual-based method is critical to exploring heterogeneity in mTBI. We recruited 80 mTBI patients and 40 matched healthy controls, obtaining high-resolution structural MRI for constructing Individual Differential Structural Covariance Networks (IDSCN). Comparisons were conducted at both the individual and group levels. Connectome-based Predictive Modeling (CPM) was applied to predict cognitive performance based on whole-brain connectivity. During the acute stage of mTBI, patients exhibited significant heterogeneity in the count and direction of altered edges, obscured by group-level analysis. In the chronic stage, the number of altered edges decreased and became more consistent, aligning with clinical observations of acute cognitive impairment and gradual improvement. Subgroup analysis based on loss of consciousness/post-traumatic amnesia revealed distinct patterns of alterations. The temporal lobe, particularly regions related to the limbic system, significantly predicted cognitive function from acute to chronic stage. The use of IDSCN and CPM has provided valuable individual-level insights, reconciling discrepancies from previous studies. Additionally, the limbic system may be an appropriate target for future intervention efforts.

Sleep is critical to a variety of cognitive functions and insufficient sleep can have negative consequences for mood and behavior across the lifespan. An important open question is how sleep duration is related to functional brain organization which may in turn impact cognition. To characterize the functional brain networks related to sleep across youth and young adulthood, we analyzed data from the publicly available Human Connectome Project (HCP) dataset, which includes n‐back task‐based and resting‐state fMRI data from adults aged 22–35 years (task n = 896; rest n = 898). We applied connectome‐based predictive modeling (CPM) to predict participants' mean sleep duration from their functional connectivity patterns. Models trained and tested using 10‐fold cross‐validation predicted self‐reported average sleep duration for the past month from n‐back task and resting‐state connectivity patterns. We replicated this finding in data from the 2‐year follow‐up study session of the Adolescent Brain Cognitive Development (ABCD) Study, which also includes n‐back task and resting‐state fMRI for adolescents aged 11–12 years (task n = 786; rest n = 1274) as well as Fitbit data reflecting average sleep duration per night over an average duration of 23.97 days. CPMs trained and tested with 10‐fold cross‐validation again predicted sleep duration from n‐back task and resting‐state functional connectivity patterns. Furthermore, demonstrating that predictive models are robust across independent datasets, CPMs trained on rest data from the HCP sample successfully generalized to predict sleep duration in the ABCD Study sample and vice versa. Thus, common resting‐state functional brain connectivity patterns reflect sleep duration in youth and young adults. [ABSTRACT FROM AUTHOR]

Resting-state functional connectivity (RSFC) has been proposed as a potential indicator of repetitive negative thinking (RNT) in depression. However, identifying the specific functional process associated with RSFC alterations is challenging, and it remains unclear whether alterations in RSFC for depressed individuals are directly related to the RNT process or to individual characteristics distinct from the negative thinking process per se. To investigate the relationship between RSFC alterations and the RNT process in individuals with major depressive disorder (MDD), we compared RSFC with functional connectivity during an induced negative-thinking state (NTFC) in terms of their predictability of RNT traits and associated whole-brain connectivity patterns using connectome-based predictive modeling (CPM) and connectome-wide association (CWA) analyses. Thirty-six MDD participants and twenty-six healthy control participants underwent both resting state and induced negative thinking state fMRI scans. Both RSFC and NTFC distinguished between healthy and depressed individuals with CPM. However, trait RNT in depressed individuals, as measured by the Ruminative Responses Scale-Brooding subscale, was only predictable from NTFC, not from RSFC. CWA analysis revealed that negative thinking in depression was associated with higher functional connectivity between the default mode and executive control regions, which was not observed in RSFC. These findings suggest that RNT in depression involves an active mental process encompassing multiple brain regions across functional networks, which is not represented in the resting state. Although RSFC indicates brain functional alterations in MDD, they may not directly reflect the negative thinking process. • Resting-state functional connectivity (RSFC) did not predict rumination in MDD. • Negative-thinking-state functional connectivity (NTFC) predicted rumination in MDD. • Both RSFC and NTFC distinguished between healthy and depressed individuals. • Rumination in depression involves active mental process across functional networks. • NTFC associated with higher FC between default mode and executive control regions. [ABSTRACT FROM AUTHOR]

Objective: It is well known that altered functional connectivity is a robust neuroimaging marker of schizophrenia. However, there is inconsistency in the direction of alterations, i.e., increased or decreased connectivity. In this study, we aimed to determine the direction of the connectivity alteration associated with schizophrenia using a multivariate, data-driven approach. Methods: Resting-state functional magnetic resonance imaging data were acquired from 109 individuals with schizophrenia and 120 controls across two openly available datasets. A whole-brain resting-state functional connectivity (rsFC) matrix was computed for each individual. A modified connectome-based predictive model (CPM) with a support vector machine (SVM) was used to classify patients and controls. We conducted a series of multivariate classification analyses using three different feature sets, increased, decreased, and both increased and decreased rsFC. Results: For both datasets, combining information from both increased and decreased rsFC substantially improved prediction accuracy (Dataset 1: accuracy = 70.2%, permutation p =0.001; Dataset 2: accuracy = 64.4%, permutation p = 0.003). When tested across datasets, the prediction model using decreased rsFC performed best. The identified predictive features of decreased rsFC were distributed mostly in the motor network for both datasets. Conclusion: These findings suggest that bidirectional alterations in rsFC are distributed in schizophrenia patients, with the pattern of decreased rsFC being more similar across different populations. [ABSTRACT FROM AUTHOR]

Introduction: Abnormal interactions among distributed brain systems are implicated in the mechanisms of nicotine addiction. However, the relationship between the structural covariance network, a measure of brain connectivity, and smoking severity remains unclear. To fill this gap, this study aimed to investigate the relationship between structural covariance network and smoking severity in smokers. Methods: A total of 101 male smokers and 51 male non-smokers were recruited, and they underwent a T1-weighted anatomical image scan. First, an individualized structural covariance network was derived via a jackknife-bias estimation procedure for each participant. Then, a data-driven machine learning method called connectome-based predictive modeling (CPM) was conducted to infer smoking severity measured with Fagerström Test for Nicotine Dependence (FTND) scores using an individualized structural covariance network. The performance of CPM was evaluated using the leave-one-out cross-validation and a permutation testing. Results: As a result, CPM identified the smoking severity-related structural covariance network, as indicated by a significant correlation between predicted and actual FTND scores (r = 0.23, permutation p = 0.020). Identified networks comprised of edgesmainly located between the subcortical-cerebellumnetwork and networks including the frontoparietal default model and motor and visual networks. Discussion: These results identified smoking severity-related structural covariance networks and provided a new insight into the neural underpinnings of smoking severity. [ABSTRACT FROM AUTHOR]

Introduction: Neuro‐navigated repetitive transcranial magnetic stimulation (rTMS) is effective in alleviating cognitive deficits in Alzheimer's disease (AD). However, the strategy for target determination and the mechanisms for cognitive improvement remain unclear. Methods: One hundred and thirteen elderly subjects were recruited in this study, including both cross‐sectional (n = 79) and longitudinal experiments (the rTMS group: n = 24; the sham group: n = 10). The cross‐sectional experiment explored the precise intervention target based on the cortical–hippocampal network. The longitudinal experiment investigated the clinical efficacy of neuro‐navigated rTMS treatment over a four‐week period and explored its underlying neural mechanism using seed‐based and network‐based analysis. Finally, we applied connectome‐based predictive modeling to predict the rTMS response using these functional features at baseline. Results: RTMS at a targeted site of the left angular gyrus (MNI: −45, −67, 38) significantly induced cognitive improvement in memory and language function (p < 0.001). The improved cognition correlated with the default mode network (DMN) subsystems. Furthermore, the connectivity patterns of DMN subsystems (r = 0.52, p = 0.01) or large‐scale networks (r = 0.85, p = 0.001) at baseline significantly predicted the Δ language cognition after the rTMS treatment. The connectivity patterns of DMN subsystems (r = 0.47, p = 0.019) or large‐scale networks (r = 0.80, p = 0.001) at baseline could predict the Δ memory cognition after the rTMS treatment. Conclusion: These findings suggest that neuro‐navigated rTMS targeting the left angular gyrus could improve cognitive function in AD patients. Importantly, dynamic regulation of the intra‐ and inter‐DMN at baseline may represent a potential predictor for favorable rTMS treatment response in patients with cognitive impairment. [ABSTRACT FROM AUTHOR]

Working memory plays a crucial role in our daily lives, and brain imaging has been used to predict working memory performance. Here, we present an improved connectome-based predictive modeling approach for building a predictive model of individual working memory performance from whole-brain functional connectivity. The model was built using n-back task-based fMRI and resting-state fMRI data from the Human Connectome Project. Compared to prior models, our model was more interpretable, demonstrated a closer connection to the known anatomical and functional network. The model also demonstrates strong generalization on nine other cognitive behaviors from the HCP database and can well predict the working memory performance of healthy individuals in external datasets. By comparing the differences in prediction effects of different brain networks and anatomical feature analysis on n-back tasks, we found the essential role of some networks in differentiating between high and low working memory loads conditions. [ABSTRACT FROM AUTHOR]

Global cognitive performance plays an important role in the diagnosis of HIV-associated neurocognitive disorders (HAND), yet to date, there is no simple way to measure global cognitive performance in people with HIV (PWH). Here, we performed connectome-based predictive modeling (CPM) to pursue a neural biomarker of global cognitive performance in PWH based on whole-brain resting-state functional connectivity. We built a CPM model that successfully predicted individual differences in global cognitive performance in the training set of 67 PWH by using leave-one-out cross-validation. This model generalized to both 33 novel PWH in the testing set and a subset of 39 PWH who completed a follow-up visit two years later. Furthermore, network strengths identified by the CPM model were significantly different between PWH with HAND and without HAND. Together, these results demonstrate that whole-brain functional network strengths could serve as a potential neural biomarker of global cognitive performance in PWH.

Objective: Headache is among the most frequent symptoms after coronavirus disease 2019 (COVID-19), so-called long COVID syndrome. Although distinct brain changes have been reported in patients with long COVID, such reported brain changes have not been used for predictions and interpretations in a multivariate manner. In this study, we applied machine learning to assess whether individual adolescents with long COVID can be accurately distinguished from those with primary headaches. Methods: Twenty-three adolescents with long COVID headaches with the persistence of headache for at least 3 months and 23 age- and sex-matched adolescents with primary headaches (migraine, new daily persistent headache, and tension-type headache) were enrolled. Multivoxel pattern analysis (MVPA) was applied for disorder-specific predictions of headache etiology based on individual brain structural MRI. In addition, connectome-based predictive modeling (CPM) was also performed using a structural covariance network. Results: MVPA correctly classified long COVID patients from primary headache patients, with an area under the curve of 0.73 (accuracy = 63.4%; permutation p = 0.001). The discriminating GM patterns exhibited lower classification weights for long COVID in the orbitofrontal and medial temporal lobes. The CPM using the structural covariance network achieved an area under the curve of 0.81 (accuracy = 69.5%; permutation p = 0.005). The edges that classified long COVID patients from primary headache were mainly comprising thalamic connections. Conclusion: The results suggest the potential value of structural MRI-based features for classifying long COVID headaches from primary headaches. The identified features suggest that the distinct gray matter changes in the orbitofrontal and medial temporal lobes occurring after COVID, as well as altered thalamic connectivity, are predictive of headache etiology. [ABSTRACT FROM AUTHOR]

• Connectome-based predictive modeling was used with a Frailty Index. • The frailty network had its main connectivity in the visual network. • The robustness network had its main connectivity in the basal ganglia. • Connectivity patterns were different based on the degree of frailty. Frailty in older adults is associated with greater risk of cognitive decline. Brain connectivity insights could help understand the association, but studies are lacking. We applied connectome-based predictive modeling to a 32-item self-reported Frailty Index (FI) using resting state functional MRI data from The Irish Longitudinal Study on Ageing. A total of 347 participants were included (48.9% male, mean age 68.2 years). From connectome-based predictive modeling, we obtained 204 edges that positively correlated with the FI and composed the "frailty network" characterised by connectivity of the visual network (right); and 188 edges that negatively correlated with the FI and formed the "robustness network" characterized by connectivity in the basal ganglia. Both networks' highest degree node was the caudate but with different patterns: from caudate to visual network in the frailty network; and to default mode network in the robustness network. The FI was correlated with walking speed but not with metrics of global cognition, reinforcing the matching between the FI and the brain connectivity pattern found (main predicted connectivity in basal ganglia). [ABSTRACT FROM AUTHOR]

Background: Anhedonia is a key symptom of major depressive disorder (MDD) and other psychiatric diseases. The neural basis of anhedonia has been widely examined, yet the interindividual variability in neuroimaging biomarkers underlying individual‐specific symptom severity is not well understood. Methods: To establish an individualized prediction model of anhedonia, we applied connectome‐based predictive modeling (CPM) to whole‐brain resting‐state functional connectivity profiles of MDD patients. Results: The CPM can successfully and reliably predict individual consummatory but not anticipatory anhedonia. The predictive model mainly included salience network (SN), frontoparietal network (FPN), default mode network (DMN), and motor network. Importantly, subsequent computational lesion prediction and consummatory‐specific model prediction revealed that connectivity of the SN with DMN and FPN is essential and specific for the prediction of consummatory anhedonia. Conclusions: This study shows that brain functional connectivity, especially the connectivity of SN‐FPN and SN‐DMN, can specifically predict individualized consummatory anhedonia in MDD. These findings suggest the potential of functional connectomes for the diagnosis and prognosis of anhedonia in MDD and other disorders. [ABSTRACT FROM AUTHOR]

Empathy impairments have been linked to alcohol dependence even during abstinent periods. Nonetheless, the neural underpinnings of abstinence-induced empathy deficits remain unclear. In this study, we employed connectome-based predictive modeling (CPM) by using whole brain resting-state functional connectivity (rs-FC) to predict empathy capability of abstinent alcoholics (n = 47) versus healthy controls (n = 59). In addition, the generalizability of the predictive model (i.e., one group treated as a training dataset and another one treated as a test dataset) was performed to determine whether healthy controls and abstinent alcoholics share common neural fingerprints of empathy. Our results showed that abstinent alcoholics relative to healthy controls had decreased empathy capacity. Although no predictive models were observed in the abstinence group, we found that individual empathy scores in the healthy group can be reliably predicted by functional connectivity from the default mode network (DMN) to the sensorimotor network (SMN), occipital network, and cingulo-opercular network (CON). Moreover, the identified connectivity fingerprints of healthy controls could be generalized to predict empathy in the abstinence group. These findings indicate that neural circuits accounting for empathy may be disrupted by alcohol use and the impaired degree varies greatly among abstinent individuals. The large inter-individual variation may impede identification of the predictive model of empathy in alcohol abstainers. [ABSTRACT FROM AUTHOR]

Although trait and state rumination play a central role in the exacerbation of negative affect, evidence suggests that they are weakly correlated and exert distinct influences on emotional reactivity to stressors. Whether trait and state rumination share a common or exhibit distinct neural substrate remains unclear. In this study, we utilized functional near-infrared spectroscopy (fNIRS) combined with connectome-based predictive modeling (CPM) to identify neural fingerprints associated with trait and state rumination. CPM identified distinctive functional connectivity (FC) profiles that contribute to the prediction of trait rumination, primarily involving FC within the default mode network (DMN) and the dorsal attention network (DAN) as well as FC between the DMN, control network (CN), DAN, and salience network (SN). Conversely, state rumination was predominantly associated with FC between the DMN and CN. Furthermore, the predictive features of trait rumination can be robustly generalized to predict state rumination, and vice versa. In conclusion, this study illuminates the importance of both DMN and non-DMN systems in the emergence and persistence of rumination. While trait rumination was associated with stronger and broader FC than state rumination, the generalizability of the predictive features underscores the presence of shared neural mechanisms between the two forms of rumination. These identified connectivity fingerprints may hold promise as targets for innovative therapeutic interventions aimed at mitigating rumination-related negative affect. • Trait and state rumination exhibit distinctive brain connectivity features. • The FC model developed for one type of rumination predict the other. • Trait and state rumination share common connectivity fingerprints. [ABSTRACT FROM AUTHOR]

Mild traumatic brain injury (mTBI) is a known risk factor for neurodegenerative diseases, yet the precise pathophysiological mechanisms remain poorly understand, often obscured by group-level analysis in non-invasive neuroimaging studies. Individual-based method is critical to exploring heterogeneity in mTBI. We recruited 80 mTBI patients and 40 matched healthy controls, obtaining high-resolution structural MRI for constructing Individual Differential Structural Covariance Networks (IDSCN). Comparisons were conducted at both the individual and group levels. Connectome-based Predictive Modeling (CPM) was applied to predict cognitive performance based on whole-brain connectivity. During the acute stage of mTBI, patients exhibited significant heterogeneity in the count and direction of altered edges, obscured by group-level analysis. In the chronic stage, the number of altered edges decreased and became more consistent, aligning with clinical observations of acute cognitive impairment and gradual improvement. Subgroup analysis based on loss of consciousness/post-traumatic amnesia revealed distinct patterns of alterations. The temporal lobe, particularly regions related to the limbic system, significantly predicted cognitive function from acute to chronic stage. The use of IDSCN and CPM has provided valuable individual-level insights, reconciling discrepancies from previous studies. Additionally, the limbic system may be an appropriate target for future intervention efforts. [Display omitted] [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

As characterized by repeated exposure of others’ trauma, vicarious traumatization is a common negative psychological reaction during the COVID-19 pandemic and plays a crucial role in the development of general mental distress. This study aims to identify functional connectome that encodes individual variations of pandemic-related vicarious traumatization and reveal the underlying brain-vicarious traumatization mechanism in predicting general distress. The eligible subjects were 105 general university students (60 females, aged from 19 to 27 years) undergoing brain MRI scanning and baseline behavioral tests (October 2019 to January 2020), whom were re-contacted for COVID-related vicarious traumatization measurement (February to April 2020) and follow-up general distress evaluation (March to April 2021). We applied a connectome-based predictive modeling (CPM) approach to identify the functional connectome supporting vicarious traumatization based on a 268-region-parcellation assigned to network memberships. The CPM analyses showed that only the negative network model stably predicted individuals’ vicarious traumatization scores (q2 = -0.18, MSE = 617, r [predicted, actual] = 0.18, p = 0.024), with the contributing functional connectivity primarily distributed in the fronto-parietal, default mode, medial frontal, salience, and motor network. Furthermore, mediation analysis revealed that vicarious traumatization mediated the influence of brain functional connectome on general distress. Importantly, our results were independent of baseline family socioeconomic status, other stressful life events and general mental health as well as age, sex and head motion. Our study is the first to provide evidence for the functional neural markers of vicarious traumatization and reveal an underlying neuropsychological pathway to predict distress symptoms in which brain functional connectome affects general distress via vicarious traumatization.

Internet gaming disorder (IGD), a worldwide mental health issue, has been widely studied using neuroimaging techniques during the last decade. Although dysfunctions in resting‐state functional connectivity have been reported in IGD, mapping relationships from abnormal connectivity patterns to behavioral measures have not been fully investigated. Connectome‐based predictive modeling (CPM)—a recently developed machine‐learning approach—has been used to examine potential neural mechanisms in addictions and other psychiatric disorders. To identify the resting‐state connections associated with IGD, we modified the CPM approach by replacing its core learning algorithm with a support vector machine. Resting‐state functional magnetic resonance imaging (fMRI) data were acquired in 72 individuals with IGD and 41 healthy comparison participants. The modified CPM was conducted with respect to classification and regression. A comparison of whole‐brain and network‐based analyses showed that the default‐mode network (DMN) is the most informative network in predicting IGD both in classification (individual identification accuracy = 78.76%) and regression (correspondence between predicted and actual psychometric scale score: r = 0.44, P < 0.001). To facilitate the characterization of the aberrant resting‐state activity in the DMN, the identified networks have been mapped into a three‐subsystem division of the DMN. Results suggest that individual differences in DMN function at rest could advance our understanding of IGD and variability in disorder etiology and intervention outcomes. [ABSTRACT FROM AUTHOR]

Although many studies have explored the neural mechanism of the feeling of stress, to date, no effort has been made to establish a model capable of predicting the feeling of stress at the individual level using the resting-state functional connectome. Although individuals may be confronted with multidimensional stressors during the coronavirus disease 2019 (COVID-19) pandemic, their appraisal of the impact and severity of these events might vary. In this study, connectome-based predictive modeling (CPM) with leave-one-out cross-validation was conducted to predict individual perceived stress (PS) from whole-brain functional connectivity data from 817 participants. The results showed that the feeling of stress could be predicted by the interaction between the default model network and salience network, which are involved in emotion regulation and salience attribution, respectively. Key nodes that contributed to the prediction model comprised regions mainly located in the limbic systems and temporal lobe. Critically, the CPM model of PS based on regular days can be generalized to predict individual PS levels during the COVID-19 pandemic, which is a multidimensional, uncontrollable stressful situation. The stability of the results was demonstrated by two independent datasets. The present work not only expands existing knowledge regarding the neural mechanism of PS but also may help identify high-risk individuals in healthy populations.

Are intelligence and creativity distinct abilities, or do they rely on the same cognitive and neural systems? We sought to quantify the extent to which intelligence and creative cognition overlap in brain and behavior by combining machine learning of fMRI data and latent variable modeling of cognitive ability data in a sample of young adults (N = 186) who completed a battery of intelligence and creative thinking tasks. The study had 3 analytic goals: (a) to assess contributions of specific facets of intelligence (e.g., fluid and crystallized intelligence) and general intelligence to creative ability (i.e., divergent thinking originality), (b) to model whole-brain functional connectivity networks that predict intelligence facets and creative ability, and (c) to quantify the degree to which these predictive networks overlap in the brain. Using structural equation modeling, we found moderate to large correlations between intelligence facets and creative ability, as well as a large correlation between general intelligence and creative ability (r = .63). Using connectome-based predictive modeling, we found that functional brain networks that predict intelligence facets overlap to varying degrees with a network that predicts creative ability, particularly within the prefrontal cortex of the executive control network. Notably, a network that predicted general intelligence shared 46% of its functional connections with a network that predicted creative ability--including connections linking executive control and salience/ventral attention networks--suggesting that intelligence and creative thinking rely on similar neural and cognitive systems. [ABSTRACT FROM AUTHOR]

Global cognitive performance plays an important role in the diagnosis of HIV-associated neurocognitive disorders (HAND), yet to date, there is no simple way to measure global cognitive performance in people with HIV (PWH). Here, we performed connectome-based predictive modeling (CPM) to pursue a neural biomarker of global cognitive performance in PWH based on whole-brain resting-state functional connectivity. We built a CPM model that successfully predicted individual differences in global cognitive performance in the training set of 67 PWH by using leave-one-out cross-validation. This model generalized to both 33 novel PWH in the testing set and a subset of 39 PWH who completed a follow-up visit two years later. Furthermore, network strengths identified by the CPM model were significantly different between PWH with HAND and without HAND. Together, these results demonstrate that whole-brain functional network strengths could serve as a potential neural biomarker of global cognitive performance in PWH.

The prevalence of risky behavior such as substance use increases during adolescence; however, the neurobiological precursors to adolescent substance use remain unclear. Predictive modeling may complement previous work observing associations with known risk factors or substance use outcomes by developing generalizable models that predict early susceptibility. The aims of the current study were to identify and characterize behavioral and brain models of vulnerability to future substance use. Principal components analysis (PCA) of behavioral risk factors were used together with connectome-based predictive modeling (CPM) during rest and task-based functional imaging to generate predictive models in a large cohort of nine- and ten-year-olds enrolled in the Adolescent Brain & Cognitive Development (ABCD) study (NDA release 2.0.1). Dimensionality reduction (n = 9,437) of behavioral measures associated with substance use identified two latent dimensions that explained the largest amount of variance: risk-seeking (PC1; e.g., curiosity to try substances) and familial factors (PC2; e.g., family history of substance use disorder). Using cross-validated regularized regression in a subset of data (Year 1 Fast Track data; n>1,500), functional connectivity during rest and task conditions (resting-state; monetary incentive delay task; stop signal task; emotional n-back task) significantly predicted individual differences in risk-seeking (PC1) in held-out participants (partial correlations between predicted and observed scores controlling for motion and number of frames [rp]: 0.07-0.21). By contrast, functional connectivity was a weak predictor of familial risk factors associated with substance use (PC2) (rp: 0.03-0.06). These results demonstrate a novel approach to understanding substance use vulnerability, which—together with mechanistic perspectives—may inform strategies aimed at early identification of risk for addiction.

Previous studies have demonstrated relations between spontaneous neural activity evaluated by resting-state functional magnetic resonance imaging (fMRI) and symptom severity in post-traumatic stress disorder. However, few studies have used brain-based measures to identify imaging associations with illness severity at the level of individual patients. This study applied connectome-based predictive modeling (CPM), a recently developed data-driven and subject-level method, to identify brain function features that are related to symptom severity of trauma survivors. Resting-state fMRI scans and clinical ratings were obtained 10–15 months after the earthquake from 122 earthquake survivors. Symptom severity of post-traumatic stress disorder features for each survivor was evaluated using the Clinician Administered Post-traumatic Stress Disorder Scale (CAPS-IV). A functionally pre-defined atlas was applied to divide the human brain into 268 regions. Each individual’s functional connectivity 268 × 268 matrix was created to reflect correlations of functional time series data across each pair of nodes. The relationship between CAPS-IV scores and brain functional connectivity was explored in a CPM linear model. Using a leave-one-out cross-validation (LOOCV) procedure, findings showed that the positive network model predicted the left-out individual’s CAPS-IV scores from resting-state functional connectivity. CPM predicted CAPS-IV scores, as indicated by a significant correspondence between predicted and actual values (r = 0.30, P = 0.001) utilizing primarily functional connectivity between visual cortex, subcortical-cerebellum, limbic, and motor systems. The current study provides data-driven evidence regarding the functional brain features that predict symptom severity based on the organization of intrinsic brain networks and highlights its potential application in making clinical evaluation of symptom severity at the individual level.

Neuropsychological assessments are essential in diagnosing age-related neurocognitive disorders. However, they are lengthy in duration and can be unreliable at times. To this end, we explored a modified connectome-based predictive modeling approach to estimating individualized scores from multiple cognitive domains using structural connectivity (SC) and functional connectivity (FC) features. Multi-shell HARDI and resting-state functional magnetic resonance imaging scans, and scores from 10 cognitive measures were acquired from 91 older adults with mild cognitive impairment. SC and FC matrices were derived from these scans and, in various combinations, entered into models along with demographic covariates to predict cognitive scores. Leave-one-out cross-validation was performed. Predictive accuracy was assessed via the correlation between predicted and observed scores (rpredicted-observed). Across all cognitive measures, significant rpredicted-observed (0.402 to 0.654) were observed from the best-predicting models. Six of these models consisted of multimodal features. For three cognitive measures, their best-predicting models’ rpredicted-observed were similar to that of a model that included only demographic covariates— suggesting that SC and/or FC features did not contribute significantly on top of demographics. Cross-prediction models revealed that the best-predicting models were similarly accurate in predicting scores of related cognitive measures— suggesting their limited specificity in predicting cognitive scores. Generally, multimodal connectomes together with demographics, can be exploited as sensitive markers, though with limited specificity, to predict cognitive performance across a spectrum in multiple cognitive domains. In certain situations, it may not be worthwhile to acquire neuroimaging data, considering that demographics alone can be similarly accurate in predicting cognitive scores.

Alzheimer's disease (AD) is a neurodegenerative disease characterized by cognitive decline. Sex differences in the progression of AD exist, but the neural mechanisms are not well understood. The purpose of the current study was to explore sex differences in brain functional connectivity (FC) at different stages of AD and their predictive ability on Montreal Cognitive Assessment (MoCA) scores using connectome-based predictive modeling (CPM). Resting-state functional magnetic resonance imaging was collected from 81 AD patients (44 females), 78 amnestic mild cognitive impairment patients (44 females), and 92 healthy controls (50 females). The FC analysis was conducted and the interaction effect between sex and group was investigated using two-factor variance analysis. The CPM was used to predict MoCA scores. There were sex-by-group interaction effects on FC between the left dorsolateral superior frontal gyrus and left middle temporal gyrus, left precuneus and right calcarine fissure surrounding cortex, left precuneus and left middle occipital gyrus, left middle temporal gyrus and left precentral gyrus, and between the left middle temporal gyrus and right cuneus. In the CPM, the positive network predictive model significantly predicted MoCA scores in both males and females. There were significant sex-by-group interaction effects on FC between the left precuneus and left middle occipital gyrus, and between the left middle temporal gyrus and right cuneus could predict MoCA scores in female patients. Our results suggest that there are sex differences in FC at different stages of AD. The sex-specific FC can further predict MoCA scores at individual level., (© 2024 Wiley Periodicals LLC.)

Internet addiction symptomatology (IAS) is characterized by persistent and involuntary patterns of compulsive Internet use, leading to significant impairments in both physical and mental well-being. Here, a connectome-based predictive modeling approach was applied to decode IAS from whole-brain resting-state functional connectivity in healthy population. The findings showed that IAS could be predicted by the functional connectivity between prefrontal cortex with the cerebellum and limbic lobe and connections of the occipital lobe with the limbic lobe and insula lobe. The identified edges associated with IAS exhibit generalizability in predicting IAS within an independent sample. Furthermore, we found that the unique contributing network, which predicted IAS in contrast to the prediction networks of alcohol use disorder symptomatology (the range of symptoms and behaviors associated with alcohol use disorder), prominently comprised connections involving the occipital lobe and other lobes. The current data-driven approach provides the first evidence of the predictive brain features of IAS based on the organization of intrinsic brain networks, thus advancing our understanding of the neurobiological basis of Internet addiction disorder (IAD) susceptibility, and may have implications for the timely intervention of people potentially at risk of IAD., (© The Author(s) 2024. Published by Oxford University Press.)

Researches have reported the close association between fingers and arithmetic. However, it remains unclear whether and how finger training can benefit arithmetic. To address this issue, we used the abacus-based mental calculation (AMC), which combines finger training and mental arithmetic learning, to explore the neural correlates underlying finger-related arithmetic training. A total of 147 Chinese children (75 M/72 F, mean age, 6.89 ± 0.46) were recruited and randomly assigned into AMC and control groups at primary school entry. The AMC group received 5 years of AMC training, and arithmetic abilities and resting-state functional magnetic resonance images data were collected from both groups at year 1/3/5. The connectome-based predictive modeling was used to find the arithmetic-related networks of each group. Compared to controls, the AMC's positively arithmetic-related network was less located in the control module, and the inter-module connections between somatomotor-default and somatomotor-control modules shifted to somatomotor-visual and somatomotor-dorsal attention modules. Furthermore, the positive network of the AMC group exhibited a segregated connectivity pattern, with more intra-module connections than the control group. Overall, our results suggested that finger motor representation with motor module involvement facilitated arithmetic-related network segregation, reflecting increased autonomy of AMC, thus reducing the dependency of arithmetic on higher-order cognitive functions., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Previous studies have demonstrated relations between spontaneous neural activity evaluated by resting-state functional magnetic resonance imaging (fMRI) and symptom severity in post-traumatic stress disorder. However, few studies have used brain-based measures to identify imaging associations with illness severity at the level of individual patients. This study applied connectome-based predictive modeling (CPM), a recently developed data-driven and subject-level method, to identify brain function features that are related to symptom severity of trauma survivors. Resting-state fMRI scans and clinical ratings were obtained 10–15 months after the earthquake from 122 earthquake survivors. Symptom severity of post-traumatic stress disorder features for each survivor was evaluated using the Clinician Administered Post-traumatic Stress Disorder Scale (CAPS-IV). A functionally pre-defined atlas was applied to divide the human brain into 268 regions. Each individual's functional connectivity 268 × 268 matrix was created to reflect correlations of functional time series data across each pair of nodes. The relationship between CAPS-IV scores and brain functional connectivity was explored in a CPM linear model. Using a leave-one-out cross-validation (LOOCV) procedure, findings showed that the positive network model predicted the left-out individual's CAPS-IV scores from resting-state functional connectivity. CPM predicted CAPS-IV scores, as indicated by a significant correspondence between predicted and actual values (r = 0.30, P = 0.001) utilizing primarily functional connectivity between visual cortex, subcortical-cerebellum, limbic, and motor systems. The current study provides data-driven evidence regarding the functional brain features that predict symptom severity based on the organization of intrinsic brain networks and highlights its potential application in making clinical evaluation of symptom severity at the individual level. [ABSTRACT FROM AUTHOR]

Intelligence is a socially and scientifically interesting topic because of its prominence in human behavior, yet there is little clarity on how the neuroimaging and neurobiological correlates of intelligence differ between males and females, with most investigations limited to using either mass-univariate techniques or a single neuroimaging modality. Here we employed connectome-based predictive modeling (CPM) to predict the intelligence quotient (IQ) scores for 166 males and 160 females separately, using resting-state functional connectivity, grey matter cortical thickness or both. The identified multimodal, IQ-predictive imaging features were then compared between genders. CPM showed high out-of-sample prediction accuracy (r > 0.34), and integrating both functional and structural features further improved prediction accuracy by capturing complementary information (r = 0.45). Male IQ demonstrated higher correlations with cortical thickness in the left inferior parietal lobule, and with functional connectivity in left parahippocampus and default mode network, regions previously implicated in spatial cognition and logical thinking. In contrast, female IQ was more correlated with cortical thickness in the right inferior parietal lobule, and with functional connectivity in putamen and cerebellar networks, regions previously implicated in verbal learning and item memory. Results suggest that the intelligence generation of males and females may rely on opposite cerebral lateralized key brain regions and distinct functional networks consistent with their respective superiority in cognitive domains. Promisingly, understanding the neural basis of gender differences underlying intelligence may potentially lead to optimized personal cognitive developmental programs and facilitate advancements in unbiased educational test design. [ABSTRACT FROM AUTHOR]

Real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback is a non-invasive, non-pharmacological therapeutic tool that may be useful for training behavior and alleviating clinical symptoms. Although previous work has used rt-fMRI to target brain activity in or functional connectivity between a small number of brain regions, there is growing evidence that symptoms and behavior emerge from interactions between a number of distinct brain areas. Here, we propose a new method for rt-fMRI, connectome-based neurofeedback, in which intermittent feedback is based on the strength of complex functional networks spanning hundreds of regions and thousands of functional connections. We first demonstrate the technical feasibility of calculating whole-brain functional connectivity in real-time and provide resources for implementing connectome-based neurofeedback. We next show that this approach can be used to provide accurate feedback about the strength of a previously defined connectome-based model of sustained attention, the saCPM, during task performance. Although, in our initial pilot sample, neurofeedback based on saCPM strength did not improve performance on out-of-scanner attention tasks, future work characterizing effects of network target, training duration, and amount of feedback on the efficacy of rt-fMRI can inform experimental or clinical trial designs.

Brain imaging has been used to predict language skills during development and neuropathology but its accuracy in predicting language performance in healthy adults has been poorly investigated. To address this shortcoming, we studied the ability to predict reading accuracy and single‐word comprehension scores from rest‐ and task‐based functional magnetic resonance imaging (fMRI) datasets of 424 healthy adults. Using connectome‐based predictive modeling, we identified functional brain networks with >400 edges that predicted language scores and were reproducible in independent data sets. To simplify these complex models we identified the overlapping edges derived from the three task‐fMRI sessions (language, working memory, and motor tasks), and found 12 edges for reading recognition and 11 edges for vocabulary comprehension that accounted for 20% of the variance of these scores, both in the training sample and in the independent sample. The overlapping edges predominantly emanated from language areas within the frontoparietal and default‐mode networks, with a strong precuneus prominence. These findings identify a small subset of edges that accounted for a significant fraction of the variance in language performance that might serve as neuromarkers for neuromodulation interventions to improve language performance or for presurgical planning to minimize language impairments. [ABSTRACT FROM AUTHOR]

Being able to predict who will likely experience cancer related cognitive impairment (CRCI) could enhance patient care and potentially reduce economic and human costs associated with this adverse event. We aimed to determine if post-treatment patient reported CRCI could also be predicted from baseline resting state fMRI in patients with breast cancer. 76 newly diagnosed patients (n = 42 planned for chemotherapy; n = 34 not planned for chemotherapy) and 50 healthy female controls were assessed at 3 times points [T1 (prior to treatment); T2 (1 month post chemotherapy); T3 (1 year after T2)], and at yoked intervals for controls. Data collection included self-reported executive dysfunction, memory function, and psychological distress and resting state fMRI data converted to connectome matrices for each participant. Statistical analyses included linear mixed modeling, independent t tests, and connectome-based predictive modeling (CPM). Executive dysfunction increased over time in the chemotherapy group and was stable in the other two groups (p < 0.001). Memory function decreased over time in both patient groups compared to controls (p < 0.001). CPM models successfully predicted executive dysfunction and memory function scores (r > 0.31, p < 0.002). Support vector regression with a radial basis function (SVR RBF) showed the highest performance for executive dysfunction and memory function (r = 0.68; r = 0.44, p's < 0.001). Baseline neuroimaging may be useful for predicting patient reported cognitive outcomes which could assist in identifying patients in need of surveillance and/or early intervention for treatment-related cognitive effects. [ABSTRACT FROM AUTHOR]

Social attention involves selectively attending to and encoding socially relevant information. We investigated the neural systems underlying the wide range of variability in both social attention ability and social experience in a neurotypical sample. Participants performed a selective social attention task, while undergoing fMRI and completed self-report measures of social functioning. Using connectome-based predictive modeling, we demonstrated that individual differences in whole-brain functional connectivity patterns during selective attention to faces predicted task performance. Individuals with more cerebellar-occipital connectivity performed better on the social attention task, suggesting more efficient social information processing. Then, we estimated latent communities of autistic and socially anxious traits using exploratory graph analysis to decompose heterogeneity in social functioning between individuals. Connectivity strength within the identified social attention network was associated with social skills, such that more temporal-parietal connectivity predicted fewer challenges with social communication and interaction. These findings demonstrate that individual differences in functional connectivity strength during a selective social attention task are related to varying levels of self-reported social skill., (© The Author(s) 2023. Published by Oxford University Press.)

Brain functional connectivity features can predict cognition and behavior at the level of the individual. Most studies measure univariate signals, correlating timecourses from the average of constituent voxels in each node. While straightforward, this approach overlooks the spatial patterns of voxel-wise signals within individual nodes. Given that multivariate spatial activity patterns across voxels can improve fMRI measures of mental representations, here we asked whether using voxel-wise timecourses can better characterize region-by-region interactions relative to univariate approaches. Using two fMRI datasets, the Human Connectome Project sample and a local test-retest sample, we measured multivariate functional connectivity with multivariate distance correlation and univariate connectivity with Pearson's correlation. We compared multivariate and univariate connectivity estimates, demonstrating that relative to univariate estimates, multivariate estimates exhibited higher reliability at both the edge-level and connectome-level, stronger prediction of individual differences, and greater sensitivity to brain states within individuals. Our findings suggest that multivariate estimates reliably provide more powerful information about an individual's functional brain organization and its relation to cognitive skills. • Multivariate connectivity is calculated using all voxels' timecourses in each region. • Multivariate connectivity estimates are more reliable than univariate estimates. • Multivariate estimates better predict individuals' fluid intelligence. • Multivariate estimates have higher sensitivity to cognitive states within individuals. [ABSTRACT FROM AUTHOR]

Trust constitutes a fundamental basis of human society and plays a pivotal role in almost every aspect of human relationships. Although enormous interest exists in determining the neuropsychological underpinnings of a person's propensity to trust utilizing task‐based fMRI; however, little progress has been made in predicting its variations by task‐free fMRI based on whole‐brain resting‐state functional connectivity (RSFC). Here, we combined a one‐shot trust game with a connectome‐based predictive modeling approach to predict propensity to trust from whole‐brain RSFC. We demonstrated that individual variations in the propensity to trust were primarily predicted by RSFC rooted in the functional integration of distributed key nodes—caudate, amygdala, lateral prefrontal cortex, temporal–parietal junction, and the temporal pole—which are part of domain‐general large‐scale networks essential for the motivational, affective, and cognitive aspects of trust. We showed, further, that the identified brain‐behavior associations were only evident for trust but not altruistic preferences and that propensity to trust (and its underlying neural underpinnings) were modulated according to the extent to which a person emphasizes general social preferences (i.e., horizontal collectivism) rather than general risk preferences (i.e., trait impulsiveness). In conclusion, the employed data‐driven approach enables to predict propensity to trust from RSFC and highlights its potential use as an objective neuromarker of trust impairment in mental disorders. [ABSTRACT FROM AUTHOR]

Loneliness is an increasingly prevalent condition linking with enhanced morbidity and premature mortality. Despite recent proposal on medicalization of loneliness, so far no effort has been made to establish a model capable of predicting loneliness at the individual level. Here, we applied a machine-learning approach to decode loneliness from whole-brain resting-state functional connectivity (RSFC). The relationship between whole-brain RSFC and loneliness was examined in a linear predictive model. The results revealed that individual loneliness could be predicted by within- and between-network connectivity of prefrontal, limbic and temporal systems, which are involved in cognitive control, emotional processing and social perceptions and communications, respectively. Key nodes that contributed to the prediction model comprised regions previously implicated in loneliness, including the dorsolateral prefrontal cortex, lateral orbital frontal cortex, ventromedial prefrontal cortex, caudate, amygdala and temporal regions. Our findings also demonstrated that both loneliness and associated neural substrates are modulated by levels of neuroticism and extraversion. The current data-driven approach provides the first evidence on the predictive brain features of loneliness based on organizations of intrinsic brain networks. Our work represents initial efforts in the direction of making individualized prediction of loneliness that could be useful for diagnosis, prognosis and treatment. [ABSTRACT FROM AUTHOR]

• Whole-brain RSFC predict cognitive and affective empathy in normative and LPE groups. • Connectivity underlying empathy differentiates normative and LPE groups. • RSFC models of empathy in normative group did not generalize to LPE adolescents. • DMN, ECN, SAL, limbic, and cerebellar density characterized group empathy differences. • ECN-DMN density characterized differences in both cognitive and affective empathy. Empathy impairments are an important part of a broader affective impairments defining the youth antisocial phenotype callous-unemotional (CU) traits and the DSM-5 low prosocial emotion (LPE) specifier. While functional connectivity underlying empathy and CU traits have been well studied, less is known about what functional connections underly differences in empathy amongst adolescents qualifying for the LPE specifier. Such information can provide mechanistic distinctions for this clinically relevant specifier. The present study uses connectome-based predictive modeling that uses whole-brain resting-state functional connectivity data to predict cognitive and affective empathy for those meeting the LPE specifier (n = 29) and those that do not (n = 57). Additionally, we tested if models of empathy generalized between groups as well as density differences for each model of empathy between groups. Results indicate the LPE group had lower cognitive and affective empathy as well as higher CU traits and conduct problems. Negative and positive models were identified for affective empathy for both groups, but only the negative model for the LPE and positive model for the normative group reliably predicted cognitive empathy. Models predicting empathy did not generalize between groups. Density differences within the default mode, salience, executive control, limbic, and cerebellar networks were found as well as between the executive control, salience, and default mode networks. And, importantly, connections between the executive control and default mode networks characterized empathy differences the LPE group such that more positive connections characterized cognitive differences and less negative connections characterized affective differences. These findings indicate neural differences in empathy for those meeting LPE criteria that may explain decrements in empathy amongst these youth. These findings support theoretical accounts of empathy decrements in the LPE clinical specifier and extend them to identify specific circuits accounting for variation in empathy impairments. The identified negative models help understand what connections inhibit empathy whereas the positive models reveal what brain patterns are being used to support empathy in those with the LPE specifier. LPE differences from the normative group and could be an appropriate biomarker for predicting CU trait severity. Replication and validation using other large datasets are important next steps. [ABSTRACT FROM AUTHOR]

Cognitive neuroscience continues to advance our understanding of the neural foundations of human intelligence, with significant progress elucidating the role of the frontoparietal network in cognitive control mechanisms for flexible, intelligent behavior. Recent evidence in network neuroscience further suggests that this finding may represent the tip of the iceberg and that fluid intelligence may depend on the collective interaction of multiple brain networks. However, the global brain mechanisms underlying fluid intelligence and the nature of multi-network interactions remain to be well established. We therefore conducted a large-scale Connectome-based Predictive Modeling study, administering resting-state fMRI to 159 healthy college students and examining the contributions of seven intrinsic connectivity networks to the prediction of fluid intelligence, as measured by a state-of-the-art cognitive task (the Bochum Matrices Test). Specifically, we aimed to: (i) identify whether fluid intelligence relies on a primary brain network or instead engages multiple brain networks; and (ii) elucidate the nature of brain network interactions by assessing network allegiance (within- versus between-network connections) and network topology (strong versus weak connections) in the prediction of fluid intelligence. Our results demonstrate that whole-brain predictive models account for a large and significant proportion of variance in fluid intelligence (18%) and illustrate that the contribution of individual networks is relatively modest by comparison. In addition, we provide novel evidence that the global architecture of fluid intelligence prioritizes between-network connections and flexibility through weak ties. Our findings support a network neuroscience approach to understanding the collective role of brain networks in fluid intelligence and elucidate the system-wide network mechanisms from which flexible, adaptive behavior is constructed., (© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permission@oup.com.)

Objective: It is well known that altered functional connectivity is a robust neuroimaging marker of schizophrenia. However, there is inconsistency in the direction of alterations, i.e., increased or decreased connectivity. In this study, we aimed to determine the direction of the connectivity alteration associated with schizophrenia using a multivariate, data-driven approach., Methods: Resting-state functional magnetic resonance imaging data were acquired from 109 individuals with schizophrenia and 120 controls across two openly available datasets. A whole-brain resting-state functional connectivity (rsFC) matrix was computed for each individual. A modified connectome-based predictive model (CPM) with a support vector machine (SVM) was used to classify patients and controls. We conducted a series of multivariate classification analyses using three different feature sets, increased, decreased, and both increased and decreased rsFC., Results: For both datasets, combining information from both increased and decreased rsFC substantially improved prediction accuracy (Dataset 1: accuracy = 70.2%, permutation p = 0.001; Dataset 2: accuracy = 64.4%, permutation p  = 0.003). When tested across datasets, the prediction model using decreased rsFC performed best. The identified predictive features of decreased rsFC were distributed mostly in the motor network for both datasets., Conclusion: These findings suggest that bidirectional alterations in rsFC are distributed in schizophrenia patients, with the pattern of decreased rsFC being more similar across different populations., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Kim, Seo, Yun and Kim.)

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.

Older adulthood is characterized by enhanced emotional well-being potentially resulting from greater reliance on adaptive emotion regulation strategies. However, not all older adults demonstrate an increase in emotional well-being and instead rely on maladaptive emotion regulation strategies. An important moderator of age-related shifts in strategy preferences is working memory (WM) and its underlying neural circuitry. As such, individual differences in the neural integrity underlying WM may predict older adults' emotion regulation strategy preferences. Our study used whole-brain WM networks-derived from young adults using connectome-based predictive modeling-to predict WM performance and acceptance strategy use in healthy older adults. Older adults (N = 110) completed baseline assessments as part of a randomized controlled trial examining the impact of mind-body interventions on healthy aging. Our results revealed that the WM networks predicted WM accuracy but not acceptance use or difficulties in emotion regulation in older adults. Individual differences in WM performance, but not WM networks, moderated relationships between image intensity and acceptance use. These findings highlight that robust neural markers of WM generalize to an independent sample of healthy older adults but may not generalize beyond cognitive domains to predict emotion-based behaviors., (© The Author(s) 2023. Published by Oxford University Press.)

Introduction: Abnormal interactions among distributed brain systems are implicated in the mechanisms of nicotine addiction. However, the relationship between the structural covariance network, a measure of brain connectivity, and smoking severity remains unclear. To fill this gap, this study aimed to investigate the relationship between structural covariance network and smoking severity in smokers., Methods: A total of 101 male smokers and 51 male non-smokers were recruited, and they underwent a T1-weighted anatomical image scan. First, an individualized structural covariance network was derived via a jackknife-bias estimation procedure for each participant. Then, a data-driven machine learning method called connectome-based predictive modeling (CPM) was conducted to infer smoking severity measured with Fagerström Test for Nicotine Dependence (FTND) scores using an individualized structural covariance network. The performance of CPM was evaluated using the leave-one-out cross-validation and a permutation testing., Results: As a result, CPM identified the smoking severity-related structural covariance network, as indicated by a significant correlation between predicted and actual FTND scores (r = 0.23, permutation p = 0.020). Identified networks comprised of edges mainly located between the subcortical-cerebellum network and networks including the frontoparietal default model and motor and visual networks., Discussion: These results identified smoking severity-related structural covariance networks and provided a new insight into the neural underpinnings of smoking severity., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Wang, Kang, Niu, Zhang, Li, Gao, Zhang, Cheng and Zhang.)

Abstract: Narcissism is one of the most fundamental personality traits in which individuals in general population exhibit a large heterogeneity. Despite a surge of interest in examining behavioral characteristics of narcissism in the past decades, the neurobiological substrates underlying narcissism remain poorly understood. Here, we addressed this issue by applying a machine learning approach to decode trait narcissism from whole‐brain resting‐state functional connectivity (RSFC). Resting‐state functional MRI (fMRI) data were acquired for a large sample comprising 155 healthy adults, each of whom was assessed for trait narcissism. Using a linear prediction model, we examined the relationship between whole‐brain RSFC and trait narcissism. We demonstrated that the machine‐learning model was able to decode individual trait narcissism from RSFC across multiple neural systems, including functional connectivity between and within limbic and prefrontal systems as well as their connectivity with other networks. Key nodes that contributed to the prediction model included the amygdala, prefrontal and anterior cingulate regions that have been linked to trait narcissism. These findings remained robust using different validation procedures. Our findings thus demonstrate that RSFC among multiple neural systems predicts trait narcissism at the individual level. [ABSTRACT FROM AUTHOR]

A number of studies have indicated that disorders of consciousness result from multifocal injuries as well as from the impaired functional and anatomical connectivity between various anterior forebrain regions. However, the specific causal mechanism linking these regions remains unclear. In this study, we used spectral dynamic causal modeling to assess how the effective connections (ECs) between various regions differ between individuals. Next, we used connectome-based predictive modeling to evaluate the performance of the ECs in predicting the clinical scores of DOC patients. We found increased ECs from the striatum to the globus pallidus as well as from the globus pallidus to the posterior cingulate cortex, and decreased ECs from the globus pallidus to the thalamus and from the medial prefrontal cortex to the striatum in DOC patients as compared to healthy controls. Prediction of the patients’ outcome was effective using the negative ECs as features. In summary, the present study highlights a key role of the thalamo-basal ganglia-cortical loop in DOCs and supports the anterior forebrain mesocircuit hypothesis. Furthermore, EC could be potentially used to assess the consciousness level. [ABSTRACT FROM AUTHOR]

Episodic future thinking (EFT) refers to the critical ability that enables people to construct and pre-experience the vivid mental imagery about future events, which impacts on the decision-making for individuals and group. Although EFT is generally believed to have a visual nature by theorists, little neuroscience evidence has been provided to verify this assumption. Here, by employing the approach of connectome-based predictive modeling (CPM) and graph-theoretical analysis, we analyzed resting-state functional brain image from 191 participants to predict their variability of EFT ability (leave-one-out cross-validation), and validated the results by applying different parcellation schemas and feature selection thresholds. At the connectome strength level, CPM-based analysis revealed that EFT ability could be predicted by the connectome strength of visual network. Besides, at the network level, graph-theoretical analysis showed that EFT ability could be predicted by the network efficiency of visual network. Moreover, these findings were replicated using different parcellation schemas and feature selection thresholds. These results robustly and collectively supported that the visual network might be one of the neural substrates underlying EFT ability from a comprehensive perspective of resting-state functional connectivity strength and the neural network. This study provides indications on how the function of visual network supports EFT ability, and enhances our understanding of the EFT ability from a neural basis perspective. [ABSTRACT FROM AUTHOR]

Objective: Headache is among the most frequent symptoms after coronavirus disease 2019 (COVID-19), so-called long COVID syndrome. Although distinct brain changes have been reported in patients with long COVID, such reported brain changes have not been used for predictions and interpretations in a multivariate manner. In this study, we applied machine learning to assess whether individual adolescents with long COVID can be accurately distinguished from those with primary headaches., Methods: Twenty-three adolescents with long COVID headaches with the persistence of headache for at least 3 months and 23 age- and sex-matched adolescents with primary headaches (migraine, new daily persistent headache, and tension-type headache) were enrolled. Multivoxel pattern analysis (MVPA) was applied for disorder-specific predictions of headache etiology based on individual brain structural MRI. In addition, connectome-based predictive modeling (CPM) was also performed using a structural covariance network., Results: MVPA correctly classified long COVID patients from primary headache patients, with an area under the curve of 0.73 (accuracy = 63.4%; permutation p = 0.001). The discriminating GM patterns exhibited lower classification weights for long COVID in the orbitofrontal and medial temporal lobes. The CPM using the structural covariance network achieved an area under the curve of 0.81 (accuracy = 69.5%; permutation p = 0.005). The edges that classified long COVID patients from primary headache were mainly comprising thalamic connections., Conclusion: The results suggest the potential value of structural MRI-based features for classifying long COVID headaches from primary headaches. The identified features suggest that the distinct gray matter changes in the orbitofrontal and medial temporal lobes occurring after COVID, as well as altered thalamic connectivity, are predictive of headache etiology., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Kim, Sim, Yang and Kim.)

Boys with fragile X syndrome (FXS), the leading known genetic cause of autism spectrum disorder (ASD), demonstrate significant impairments in social gaze and associated weaknesses in communication, social interaction, and other areas of adaptive functioning. Little is known, however, concerning the impact of behavioral treatments for these behaviors on functional brain connectivity in this population. As part of a larger study, boys with FXS (mean age 13.23 ± 2.31 years) and comparison boys with ASD (mean age 12.15 ± 2.76 years) received resting-state functional magnetic resonance imaging scans prior to and following social gaze training administered by a trained behavior therapist in our laboratory. Network-agnostic connectome-based predictive modeling of pretreatment resting-state functional connectivity data revealed a set of positive (FXS > ASD) and negative (FXS < ASD) edges that differentiated the groups significantly and consistently across all folds of cross-validation. Following administration of the brief training, the FXS and ASD groups demonstrated reorganization of connectivity differences. The divergence in the spatial pattern of reorganization response, based on functional connectivity differences pretreatment, suggests a unique pattern of response to treatment in the FXS and ASD groups. These results provide further support for implementing targeted behavioral treatments to ameliorate syndrome-specific behavioral features in FXS., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)