Building a Science of Individual Differences from fMRI

Network Analysis of Human Brain Connectivity Reveals Neural Fingerprints of a Compositionality Bias in Signaling Systems

Compositionality is a hallmark of human language and other symbolic systems: a finite set of meaningful elements can be systematically combined to convey an open-ended array of ideas. Compositionality is not uniformly distributed over expressions in a language or over individuals' communicative behavior: at both levels, variation is observed. Here, we investigate the neural bases of interindividual variability by probing the relationship between intrinsic characteristics of brain networks and compositional behavior. We first collected functional resting-state and diffusion magnetic resonance imaging data from a large participant sample (N = 51). Subsequently, participants took part in two signaling games. They were instructed to learn and reproduce an auditory symbolic system of signals (tone sequences) associated with affective meanings (human faces expressing emotions). Signal-meaning mappings were artificial and had to be learned via repeated signaling interactions. We identified a temporoparietal network in which connection length was related to the degree of compositionality introduced in a signaling system by each player. Graph-theoretic analysis of resting-state functional connectivity revealed that, within that network, compositional behavior was associated with integration measures in 2 semantic hubs: the left posterior cingulate cortex and the left angular gyrus. Our findings link individual variability in compositional biases to variation in the anatomy of semantic networks and in the functional topology of their constituent units.

Prediction of dispositional dialectical thinking from resting-state electroencephalography

This study aims to explore the possibility of predicting the dispositional level of dialectical thinking using resting-state electroencephalography signals. Thirty-four participants completed a self-reported measure of dialectical thinking, and their resting-state electroencephalography was recorded. After wave filtration and eye movement removal, time-frequency electroencephalography signals were converted into four frequency domains: delta (1-4 Hz), theta (4-7 Hz), alpha (7-13 Hz), and beta (13-30 Hz). Functional principal component analysis with B-spline approximation was then applied for feature reduction. Five machine learning methods (support vector regression, least absolute shrinkage and selection operator, K-nearest neighbors, random forest, and gradient boosting decision tree) were applied to the reduced features for prediction. The model ensemble technique was used to create the best performing final model. The results showed that the alpha wave of the electroencephalography signal in the early period (12-15 s) contributed most to the prediction of dialectical thinking. With data-driven electrode selection (FC1, FCz, Fz, FC3, Cz, AFz), the prediction model achieved an average coefficient of determination of 0.45 on 200 random test sets. Furthermore, a significant positive correlation was found between the alpha value of standardized low-resolution electromagnetic tomography activity in the right dorsal anterior cingulate cortex and dialectical self-scale score. The prefrontal and midline alpha oscillations of resting electroencephalography are good predictors of the dispositional level of dialectical thinking, possibly reflecting these brain structures' involvement in dialectical thinking.

Interindividual variability of functional connectome in schizophrenia

Schizophrenia is a complex psychiatric disorder that displays an outstanding interindividual variability in clinical manifestation and neurobiological substrates. A better characterization and quantification of this heterogeneity could guide the search for both common abnormalities (linked to lower intersubject variability) and the presence of biological subtypes (leading to a greater heterogeneity across subjects). In the current study, we address interindividual variability in functional connectome by means of resting-state fMRI in a large sample of patients with schizophrenia and healthy controls. Among the different metrics of distance/dissimilarity used to assess variability, geodesic distance showed robust results to head motion. The main findings of the current study point to (i) a higher between subject heterogeneity in the functional connectome of patients, (ii) variable levels of heterogeneity throughout the cortex, with greater variability in frontoparietal and default mode networks, and lower variability in the salience network, and (iii) an association of whole-brain variability with levels of clinical symptom severity and with topological properties of brain networks, suggesting that the average functional connectome overrepresents those patients with lower functional integration and with more severe clinical symptoms. Moreover, after performing a graph theoretical analysis of brain networks, we found that patients with more severe clinical symptoms had decreased connectivity at both whole-brain level and within the salience network, and that patients with higher negative symptoms had large-scale functional integration deficits.

Striving toward translation: strategies for reliable fMRI measurement

fMRI has considerable potential as a translational tool for understanding risk, prioritizing interventions, and improving the treatment of brain disorders. However, recent studies have found that many of the most widely used fMRI measures have low reliability, undermining this potential. Here, we argue that many fMRI measures are unreliable because they were designed to identify group effects, not to precisely quantify individual differences. We then highlight four emerging strategies [extended aggregation, reliability modeling, multi-echo fMRI (ME-fMRI), and stimulus design] that build on established psychometric properties to generate more precise and reliable fMRI measures. By adopting such strategies to improve reliability, we are optimistic that fMRI can fulfill its potential as a clinical tool.

Dynamic Functional Connectivity of EEG: From Identifying Fingerprints to Gender Differences to a General Blueprint for the Brain's Functional Organization

An individual's brain functional organization is unique and can reliably be observed using modalities such as functional magnetic resonance imaging (fMRI). Here we demonstrate that a quantification of the dynamics of functional connectivity (FC) as measured using electroencephalography (EEG) offers an alternative means of observing an individual's brain functional organization. Using data from both healthy individuals as well as from patients with Parkinson's disease (PD) (n = 103 healthy individuals, n = 57 PD patients), we show that “dynamic FC” (DFC) profiles can be used to identify individuals in a large group. Furthermore, we show that DFC profiles predict gender and exhibit characteristics shared both among individuals as well as between both hemispheres. Furthermore, DFC profile characteristics are frequency band specific, indicating that they reflect distinct processes in the brain. Our empirically derived method of DFC demonstrates the potential of studying the dynamics of the functional organization of the brain using EEG.

Long-term stability of the cortical volumetric profile and the functional human connectome throughout childhood and adolescence

There is compelling evidence showing that between-subject variability in several functional and structural brain features is sufficient for unique identification in adults. However, individuation of brain functional connectomes depends on the stabilization of neurodevelopmental processes during childhood and adolescence. Here, we aimed to (1) evaluate the intra-subject functional connectome stability over time for the whole brain and for large scale functional networks and (2) determine the long-term identification accuracy or 'fingerprinting' for the cortical volumetric profile and the functional connectome. For these purposes, we analysed a longitudinal cohort of 239 children and adolescents scanned in two sessions with an interval of approximately 3 years (age range 6-15 years at baseline and 9-18 years at follow-up). Corroborating previous results using short between-scan intervals in children and adolescents, we observed a moderate identification accuracy (38%) for the whole functional profile. In contrast, identification accuracy using cortical volumetric profile was 95%. Among the large-scale networks, the default-mode (26.8%), the frontoparietal (23.4%) and the dorsal-attention (27.6%) networks were the most discriminative. Our results provide further evidence for a protracted development of specific individual structural and functional connectivity profiles.

Stress Analysis Based on Simultaneous Heart Rate Variability and EEG Monitoring

OBJECTIVE

Stress is a significant risk factor for various diseases such as hypertension, heart attack, stroke, and even sudden death. Stress can also lead to psychological and behavioral disorders. Heart rate variability (HRV) can reflect changes in stress levels while other physiological factors, like blood pressure, are within acceptable ranges. Electroencephalogram (EEG) is a vital technique for studying brain activities and provides useful data regarding changes in mental status. This study incorporates EEG and a detailed HRV analysis to have a better understanding and analysis of stress. Investigating the correlation between EEG and HRV under stress conditions is valuable since they provide complementary information regarding stress.

METHODS

Simultaneous electrocardiogram (ECG) and EEG recordings were obtained from fifteen subjects. HRV /EEG features were analyzed and compared in rest, stress, and meditation conditions. A one-way ANOVA and correlation coefficient were used for statistical analysis to explore the correlation between HRV features and features extracted from EEG.

RESULTS

The HRV features LF (low frequency), HF (high frequency), LF/HF, and rMSSD (root mean square of the successive differences) correlated with EEG features, including alpha power band in the left hemisphere and alpha band power asymmetry.

CONCLUSION

This study demonstrated five significant relationships between EEG and HRV features associated with stress. The ability to use stress-related EEG features in combination with correlated HRV features could help improve detecting stress and monitoring the progress of stress treatments/therapies. The outcomes of this study could enhance the efficiency of stress management technologies such as meditation studies and bio-feedback training.

The Dual Mechanisms of Cognitive Control Project

We describe an ambitious ongoing study that has been strongly influenced and inspired by Don Stuss's career-long efforts to identify key cognitive processes that characterize executive control, investigate potential unifying dimensions that define prefrontal function, and carefully attend to individual differences. The Dual Mechanisms of Cognitive Control project tests a theoretical framework positing two key control dimensions: proactive and reactive. The framework's central tenets are that proactive and reactive control modes reflect domain-general dimensions of individual variation, with distinctive neural signatures, involving the lateral pFC as a central node within associated brain networks (e.g., fronto-parietal, cingulo-opercular). In the Dual Mechanisms of Cognitive Control project, each participant is scanned while performing theoretically targeted variants of multiple well-established cognitive control tasks (Stroop, cued task-switching, AX-CPT, Sternberg working memory) in three separate imaging sessions, that each encourages utilization of different control modes plus also completes an extensive out-of-scanner individual differences battery. Additional key features of the project include a high spatio-temporal resolution (multiband) acquisition protocol and a sample that includes a substantial subset of monozygotic twin pairs and participants recruited from the Human Connectome Project. Although data collection is still continuing (target n = 200), we provide an overview of the study design and protocol, along with initial results (n = 80) revealing evidence of a domain-general neural signature of cognitive control and its modulation under reactive conditions. Aligned with Don Stuss's legacy of scientific community building, a partial data set has been publicly released, with the full data set released at project completion, so it can serve as a valuable resource.

Multiview Diffusion Map Improves Prediction of Fluid Intelligence With Two Paradigms of fMRI Analysis

OBJECTIVE

To understand the association between brain networks and behaviors of an individual, most studies build predictive models based on functional connectivity (FC) from a single dataset with linear analysis techniques. Such approaches may fail to capture the nonlinear structure of brain networks and neglect the complementary information contained in FC networks (FCNs) from multiple datasets. To address this challenging issue, we use multiview dimensionality reduction to extract a coherent low-dimensional representation of the FCNs from resting-state and emotion identification task-based functional magnetic resonance imaging (fMRI) datasets.

METHODS

We propose a scheme based on multiview diffusion map to extract intrinsic features while preserving the underlying geometric structure of high dimensional datasets. This method is robust to noise and small variations in the data.

RESULTS

After validation on the Philadelphia Neurodevelopmental Cohort data, the predictive model built from both resting-state and emotion identification task-based fMRI datasets outperforms the one using each individual fMRI dataset. In addition, the proposed model achieves better prediction performance than principal component analysis (PCA) and three other competing data fusion methods.

CONCLUSION

Our framework for combing multiple FCNs in one predictive model exhibits improved prediction performance.

SIGNIFICANCE

To our knowledge, we demonstrate a first application of multiview diffusion map to successfully fuse different types of fMRI data for predicting fluid intelligence (gF).

Intrinsic functional connectivity of the frontoparietal network predicts inter-individual differences in the propensity for costly third-party punishment

Humans are motivated to give norm violators their just deserts through costly punishment even as unaffected third parties (i.e., third-party punishment, TPP). A great deal of individual variability exists in costly punishment; however, how this variability particularly in TPP is represented by the brain's intrinsic network architecture remains elusive. Here, we examined whether inter-individual differences in the propensity for TPP can be predicted based on resting-state functional connectivity (RSFC) combining an economic TPP game with task-free functional neuroimaging and a multivariate prediction framework. Our behavioral results revealed that TPP punishment increased with the severity of unfairness for offers. People with higher TPP propensity punished more harshly across norm-violating scenarios. Our neuroimaging findings showed RSFC within the frontoparietal network predicted individual differences in TPP propensity. Our findings contribute to understanding the neural fingerprint for an individual's propensity to costly punish strangers, and shed some light on how social norm enforcement behaviors are represented by the brain's intrinsic network architecture.

Intergenerational transmission of the patterns of functional and structural brain networks

There is clear evidence of intergenerational transmission of life values, cognitive traits, psychiatric disorders, and even aspects of daily decision making. To investigate biological substrates of this phenomenon, the brain has received increasing attention as a measurable biomarker and potential target for intervention. However, no previous study has quantitatively and comprehensively investigated the effects of intergenerational transmission on functional and structural brain networks. Here, by employing an unusually large cohort dataset (N = 84 parent-child dyads; 45 sons, 39 daughters, 81 mothers, and 3 fathers), we show that patterns of functional and structural brain networks are preserved over a generation. We also demonstrate that several demographic factors and behavioral/physiological phenotypes have a relationship with brain similarity. Collectively, our results provide a comprehensive picture of neurobiological substrates of intergenerational transmission and demonstrate the usability of our dataset for investigating the neurobiological substrates of intergenerational transmission.

Connectome-based individual prediction of cognitive behaviors via graph propagation network reveals directed brain network topology

Objective. Brain connectivity network supports the information flow underlying human cognitions and should reflect the individual variability in human cognitive behaviors. Various studies have utilized brain connectivity to predict individual differences in human behaviors. However, traditional studies viewed brain connectivity network as a one-dimensional vector, a method which neglects topological properties of brain connectivity network.Approach. To utilize these topological properties, we proposed that graph neural network (GNN) which combines graph theory and neural network can be adopted. Different from previous node-driven GNNs that parameterize on the node feature transformation, we designed an edge-driven GNN named graph propagation network (GPN) that parameterizes on the information propagation within brain connectivity network.Main results.Edge-driven GPN outperforms various baseline models such as node-driven GNN and traditional partial least square regression in predicting the individual total cognition based on the resting-state functional connectome. GPN also reveals a directed network topology encoding the information flow, indicating that higher-order association cortices such as dorsolateral prefrontal, inferior frontal and inferior parietal cortices are responsible for the information integration underlying total cognition.Significance. These results suggest that edge-driven GPN can better explore topological structures of brain connectivity network and can serve as a new method to associate brain connectome and human behaviors.

Widespread plasticity of cognition-related brain networks in single-sided deafness revealed by randomized window-based dynamic functional connectivity

As an extreme type of partial auditory deprivation, single-sided deafness (SSD) has been demonstrated to lead to extensive neural plasticity according to multimodal neuroimaging studies. Among them, resting-state functional magnetic resonance imaging (rs-fMRI) offers valuable information on functional connectivities (FCs). However, most previous SSD rs-fMRI studies assumed that the extracted FC remains stationary during the entire fMRI scan and neglected dynamic functional activities. Existing fixed window-based dynamic FC analysis also ignores dynamic functional activities under different temporal terms. Additionally, due to the cost constraints of using MRI machines, using data-driven methods for unbiased hypothesis investigations may require more effective sample data augmentation techniques. To tackle these challenges and problems together, in this study, we proposed a dynamic window with a random length and position to extract participants' dynamic characteristics under different temporal terms and to extract more information from the dataset. Then, we proposed a nodal efficiency-based correlation matrix to describe the relationships of synergism between regions as features and applied a linear support vector machine (SVM) model to learn the importance of the features, which helped to identify SSD patients and healthy controls. A total of 68 participants (including 23 with left SSD, 20 with right SSD and 25 healthy controls) were enrolled. Our proposed approach with a random window showed clear improvement compared with traditional static and fixed window-based dynamic FC by using the linear SVM model. FCs related to the frontoparietal, somatomotor, dorsal attention, limbic and default mode networks played significant roles in differentiating SSD patients from healthy controls. Additionally, FCs between the somatomotor and frontoparietal networks made the greatest contribution to the classification model. Regarding brain regions, FCs related to the superior frontal gyrus, superior parietal lobule, superior temporal gyrus, amygdala, and orbital gyrus played significant roles. These findings suggest that networks and regions related to higher-order cognitive functions showed the most significant FC alterations in SSD, which may represent a compensatory collaboration of cognitive resources in SSD.

Inferring the heritability of large-scale functional networks with a multivariate ACE modeling approach

Recent evidence suggests that the human functional connectome is stable at different timescales and is unique. These characteristics posit the functional connectome not only as an individual marker but also as a powerful discriminatory measure characterized by high intersubject variability. Among distinct sources of intersubject variability, the long-term sources include functional patterns that emerge from genetic factors. Here, we sought to investigate the contribution of additive genetic factors to the variability of functional networks by determining the heritability of the connectivity strength in a multivariate fashion. First, we reproduced and extended the connectome fingerprinting analysis to the identification of twin pairs. Then, we estimated the heritability of functional networks by a multivariate ACE modeling approach with bootstrapping. Twin pairs were identified above chance level using connectome fingerprinting, with monozygotic twin identification accuracy equal to 57.2% on average for whole-brain connectome. Additionally, we found that a visual (0.37), the medial frontal (0.31), and the motor (0.30) functional networks were the most influenced by additive genetic factors. Our findings suggest that genetic factors not only partially determine intersubject variability of the functional connectome, such that twins can be identified using connectome fingerprinting, but also differentially influence connectivity strength in large-scale functional networks.

The functional connectome is a unique representation of the functional organization of the human brain. As such, it has been extensively used as an individual marker, a “fingerprint,” because of its high intersubject variability. Here, we sought to investigate the influence of genetic factors on intersubject variability of functional networks. Therefore, we extended the connectome fingerprinting analysis to the identification of twin pairs, and we estimated the heritability of functional networks by a multivariate ACE modeling approach with bootstrapping. We found that genetic factors not only partially determine intersubject variability of the functional connectome, such that monozygotic twin identification accuracy achieved 57.2% on average using whole-brain connectome in the fingerprinting analysis, but also differentially influence connectivity strength in large-scale functional networks.

Combining transcranial magnetic stimulation with functional magnetic resonance imaging for probing and modulating neural circuits relevant to affective disorders

Combining transcranial magnetic stimulation (TMS) with functional magnetic resonance imaging offers an unprecedented tool for studying how brain networks interact in vivo and how repetitive trains of TMS modulate those networks among patients diagnosed with affective disorders. TMS compliments neuroimaging by allowing the interrogation of causal control among brain circuits. Together with TMS, neuroimaging can provide valuable insight into the mechanisms underlying treatment effects and downstream circuit communication. Here we provide a background of the method, review relevant study designs, consider methodological and equipment options, and provide statistical recommendations. We conclude by describing emerging approaches that will extend these tools into exciting new applications. This article is categorized under: Psychology > Emotion and Motivation Psychology > Theory and Methods Neuroscience > Clinical Neuroscience.

Tracking positive and negative symptom improvement in first-episode schizophrenia treated with risperidone using individual-level functional connectivity

BACKGROUND

To improve the treatment outcomes of patients with schizophrenia, research efforts have focused on identifying brain-based markers of treatment response. Personal characteristics regarding disease-related behaviors likely stem from inter-individual variability in the organization of brain functional systems. This study aimed to track dimension-specific changes in psychotic symptoms following risperidone treatment using individual-level functional connectivity (FC).

METHODS

A reliable cortical parcellation approach that accounts for individual heterogeneity in cortical functional anatomy was used to localize functional regions in a longitudinal cohort, consisting of 42 drug-naive first-episodes schizophrenia (FES) patients at baseline and after 8 weeks of risperidone treatment. FC was calculated in individually specified brain regions and used to predict the baseline severity and improvement of positive and negative symptoms in FES.

RESULTS

Distinct sets of individual-specific FC were separately associated with the positive and negative symptom burden at baseline, which could be used to track the corresponding symptom resolution in FES patients following risperidone treatment. Between-network connections of the fronto-parietal network (FPN) contributed the most to predicting the positive symptom domain. A combination of between-network connections of the default mode network, FPN, and within-network connections of the FPN contributed markedly to the prediction model of negative symptom.

CONCLUSION

This novel study, which accounts for individual brain variation, take a step toward establishing individual-specific theranostic biomarkers in schizophrenia.

Distributed functional connectivity predicts neuropsychological test performance among older adults

Neuropsychological test is an essential tool in assessing cognitive and functional changes associated with late-life neurocognitive disorders. Despite the utility of the neuropsychological test, the brain-wide neural basis of the test performance remains unclear. Using the predictive modeling approach, we aimed to identify the optimal combination of functional connectivities that predicts neuropsychological test scores of novel individuals. Resting-state functional connectivity and neuropsychological tests included in the OASIS-3 dataset (n = 428) were used to train the predictive models, and the identified models were iteratively applied to the holdout internal test set (n = 216) and external test set (KSHAP, n = 151). We found that the connectivity-based predicted score tracked the actual behavioral test scores (r = 0.08-0.44). The predictive models utilizing most of the connectivity features showed better accuracy than those composed of focal connectivity features, suggesting that its neural basis is largely distributed across multiple brain systems. The discriminant and clinical validity of the predictive models were further assessed. Our results suggest that late-life neuropsychological test performance can be formally characterized with distributed connectome-based predictive models, and further translational evidence is needed when developing theoretically valid and clinically incremental predictive models.

Smooth graph learning for functional connectivity estimation

Functional connectivity (FC) estimated from functional magnetic resonance imaging (fMRI) signals is important in understanding neural representation and information processing in cortical networks. However, due to a lack of "ground truth" FC pattern, the reliability and robustness of FC estimates are usually examined in downstream FC analysis tasks, such as performing participant's identification (also known as "fingerprinting"). In this paper, we propose to learn FC via a smooth graph learning framework. In particular, we treat each time frame of the fMRI time series as a graph signal on an underlying functional brain graph, and estimate the smooth graph functional connectivity (SGFC) by learning the weighted graph adjacency matrix based on graph signal smoothness assumption. We demonstrate that our approach gives rise to a natural and sparse graph representation of FC from which reliable graph measures can be extracted. Reliability of SGFC is evaluated in the context of fingerprinting and compared to correlation FC (CFC). SGFC achieves higher fingerprinting accuracy across several different experiment settings; the improvement is even more significant when a shorter fMRI scanning length is used for FC estimation. In addition to being reliable, we also validate the cognitive relevance of SGFC by using it to predict fluid intelligence. Finally, in evaluating topological measures of the sparse graph, SGFC reveals a more small-world and modular structure compared to CFC. Together, our results suggest that the smooth graph learning framework produces a naturally sparse, reliable, and cognitive-relevant representation of functional connectivity.

Infant functional networks are modulated by state of consciousness and circadian rhythm

Functional connectivity networks are valuable tools for studying development, cognition, and disease in the infant brain. In adults, such networks are modulated by the state of consciousness and the circadian rhythm; however, it is unknown if infant brain networks exhibit similar variation, given the unique temporal properties of infant sleep and circadian patterning. To address this, we analyzed functional connectivity networks calculated from long-term EEG recordings (average duration 20.8 hr) from 19 healthy infants. Networks were subject specific, as intersubject correlations between weighted adjacency matrices were low. However, within individual subjects, both sleep and wake networks were stable over time, with stronger functional connectivity during sleep than wakefulness. Principal component analysis revealed the presence of two dominant networks; visual sleep scoring confirmed that these corresponded to sleep and wakefulness. Lastly, we found that network strength, degree, clustering coefficient, and path length significantly varied with time of day, when measured in either wakefulness or sleep at the group level. Together, these results suggest that modulation of healthy functional networks occurs over ∼24 hr and is robust and repeatable. Accounting for such temporal periodicities may improve the physiological interpretation and use of functional connectivity analysis to investigate brain function in health and disease.

Neural correlates of the production effect: An fMRI study

Recognition memory is improved for items produced at study (e.g., by reading them aloud) relative to a non-produced control condition (e.g., silent reading). This production effect is typically attributed to the extra elements in the production task (e.g., motor activation, auditory perception) enhancing item distinctiveness. To evaluate this claim, the present study examined the neural mechanisms underlying the production effect. Prior to a recognition memory test, different words within a study list were read either aloud, silently, or while saying "check" (as a sensorimotor control condition). Production improved recognition, and aloud words yielded higher rates of both recollection and familiarity judgments than either silent or control words. During encoding, fMRI revealed stronger activation in regions associated with motor, somatosensory, and auditory processing for aloud items than for either silent or control items. These activations were predictive of recollective success for aloud items at test. Together, our findings are compatible with a distinctiveness-based account of the production effect, while also pointing to the possible role of other processing differences during the aloud trials as compared to silent and control.

Decomposition of individual-specific and individual-shared components from resting-state functional connectivity using a multi-task machine learning method

Resting-state functional connectivity (RSFC) can be used for mapping large-scale human brain networks during rest. There is considerable interest in distinguishing the individual-shared and individual-specific components in RSFC for the better identification of individuals and prediction of behavior. Therefore, we propose a multi-task learning based sparse convex alternating structure optimization (MTL-sCASO) method to decompose RSFC into individual-specific connectivity and individual-shared connectivity. We used synthetic data to validate the efficacy of the MTL-sCASO method. In addition, we verified that individual-specific connectivity achieves higher identification rates than the Pearson correlation (PC) method, and the individual-specific components observed in 886 individuals from the Human Connectome Project (HCP) examined in two sessions over two consecutive days might serve as individual fingerprints. Individual-specific connectivity has low inter-subject similarity (-0.005±0.023), while individual-shared connectivity has high inter-subject similarity (0.822±0.061). We also determined the anatomical locations (region or subsystem) related to individual attributes and common features. We find that individual-specific connectivity exhibits low degree centrality in the sensorimotor processing system but high degree centrality in the control system. Importantly, the individual-specific connectivity estimated by the MTL-sCASO method accurately predicts behavioral scores (improved by 9.4% compared to the PC method) in the cognitive dimension. The decomposition of individual-specific and individual-shared components from RSFC provides a new approach for tracing individual traits and group analysis using functional brain networks.

Identification of specific neural circuit underlying the key cognitive deficit of remitted late-onset depression: A multi-modal MRI and machine learning study

Neuropsychological impairment is a key feature of late-onset depression (LOD), with deficits observed across multiple cognitive domains. And this neuropsychological impairment can persist even after the remission of depressive symptoms. However, none of previous studies have explored the pattern of cognitive deficit in remitted LOD (rLOD), and investigated the specific neural circuit underlying the key cognitive deficit of LOD. 40 rLOD patients and 36 controls underwent comprehensive neuropsychological assessments and magnetic resonance imaging (MRI) scans. The influence of executive function or information processing speed deficit on other cognitive domains was first investigated. We then applied a multivariate machine learning technique known as relevance vector regression to evaluate the potential of multiple-modal MRI (i.e., integrating whole-brain grey-matter [GM] volume and white-matter [WM] tract features) for making accurate predictions about the key cognitive deficit for individual rLOD patient. We revealed that the information processing speed appears to represent a key cognitive deficit in rLOD. Further the machine learning model identified a wide range of GM regions and WM tracts that significantly contributed to the prediction of individual performance on information processing speed (r = 0.50, P < 0.001). The GM regions mainly located in the frontal-subcortical and limbic systems; and the WM tracts mainly located in the frontal-limbic pathway, including the anterior corona radiata, fornix, posterior cingulate bundle, and uncinate fasciculus. This present study provide strongly evidence supporting the concept of rLOD that the core aspect of the cognitive deficits (i.e., information processing speed) is associated with disruption of the frontal-subcortical-limbic pathway.

Feature selection framework for functional connectome fingerprinting

The ability to uniquely characterize individual subjects based on their functional connectome (FC) is a key requirement for progress toward precision psychiatry. FC fingerprinting is increasingly studied in the neuroimaging community for this purpose, where a variety of approaches have been developed for effective FC fingerprinting. Recent independent studies showed that fingerprinting accuracy suffers at large sample sizes and when coarser parcellations are used for computing the FC. Quantifying this problem and understanding the reasons these factors impact fingerprinting accuracy is crucial to develop more accurate fingerprinting methods for large sample sizes. Part of the challenge in fingerprinting is that FC captures both generic and subject‐specific information. A systematic approach for identifying subject‐specific FC information is crucial for making progress in addressing the fingerprinting problem. In this study, we addressed three gaps in our understanding of the FC fingerprinting problem. First, we studied the joint effect of sample size and parcellation granularity. Second, we explained the reason for reduced fingerprinting accuracy with increased sample size and reduced parcellation granularity. To this end, we used a clustering quality metric from the data mining community. Third, we developed a general feature selection framework for systematically identifying resting‐state functional connectivity (RSFC) elements that capture information to uniquely identify subjects. In sum, we evaluated six different approaches from this framework by quantifying both subject‐specific fingerprinting accuracy and the decrease in accuracy with an increase in sample size to identify which approach improved quality metrics the most.

Subject identification using edge-centric functional connectivity

Group-level studies do not capture individual differences in network organization, an important prerequisite for understanding neural substrates shaping behavior and for developing interventions in clinical conditions. Recent studies have employed 'fingerprinting' analyses on functional connectivity to identify subjects' idiosyncratic features. Here, we develop a complementary approach based on an edge-centric model of functional connectivity, which focuses on the co-fluctuations of edges. We first show whole-brain edge functional connectivity (eFC) to be a robust substrate that improves identifiability over nodal FC (nFC) across different datasets and parcellations. Next, we characterize subjects' identifiability at different spatial scales, from single nodes to the level of functional systems and clusters using k-means clustering. Across spatial scales, we find that heteromodal brain regions exhibit consistently greater identifiability than unimodal, sensorimotor, and limbic regions. Lastly, we show that identifiability can be further improved by reconstructing eFC using specific subsets of its principal components. In summary, our results highlight the utility of the edge-centric network model for capturing meaningful subject-specific features and sets the stage for future investigations into individual differences using edge-centric models.

White matter variability in auditory callosal pathways contributes to variation in the cultural transmission of auditory symbolic systems

The cultural transmission of spoken language and music relies on human capacities for encoding and recalling auditory patterns. In this experiment, we show that interindividual differences in this ability are associated with variation in the organization of cross-callosal white matter pathways. First, high-angular resolution diffusion MRI (dMRI) data were analyzed in a large participant sample (N = 51). Subsequently, these participants underwent a behavioral test that models in the laboratory the cultural transmission of auditory symbolic systems: the signaling game. Cross-callosal and intrahemispheric (arcuate fasciculus) pathways were reconstructed and analyzed using conventional diffusion tensor imaging (DTI) as well as a more advanced dMRI technique: fixel-based analysis (FBA). The DTI metric of fractional anisotropy (FA) in auditory callosal pathways predicted-weeks after scanning-the fidelity of transmission of an artificial tone system. The ability to coherently transmit auditory signals in one signaling game, irrespective of the signals learned during the previous game, was predicted by morphological properties of the fiber bundles in the most anterior portions of the corpus callosum. The current study is the first application of dMRI in the field of cultural transmission, and the first to connect individual characteristics of callosal pathways to core behaviors in the transmission of auditory symbolic systems.

Antagonism between brain regions relevant for cognitive control and emotional memory facilitates the generation of humorous ideas

The ability to generate humor gives rise to positive emotions and thus facilitate the successful resolution of adversity. Although there is consensus that inhibitory processes might be related to broaden the way of thinking, the neural underpinnings of these mechanisms are largely unknown. Here, we use functional Magnetic Resonance Imaging, a humorous alternative uses task and a stroop task, to investigate the brain mechanisms underlying the emergence of humorous ideas in 24 subjects. Neuroimaging results indicate that greater cognitive control abilities are associated with increased activation in the amygdala, the hippocampus and the superior and medial frontal gyrus during the generation of humorous ideas. Examining the neural mechanisms more closely shows that the hypoactivation of frontal brain regions is associated with an hyperactivation in the amygdala and vice versa. This antagonistic connectivity is concurrently linked with an increased number of humorous ideas and enhanced amygdala responses during the task. Our data therefore suggests that a neural antagonism previously related to the emergence and regulation of negative affective responses, is linked with the generation of emotionally positive ideas and may represent an important neural pathway supporting mental health.

Neural representations of the amount and the delay time of reward in intertemporal decision making

Numerous studies have examined the neural substrates of intertemporal decision-making, but few have systematically investigated separate neural representations of the two attributes of future rewards (i.e., the amount of the reward and the delay time). More importantly, no study has used the novel analytical method of representational connectivity analysis (RCA) to map the two dimensions' functional brain networks at the level of multivariate neural representations. This study independently manipulated the amount and delay time of rewards during an intertemporal decision task. Both univariate and multivariate pattern analyses showed that brain activity in the dorsomedial prefrontal cortex (DMPFC) and lateral frontal pole cortex (LFPC) was modulated by the amount of rewards, whereas brain activity in the DMPFC and dorsolateral prefrontal cortex (DLPFC) was modulated by the length of delay. Moreover, representational similarity analysis (RSA) revealed that even for the regions of the DMPFC that overlapped between the two dimensions, they manifested distinct neural activity patterns. In terms of individual differences, those with large delay discounting rates (k) showed greater DMPFC and LFPC activity as the amount of rewards increased but showed lower DMPFC and DLPFC activity as the delay time increased. Lastly, RCA suggested that the topological metrics (i.e., global and local efficiency) of the functional connectome subserving the delay time dimension inversely predicted individual discounting rate. These findings provide novel insights into neural representations of the two attributes in intertemporal decisions, and offer a new approach to construct task-based functional brain networks whose topological properties are related to impulsivity.

The Brain Alteration of Seafarer Revealed by Activated Functional Connectivity Mode in fMRI Data Analysis

As a special occupational group, the working and living environments faced by seafarers are greatly different from those of land. It is easy to affect the psychological and physiological activities of seafarers, which inevitably lead to changes in the brain functional activities of seafarers. Therefore, it is of great significance to study the neural activity rules of seafarers' brain. In view of this, this paper studied the seafarers' brain alteration at the activated voxel level based on functional magnetic resonance imaging technology by comparing the differences in functional connectivities (FCs) between seafarers and non-seafarers. Firstly, the activated voxels of each group were obtained by independence component analysis, and then the distribution of these voxels in the brain and the common activated voxels between the two groups were statistically analyzed. Next, the FCs between the common activated voxels of the two groups were calculated and obtained the FCs that had significant differences between them through two-sample T-test. Finally, all FCs and FCs with significant differences (DFCs) between the common activated voxels were used as the features for the support vector machine to classify seafarers and non-seafarers. The results showed that DFCs between the activated voxels had better recognition ability for seafarers, especially for Precuneus_L and Precuneus_R, which may play an important role in the classification prediction of seafarers and non-seafarers, so that provided a new perspective for studying the specificity of neurological activities of seafarers.

Intranasal vasopressin modulates resting state brain activity across multiple neural systems: Evidence from a brain imaging machine learning study

Arginine vasopressin (AVP), a neuropeptide with widespread receptors in brain regions important for socioemotional processing, is critical in regulating various mammalian social behavior and emotion. Although a growing body of task-based brain imaging studies have revealed the effects of AVP on brain activity associated with emotion processing, social cognition and behaviors, the potential modulations of AVP on resting-state brain activity remain largely unknown. Here, the current study addressed this issue by adopting a machine learning approach to distinguish administration of AVP and placebo, employing the amplitude of low-frequency fluctuation (ALFF) as a measure of resting-state brain activity. The brain regions contributing to the classification were then subjected to functional connectivity and decoding analyses, allowing for a data-driven quantitative inference on psychophysiological functions. Our results indicated that ALFF across multiple neural systems were sufficient to distinguish between AVP and placebo at individual level, with the contributing regions distributed across the social cognition network, sensorimotor regions and emotional processing network. These findings suggest that the role of AVP in socioemotional functioning recruits multiple brain networks distributed across the whole brain rather than specific localized neural pathways. Beyond these findings, the current data-driven approach also opens a novel avenue to delineate neural underpinnings of various neuropeptides or hormones.

Movie-watching outperforms rest for functional connectivity-based prediction of behavior

A major goal of human neuroscience is to relate differences in brain function to differences in behavior across people. Recent work has established that whole-brain functional connectivity patterns are relatively stable within individuals and unique across individuals, and that features of these patterns predict various traits. However, while functional connectivity is most often measured at rest, certain tasks may enhance individual signals and improve sensitivity to behavior differences. Here, we show that compared to the resting state, functional connectivity measured during naturalistic viewing—i.e., movie watching—yields more accurate predictions of trait-like phenotypes in the domains of both cognition and emotion. Traits could be predicted using less than three minutes of data from single video clips, and clips with highly social content gave the most accurate predictions. Results suggest that naturalistic stimuli amplify individual differences in behaviorally relevant brain networks.

Stressed, depressed, and rank obsessed: Individual differences in compassion and neuroticism predispose towards rank-based depressive symptomatology

OBJECTIVES

As social creatures, we monitor our relative rank and/or status with others via social comparisons. Whilst research has identified perceptions of inferiority or 'low rank' relative to others is a robust predictor of depressive, anxious, and stress symptomology, to date individual differences have been ignored. We wish to provide empirical evidence to outline how differences across personality traits may interact with social rank variables to buffer or predispose towards depressive symptomology.

METHODS

Across three independent samples (N = 595), we replicated a social rank model of mental health, and with our third sample (N = 200), we sought to investigate attenuating roles for neuroticism versus compassion with multiple moderated regression models.

RESULTS

Neuroticism predicted greater levels of rank-associated depression, and compassion failed to function as a protective factor for rank-associated depression. However, a closer inspection of the original Big-5 factor structure positions this scale as a measure of 'interpersonal submissiveness' or 'conflict appeasement' rather than genuine compassion.

CONCLUSIONS

Whilst it is necessary to delineate the conditions where compassion is appropriate and able to lead to positive mental health outcomes, we argue this cannot be addressed with the Big-5 measure of trait compassion. We call for future work to consider valid and reliable measures for compassion, such as the self-compassion scale, submissive compassion scale, and fears of compassion scale, to more fully address how compassion may protect against both rank-based comparisons and severity of depression.

PRACTITIONER POINTS

Social rank mechanisms are robustly implicated in depression, anxiety, and stress. Clients who present as higher in neuroticism, inferiority, or submissiveness may be more prone towards rank-associated depression symptoms. Preliminary evidence suggests cultivation of genuine compassion can shift clients from a rank-focussed to a compassionate-focussed mentality, which aids mental health and fosters well-being.

Altered Task-Evoked Corticolimbic Responsivity in Generalized Anxiety Disorder

Generalized anxiety disorder (GAD) is marked by uncontrollable, persistent worry and exaggerated response to uncertainty. Here, we review and summarize the findings from the GAD literature that employs functional neuroimaging methods. In particular, the present review focuses on task-based blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) studies. We find that select brain regions often regarded as a part of a corticolimbic circuit (e.g., amygdala, anterior cingulate cortex, prefrontal cortex) are consistently targeted for a priori hypothesis-driven analyses, which, in turn, shows varying degrees of abnormal BOLD responsivity in GAD. Data-driven whole-brain analyses show the insula and the hippocampus, among other regions, to be affected by GAD, depending on the task used in each individual study. Overall, while the heterogeneity of the tasks and sample size limits the generalizability of the findings thus far, some promising convergence can be observed in the form of the altered BOLD responsivity of the corticolimbic circuitry in GAD.

Attention recruits frontal cortex in human infants

Young infants learn about the world by overtly shifting their attention to perceptually salient events. In adults, attention recruits several brain regions spanning the frontal and parietal lobes. However, it is unclear whether these regions are sufficiently mature in infancy to support attention and, more generally, how infant attention is supported by the brain. We used event-related functional magnetic resonance imaging (fMRI) in 24 sessions from 20 awake behaving infants 3 mo to 12 mo old while they performed a child-friendly attentional cuing task. A target was presented to either the left or right of the infant's fixation, and offline gaze coding was used to measure the latency with which they saccaded to the target. To manipulate attention, a brief cue was presented before the target in three conditions: on the same side as the upcoming target (valid), on the other side (invalid), or on both sides (neutral). All infants were faster to look at the target on valid versus invalid trials, with valid faster than neutral and invalid slower than neutral, indicating that the cues effectively captured attention. We then compared the fMRI activity evoked by these trial types. Regions of adult attention networks activated more strongly for invalid than valid trials, particularly frontal regions. Neither behavioral nor neural effects varied by infant age within the first year, suggesting that these regions may function early in development to support the orienting of attention. Together, this furthers our mechanistic understanding of how the infant brain controls the allocation of attention.

The Amsterdam Open MRI Collection, a set of multimodal MRI datasets for individual difference analyses

We present the Amsterdam Open MRI Collection (AOMIC): three datasets with multimodal (3 T) MRI data including structural (T1-weighted), diffusion-weighted, and (resting-state and task-based) functional BOLD MRI data, as well as detailed demographics and psychometric variables from a large set of healthy participants (N = 928, N = 226, and N = 216). Notably, task-based fMRI was collected during various robust paradigms (targeting naturalistic vision, emotion perception, working memory, face perception, cognitive conflict and control, and response inhibition) for which extensively annotated event-files are available. For each dataset and data modality, we provide the data in both raw and preprocessed form (both compliant with the Brain Imaging Data Structure), which were subjected to extensive (automated and manual) quality control. All data is publicly available from the OpenNeuro data sharing platform.

Neuroanatomical and functional substrates of the greed personality trait

Greedy individuals often exhibit more impulsive decision-making and short-sighted behaviors. It has been assumed that altered reward circuitry and prospection network is associated with greed personality trait (GPT). In this study, we first explored the morphological characteristics (i.e., gray matter volume; GMV) of GPT combined with univariate and multivariate pattern analysis (MVPA) approaches. Second, we adopted a revised version of inter-temporal choice task and independently manipulated the amount and delay time of future rewards. Using brain-imaging design, reward- and prospection-related brain activations were assessed and their associations with GPT were further examined. The MVPA results showed that GPT was associated with the GMVs in the right lateral frontal pole cortex, left ventromedial prefrontal cortex, right lateral occipital cortex, and right occipital pole. Additionally, we observed that the amount-relevant brain activations (responding to reward circuitry) in the lateral orbitofrontal cortex were negatively associated with individual's variability in GPT scores, whereas the delay time-relevant brain activations (responding to prospection network system) in the dorsolateral prefrontal cortex, dorsomedial prefrontal cortex, superior parietal lobule, and anterior cingulate cortex were positively associated with individual's variability in GPT scores. These findings not only provide novel insights into the neuroanatomical substrates underlying the human dispositional greed, but also suggest the critical roles of reward and prospection processing on the greed.

Test-retest reliability of emotion regulation networks using fMRI at ultra-high magnetic field

Given the importance of emotion regulation in affective disorders, emotion regulation is at the focus of attempts to identify brain biomarkers of disease risk, treatment response, and brain development. However, to be useful as an indicator for individual characteristics of brain functions - particularly as a biomarker in a clinical context - ensuring reliability is a key challenge. Here, we systematically evaluated test-retest reliability of task-based functional magnetic resonance imaging (fMRI) activity within neural networks associated with emotion generation and regulation across three sessions. Acquiring fMRI data at ultra-high field (7T), we examined region- and voxel-wise test-retest reliability of brain activity in response to a well-established emotion regulation task for predefined region-of-interests (ROIs) implicated in four neural networks. Test-retest reliability varied considerably across the emotion regulation networks and respective ROIs. However, core emotion regulation regions, including the ventrolateral and dorsolateral prefrontal cortex (vlPFC and dlPFC) as well as the middle temporal gyrus (MTG) showed high reliability. Our findings thus support the role of these prefrontal and temporal regions as promising candidates for the study of individual differences in emotion regulation as well as for neurobiological biomarkers in clinical neuroscience research.

Personalized connectivity-guided DLPFC-TMS for depression: Advancing computational feasibility, precision and reproducibility

Repetitive transcranial magnetic stimulation (rTMS) of the dorsolateral prefrontal cortex (DLPFC) is an established treatment for refractory depression, however, therapeutic outcomes vary. Mounting evidence suggests that clinical response relates to functional connectivity with the subgenual cingulate cortex (SGC) at the precise DLPFC stimulation site. Critically, SGC-related network architecture shows considerable interindividual variation across the spatial extent of the DLPFC, indicating that connectivity-based target personalization could potentially be necessary to improve treatment outcomes. However, to date accurate personalization has not appeared feasible, with recent work indicating that the intraindividual reproducibility of optimal targets is limited to 3.5 cm. Here we developed reliable and accurate methodologies to compute individualized connectivity-guided stimulation targets. In resting-state functional MRI scans acquired across 1,000 healthy adults, we demonstrate that, using this approach, personalized targets can be reliably and robustly pinpointed, with a median accuracy of ~2 mm between scans repeated across separate days. These targets remained highly stable, even after 1 year, with a median intraindividual distance between coordinates of only 2.7 mm. Interindividual spatial variation in personalized targets exceeded intraindividual variation by a factor of up to 6.85, suggesting that personalized targets did not trivially converge to a group-average site. Moreover, personalized targets were heritable, suggesting that connectivity-guided rTMS personalization is stable over time and under genetic control. This computational framework provides capacity for personalized connectivity-guided TMS targets to be robustly computed with high precision and has the flexibly to advance research in other basic research and clinical applications.

Connectome-Based Predictive Modeling of Individual Anxiety

Anxiety-related illnesses are highly prevalent in human society. Being able to identify neurobiological markers signaling high trait anxiety could aid the assessment of individuals with high risk for mental illness. Here, we applied connectome-based predictive modeling (CPM) to whole-brain resting-state functional connectivity (rsFC) data to predict the degree of trait anxiety in 76 healthy participants. Using a computational “lesion” approach in CPM, we then examined the weights of the identified main brain areas as well as their connectivity. Results showed that the CPM successfully predicted individual anxiety based on whole-brain rsFC, especially the rsFC between limbic areas and prefrontal cortex. The prediction power of the model significantly decreased from simulated lesions of limbic areas, lesions of the connectivity within limbic areas, and lesions of the connectivity between limbic areas and prefrontal cortex. Importantly, this neural model generalized to an independent large sample (n = 501). These findings highlight important roles of the limbic system and prefrontal cortex in anxiety prediction. Our work provides evidence for the usefulness of connectome-based modeling in predicting individual personality differences and indicates its potential for identifying personality factors at risk for psychopathology.

Individual-fMRI-approaches reveal cerebellum and visual communities to be functionally connected in obsessive compulsive disorder

There is significant interest in understanding the pathophysiology of Obsessive-Compulsive Disorder (OCD) using resting-state fMRI (rsfMRI). Previous studies acknowledge abnormalities within and beyond the fronto-striato-limbic circuit in OCD that require further clarifications. However, limited information could be inferred from the conventional way of investigating the functional connectivity differences between OCD and healthy controls. Here, we identified altered brain organization in patients with OCD by applying individual-based approaches to maximize the identification of underlying network-based features specific to the OCD group. rsfMRI of 20 patients with OCD and 22 controls were preprocessed, and individual-fMRI-subspace was derived for each subject within each group. We evaluated group differences in functional connectivity using individual-fMRI-subspace and established its advantage over conventional-fMRI methodology. We applied prediction-based approaches to highlight the group differences by evaluating the differences in functional connections that predicted the clinical scores (namely, the Obsessive-Compulsive Inventory-Revised (OCI-R) and Hamilton Anxiety Rating Scale). Then, we explored the brain network organization of both groups by estimating the subject-specific communities within each group. Lastly, we evaluated associations between the inter-individual variation of nodes in the communities to clinical measures using linear regression. Functional connectivity analysis using individual-fMRI-subspace detected 83 connections that were different between OCD and control groups, compared to none found using conventional-fMRI methodology. Connectome-based prediction analysis did not show significant overlap between the two groups in the functional connections that predicted the clinical scores. This suggests that the functional architecture in patients with OCD may be different compared to controls. Seven communities were found in both groups. Interestingly, within the OCD group but not controls, we observed functional connectivity between cerebellar and visual regions, and lack of connectivity between striato-limbic and frontal areas. Inter-individual variations in the community-size of these two communities were also associated with the OCI-R score (p < .005). Due to our small sample size, we further validated our results by (i) accounting for head motion, (ii) applying global signal regression (GSR) in data processing, and (iii) using an alternate atlas for parcellation. While the main results were consistently observed with accounting for head motion and using another atlas, the key findings were not reproduced with GSR application. The study demonstrated the existence of disconnectedness in fronto-striato-limbic community and connectedness between cerebellar and visual areas in OCD patients, which was also related to the clinical symptomatology of OCD.

Accounting for Subjectivity in Experimental Research on Human Olfaction

Although olfaction is a modality with great interindividual perceptual disparities, its subjective dimension has been let aside in modern research, in line with the overall neglect of consciousness in experimental psychology. However, following the renewed interest for the neural bases of consciousness, some methodological leads have been proposed to include subjectivity in experimental protocols. Here, we argue that adapting such methods to the field of olfaction will allow to rigorously acquire subjective reports, and we present several ways to do so. This will improve the understanding of diversity in odor perception and its underlying neural mechanisms.

Exploring valence bias as a metric for frontoamygdalar connectivity and depressive symptoms in childhood

Negativity bias is a core feature of depression that is associated with dysfunctional frontoamygdalar connectivity; this pathway is associated with emotion regulation and sensitive to neurobiological change during puberty. We used a valence bias task (ratings of emotional ambiguity) as a potential early indicator of depression risk and differences in frontoamygdalar connectivity. Previous work using this task demonstrated that children normatively have a negative bias that attenuates with maturation. Here, we test the hypothesis that persistence of this negativity bias as maturation ensues may reveal differences in emotion regulation development, and may be associated with increased risk for depression. In children aged 6-13 years, we tested the moderating role of puberty on relationships between valence bias, depressive symptoms, and frontoamygdalar connectivity. A negative bias was associated with increased depressive symptoms for those at more advanced pubertal stages (within this sample) and less regulatory frontoamygdalar connectivity, whereas a more positive bias was associated with more regulatory connectivity patterns. These data suggest that with maturation, individual differences in positivity biases and associated emotion regulation circuitry confer a differential risk for depression. Longitudinal work is necessary to determine the directionality of these effects and explore the influence of early life events.

Internal reliability of blame-related functional MRI measures in major depressive disorder

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Self-blame-related fMRI measures were previously validated in depressive disorders.

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Reproducibility and internal consistency as a measure of reliability were examined.

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Whilst simple fMRI measures exhibited fair reliability, complex measures did not.

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Yet, complex measures showed reproducible clinical validity at the group level.

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Connectivity measures, that balance reliability and validity better, are needed.

Background

Methods

Results

Conclusions

Individualizing Representational Similarity Analysis

Representational similarity analysis (RSA) is a popular multivariate analysis technique in cognitive neuroscience that uses functional neuroimaging to investigate the informational content encoded in brain activity. As RSA is increasingly being used to investigate more clinically-geared questions, the focus of such translational studies turns toward the importance of individual differences and their optimization within the experimental design. In this perspective, we focus on two design aspects: applying individual vs. averaged behavioral dissimilarity matrices to multiple participants' neuroimaging data and ensuring the congruency between tasks when measuring behavioral and neural representational spaces. Incorporating these methods permits the detection of individual differences in representational spaces and yields a better-defined transfer of information from representational spaces onto multivoxel patterns. Such design adaptations are prerequisites for optimal translation of RSA to the field of precision psychiatry.

Neural Correlates of Cognitive Dysfunctions in Cervical Spondylotic Myelopathy Patients: A Resting-State fMRI Study

Cervical spondylotic myelopathy (CSM) is a common disease of the elderly that is characterized by gait instability, sensorimotor deficits, etc. Recurrent symptoms including memory loss, poor attention, etc. have also been reported in recent studies. However, these have been rarely investigated in CSM patients. To investigate the cognitive deficits and their correlation with brain functional alterations, we conducted resting-state fMRI (rs-fMRI) signal variability. This is a novel indicator in the neuroimaging field for assessing the regional neural activity in CSM patients. Further, to explore the network changes in patients, functional connectivity (FC) and graph theory analyses were performed. Compared with the controls, the signal variabilities were significantly lower in the widespread brain regions especially at the default mode network (DMN), visual network, and somatosensory network. The altered inferior parietal lobule signal variability positively correlated with the cognitive function level. Moreover, the FC and the global efficiency of DMN increased in patients with CSM and positively correlated with the cognitive function level. According to the study results, (1) the cervical spondylotic myelopathy patients exhibited regional neural impairments, which correlated with the severity of cognitive deficits in the DMN brain regions, and (2) the increased FC and global efficiency of DMN can compensate for the regional impairment.

Individualized Prediction of PTSD Symptom Severity in Trauma Survivors From Whole-Brain Resting-State Functional Connectivity

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.

Individual Differences in Brain Responses: New Opportunities for Tailoring Health Communication Campaigns

Prevention neuroscience investigates the brain basis of attitude and behavior change. Over the years, an increasingly structurally and functionally resolved "persuasion network" has emerged. However, current studies have only identified a small handful of neural structures that are commonly recruited during persuasive message processing, and the extent to which these (and other) structures are sensitive to numerous individual difference factors remains largely unknown. In this project we apply a multi-dimensional similarity-based individual differences analysis to explore which individual factors-including characteristics of messages and target audiences-drive patterns of brain activity to be more or less similar across individuals encountering the same anti-drug public service announcements (PSAs). We demonstrate that several ensembles of brain regions show response patterns that are driven by a variety of unique factors. These results are discussed in terms of their implications for neural models of persuasion, prevention neuroscience and message tailoring, and methodological implications for future research.