Individual variation in functional brain connectivity: implications for personalized approaches to psychiatric disease

Specific Patterns of Endogenous Functional Connectivity Are Associated With Harm Avoidance in Obsessive-Compulsive Disorder

BACKGROUND

Individuals with obsessive-compulsive disorder (OCD) show persistent avoidance behaviors, often in the absence of actual threat. Quality-of-life costs and heterogeneity support the need for novel brain-behavior intervention targets. Informed by mechanistic and anatomical studies of persistent avoidance in rodents and nonhuman primates, our goal was to test whether connections within a hypothesized persistent avoidance-related network predicted OCD-related harm avoidance (HA), a trait measure of persistent avoidance. We hypothesized that 1) HA, not an OCD diagnosis, would be associated with altered endogenous connectivity in at least one connection in the network; 2) HA-specific findings would be robust to comorbid symptoms; and 3) reliable findings would replicate in a holdout testing subsample.

METHODS

Using resting-state functional connectivity magnetic resonance imaging, cross-validated elastic net for feature selection, and Poisson generalized linear models, we tested which connections significantly predicted HA in our training subsample (n = 73; 71.8% female; healthy control group n = 36, OCD group n = 37); robustness to comorbidities; and replicability in a testing subsample (n = 30; 56.7% female; healthy control group n = 15, OCD group n = 15).

RESULTS

Stronger inverse connectivity between the right dorsal anterior cingulate cortex and right basolateral amygdala and stronger positive connectivity between the right ventral anterior insula and left ventral striatum were associated with greater HA across groups. Network connections did not discriminate OCD diagnostic status or predict HA-correlated traits, suggesting sensitivity to trait HA. The dorsal anterior cingulate cortex-basolateral amygdala relationship was robust to controlling for comorbidities and medication in individuals with OCD and was also predictive of HA in our testing subsample.

CONCLUSIONS

Stronger inverse dorsal anterior cingulate cortex-basolateral amygdala connectivity was robustly and reliably associated with HA across groups and in OCD. Results support the relevance of a cross-species persistent avoidance-related network to OCD, with implications for precision-based approaches and treatment.

Individual connectivity-based parcellations reflect functional properties of human auditory cortex

Neuroimaging studies of the functional organization of human auditory cortex have focused on group-level analyses to identify tendencies that represent the typical brain. Here, we mapped auditory areas of the human superior temporal cortex (STC) in 30 participants by combining functional network analysis and 1-mm isotropic resolution 7T functional magnetic resonance imaging (fMRI). Two resting-state fMRI sessions, and one or two auditory and audiovisual speech localizer sessions, were collected on 3-4 separate days. We generated a set of functional network-based parcellations from these data. Solutions with 4, 6, and 11 networks were selected for closer examination based on local maxima of Dice and Silhouette values. The resulting parcellation of auditory cortices showed high intraindividual reproducibility both between resting state sessions (Dice coefficient: 69-78%) and between resting state and task sessions (Dice coefficient: 62-73%). This demonstrates that auditory areas in STC can be reliably segmented into functional subareas. The interindividual variability was significantly larger than intraindividual variability (Dice coefficient: 57%-68%, p<0.001), indicating that the parcellations also captured meaningful interindividual variability. The individual-specific parcellations yielded the highest alignment with task response topographies, suggesting that individual variability in parcellations reflects individual variability in auditory function. Connectional homogeneity within networks was also highest for the individual-specific parcellations. Furthermore, the similarity in the functional parcellations was not explainable by the similarity of macroanatomical properties of auditory cortex. Our findings suggest that individual-level parcellations capture meaningful idiosyncrasies in auditory cortex organization.

Disentangling the Individual-Shared and Individual-Specific Subspace of Altered Brain Functional Connectivity in Autism Spectrum Disorder

BACKGROUND

Despite considerable effort toward understanding the neural basis of autism spectrum disorder (ASD) using case-control analyses of resting-state functional magnetic resonance imaging data, findings are often not reproducible, largely due to biological and clinical heterogeneity among individuals with ASD. Thus, exploring the individual-shared and individual-specific altered functional connectivity (AFC) in ASD is important to understand this complex, heterogeneous disorder.

METHODS

We considered 254 individuals with ASD and 295 typically developing individuals from the Autism Brain Imaging Data Exchange to explore the individual-shared and individual-specific subspaces of AFC. First, we computed AFC matrices of individuals with ASD compared with typically developing individuals. Then, common orthogonal basis extraction was used to project AFC of ASD onto 2 subspaces: an individual-shared subspace, which represents altered connectivity patterns shared across ASD, and an individual-specific subspace, which represents the remaining individual characteristics after eliminating the individual-shared altered connectivity patterns.

RESULTS

Analysis yielded 3 common components spanning the individual-shared subspace. Common components were associated with differences of functional connectivity at the group level. AFC in the individual-specific subspace improved the prediction of clinical symptoms. The default mode network-related and cingulo-opercular network-related magnitudes of AFC in the individual-specific subspace were significantly correlated with symptom severity in social communication deficits and restricted, repetitive behaviors in ASD.

CONCLUSIONS

Our study decomposed AFC of ASD into individual-shared and individual-specific subspaces, highlighting the importance of capturing and capitalizing on individual-specific brain connectivity features for dissecting heterogeneity. Our analysis framework provides a blueprint for parsing heterogeneity in other prevalent neurodevelopmental conditions.

Altered intersubject functional variability of brain white-matter in major depressive disorder and its association with gene expression profiles

Major depressive disorder (MDD) is a clinically heterogeneous disorder. Its mechanism is still unknown. Although the altered intersubject variability in functional connectivity (IVFC) within gray-matter has been reported in MDD, the alterations to IVFC within white-matter (WM-IVFC) remain unknown. Based on the resting-state functional MRI data of discovery (145 MDD patients and 119 healthy controls [HCs]) and validation cohorts (54 MDD patients, and 78 HCs), we compared the WM-IVFC between the two groups. We further assessed the meta-analytic cognitive functions related to the alterations. The discriminant WM-IVFC values were used to classify MDD patients and predict clinical symptoms in patients. In combination with the Allen Human Brain Atlas, transcriptome-neuroimaging association analyses were further conducted to investigate gene expression profiles associated with WM-IVFC alterations in MDD, followed by a set of gene functional characteristic analyses. We found extensive WM-IVFC alterations in MDD compared to HCs, which were associated with multiple behavioral domains, including sensorimotor processes and higher-order functions. The discriminant WM-IVFC could not only effectively distinguish MDD patients from HCs with an area under curve ranging from 0.889 to 0.901 across three classifiers, but significantly predict depression severity (r = 0.575, p = 0.002) and suicide risk (r = 0.384, p = 0.040) in patients. Furthermore, the variability-related genes were enriched for synapse, neuronal system, and ion channel, and predominantly expressed in excitatory and inhibitory neurons. Our results obtained good reproducibility in the validation cohort. These findings revealed intersubject functional variability changes of brain WM in MDD and its linkage with gene expression profiles, providing potential implications for understanding the high clinical heterogeneity of MDD.

Multiplexity of human brain oscillations as a personal brain signature

Human individuality is likely underpinned by the constitution of functional brain networks that ensure consistency of each person's cognitive and behavioral profile. These functional networks should, in principle, be detectable by noninvasive neurophysiology. We use a method that enables the detection of dominant frequencies of the interaction between every pair of brain areas at every temporal segment of the recording period, the dominant coupling modes (DoCM). We apply this method to brain oscillations, measured with magnetoencephalography (MEG) at rest in two independent datasets, and show that the spatiotemporal evolution of DoCMs constitutes an individualized brain fingerprint. Based on this successful fingerprinting we suggest that DoCMs are important targets for the investigation of neural correlates of individual psychological parameters and can provide mechanistic insight into the underlying neurophysiological processes, as well as their disturbance in brain diseases.

Functional connectome fingerprinting across the lifespan

Systematic changes have been observed in the functional architecture of the human brain with advancing age. However, functional connectivity (FC) is also a powerful feature to detect unique "connectome fingerprints," allowing identification of individuals among their peers. Although fingerprinting has been robustly observed in samples of young adults, the reliability of this approach has not been demonstrated across the lifespan. We applied the fingerprinting framework to the Cambridge Centre for Ageing and Neuroscience cohort (n = 483 aged 18 to 89 years). We found that individuals are "fingerprintable" (i.e., identifiable) across independent functional MRI scans throughout the lifespan. We observed a U-shape distribution in the strength of "self-identifiability" (within-individual correlation across modalities), and "others-identifiability" (between-individual correlation across modalities), with a decrease from early adulthood into middle age, before improving in older age. FC edges contributing to self-identifiability were not restricted to specific brain networks and were different between individuals across the lifespan sample. Self-identifiability was additionally associated with regional brain volume. These findings indicate that individual participant-level identification is preserved across the lifespan despite the fact that its components are changing nonlinearly.

Generalizing prediction of task-evoked brain activity across datasets and populations

Predictions of task-based functional magnetic resonance imaging (fMRI) from task-free resting-state (rs) fMRI have gained popularity over the past decade. This method holds a great promise for studying individual variability in brain function without the need to perform highly demanding tasks. However, in order to be broadly used, prediction models must prove to generalize beyond the dataset they were trained on. In this work, we test the generalizability of prediction of task-fMRI from rs-fMRI across sites, MRI vendors and age-groups. Moreover, we investigate the data requirements for successful prediction. We use the Human Connectome Project (HCP) dataset to explore how different combinations of training sample sizes and number of fMRI datapoints affect prediction success in various cognitive tasks. We then apply models trained on HCP data to predict brain activations in data from a different site, a different MRI vendor (Phillips vs. Siemens scanners) and a different age group (children from the HCP-development project). We demonstrate that, depending on the task, a training set of approximately 20 participants with 100 fMRI timepoints each yields the largest gain in model performance. Nevertheless, further increasing sample size and number of timepoints results in significantly improved predictions, until reaching approximately 450-600 training participants and 800-1000 timepoints. Overall, the number of fMRI timepoints influences prediction success more than the sample size. We further show that models trained on adequate amounts of data successfully generalize across sites, vendors and age groups and provide predictions that are both accurate and individual-specific. These findings suggest that large-scale publicly available datasets may be utilized to study brain function in smaller, unique samples.

Tensorial independent component analysis reveals social and reward networks associated with major depressive disorder

Major depressive disorder (MDD) has been associated with changes in functional brain connectivity. Yet, typical analyses of functional connectivity, such as spatial independent components analysis (ICA) for resting-state data, often ignore sources of between-subject variability, which may be crucial for identifying functional connectivity patterns associated with MDD. Typically, methods like spatial ICA will identify a single component to represent a network like the default mode network (DMN), even if groups within the data show differential DMN coactivation. To address this gap, this project applies a tensorial extension of ICA (tensorial ICA)-which explicitly incorporates between-subject variability-to identify functionally connected networks using functional MRI data from the Human Connectome Project (HCP). Data from the HCP included individuals with a diagnosis of MDD, a family history of MDD, and healthy controls performing a gambling and social cognition task. Based on evidence associating MDD with blunted neural activation to rewards and social stimuli, we predicted that tensorial ICA would identify networks associated with reduced spatiotemporal coherence and blunted social and reward-based network activity in MDD. Across both tasks, tensorial ICA identified three networks showing decreased coherence in MDD. All three networks included ventromedial prefrontal cortex, striatum, and cerebellum and showed different activation across the conditions of their respective tasks. However, MDD was only associated with differences in task-based activation in one network from the social task. Additionally, these results suggest that tensorial ICA could be a valuable tool for understanding clinical differences in relation to network activation and connectivity.

Uncovering individual differences in fine-scale dynamics of functional connectivity

Functional connectivity (FC) profiles contain subject-specific features that are conserved across time and have potential to capture brain-behavior relationships. Most prior work has focused on spatial features (nodes and systems) of these FC fingerprints, computed over entire imaging sessions. We propose a method for temporally filtering FC, which allows selecting specific moments in time while also maintaining the spatial pattern of node-based activity. To this end, we leverage a recently proposed decomposition of FC into edge time series (eTS). We systematically analyze functional magnetic resonance imaging frames to define features that enhance identifiability across multiple fingerprinting metrics, similarity metrics, and data sets. Results show that these metrics characteristically vary with eTS cofluctuation amplitude, similarity of frames within a run, transition velocity, and expression of functional systems. We further show that data-driven optimization of features that maximize fingerprinting metrics isolates multiple spatial patterns of system expression at specific moments in time. Selecting just 10% of the data can yield stronger fingerprints than are obtained from the full data set. Our findings support the idea that FC fingerprints are differentially expressed across time and suggest that multiple distinct fingerprints can be identified when spatial and temporal characteristics are considered simultaneously.

Evaluating the treatment outcomes of repetitive transcranial magnetic stimulation in patients with moderate-to-severe Alzheimer's disease

The repetitive transcranial magnetic stimulation (rTMS) shows great potential in the treatment of Alzheimer's disease (AD). However, its treatment efficacy for AD patients in moderate to severe stage is relatively evaluated. Here, we proposed a randomized, sham-controlled, clinical trial of rTMS among 35 moderate-to-severe AD patients. A high frequency (10 Hz) stimulation of the left dorsal lateral prefrontal cortex (DLPFC), 60-session long treatment lasting for 3 months procedure was adopted in the trial. Each participant completed a battery of neuropsychological tests at baseline and post-treatment for evaluation of the rTMS therapeutic effect. Twelve of them completed baseline resting-state functional magnetic resonance imaging (fMRI) for exploration of the underlying neural contribution to individual difference in treatment outcomes. The result showed that the rTMS treatment significantly improved cognitive performance on the severe impairment battery (SIB), reduced psychiatric symptoms on the neuropsychiatric inventory (NPI), and improved the clinician's global impression of change (CIBIC-Plus). Furthermore, the result preliminarily proposed resting-state multivariate functional connectivity in the (para) hippocampal region as well as two clusters in the frontal and occipital cortices as a pre-treatment neuroimaging marker for predicting individual differences in treatment outcomes. The finding could brought some enlightenment and reference for the rTMS treatment of moderate and severe AD patients.

Cell-type specific profiling of histone post-translational modifications in the adult mouse striatum

Epigenetic gene regulation in the heterogeneous brain remains challenging to decipher with current strategies. Bulk tissue analysis from pooled subjects reflects the average of cell-type specific changes across cell-types and individuals, which obscures causal relationships between epigenetic modifications, regulation of gene expression, and complex pathology. To address these limitations, we optimized a hybrid protocol, ICuRuS, for the isolation of nuclei tagged in specific cell-types and histone post translational modification profiling from the striatum of a single mouse. We combined affinity-based isolation of the medium spiny neuron subtypes, Adenosine 2a Receptor or Dopamine Receptor D1, with cleavage of histone-DNA complexes using an antibody-targeted micrococcal nuclease to release DNA complexes for paired end sequencing. Unlike fluorescence activated cell sorting paired with chromatin immunoprecipitation, ICuRuS allowed for robust epigenetic profiling at cell-type specific resolution. Our analysis provides a framework to understand combinatorial relationships between neuronal-subtype-specific epigenetic modifications and gene expression.

Predicting age from resting-state scalp EEG signals with deep convolutional neural networks on TD-brain dataset

Introduction

Brain age prediction has been shown to be clinically relevant, with errors in its prediction associated with various psychiatric and neurological conditions. While the prediction from structural and functional magnetic resonance imaging data has been feasible with high accuracy, whether the same results can be achieved with electroencephalography is unclear.

Methods

The current study aimed to create a new deep learning solution for brain age prediction using raw resting-state scalp EEG. To this end, we utilized the TD-BRAIN dataset, including 1,274 subjects (both healthy controls and individuals with various psychiatric disorders, with a total of 1,335 recording sessions). To achieve the best age prediction, we used data augmentation techniques to increase the diversity of the training set and developed a deep convolutional neural network model.

Results

The model's training was done with 10-fold cross-subject cross-validation, with the EEG recordings of the subjects used for training not considered to test the model. In training, using the relative rather than the absolute loss function led to a better mean absolute error of 5.96 years in cross-validation. We found that the best performance could be achieved when both eyes-open and eyes-closed states are used simultaneously. The frontocentral electrodes played the most important role in age prediction.

Discussion

The architecture and training method of the proposed deep convolutional neural networks (DCNN) improve state-of-the-art metrics in the age prediction task using raw resting-state EEG data by 13%. Given that brain age prediction might be a potential biomarker of numerous brain diseases, inexpensive and precise EEG-based estimation of brain age will be in demand for clinical practice.

Person-specific and precision neuroimaging: Current methods and future directions

Most neuroimaging studies of brain function analyze data in normalized space to identify regions of common activation across participants. These studies treat interindividual differences in brain organization as noise, but this approach can obscure important information about the brain's functional architecture. Recently, a number of studies have adopted a person-specific approach that aims to characterize these individual differences and explore their reliability and implications for behavior. A subset of these studies has taken a precision imaging approach that collects multiple hours of data from each participant to map brain function on a finer scale. In this review, we provide a broad overview of how person-specific and precision imaging techniques have used resting-state measures to examine individual differences in the brain's organization and their impact on behavior, followed by how task-based activity continues to add detail to these discoveries. We argue that person-specific and precision approaches demonstrate substantial promise in uncovering new details of the brain's functional organization and its relationship to behavior in many areas of cognitive neuroscience. We also discuss some current limitations in this new field and some new directions it may take.

Gazing the dusty mirror: Joint effect of narcissism and sadism on workplace incivility via indirect effect of paranoia, antagonism, and emotional intelligence

Workplace productivity is badly affected by many negative factors such as narcissism, and sadism. In addition, paranoia and antagonism play an important role in increasing workplace incivility. Through emotional intelligence, such negative behaviors could be addressed by managers and their junior colleagues. The current study aims to investigate the parallel mediating role of paranoia, antagonism, and emotional intelligence on the relationship between narcissism, sadism, and workplace incivility. A survey approach was used. Primary data was collected in PLS-SEM. The population of the study was all faculty members in higher education institutions in the Khyber Pakhtunkhwa (Pakistan) region. A measurement model and structural model were developed. The measurement model demonstrated that convergent and discriminant validities were established. The structural model's findings revealed that narcissism, antagonism, and emotional intelligence were not mediated between narcissism and workplace incivility. Similarly, emotional intelligence did not play any mediating role between sadism and workplace incivility. This implied that emotional intelligence has no role in decreasing or reducing workplace uncivil behavior.

Spatiotemporal Dynamics of Stress-Induced Network Reconfigurations Reflect Negative Affectivity

BACKGROUND

Maladaptive stress responses are important risk factors in the etiology of mood and anxiety disorders, but exact pathomechanisms remain to be understood. Mapping individual differences of acute stress-induced neurophysiological changes, especially on the level of neural activation and functional connectivity (FC), could provide important insights in how variation in the individual stress response is linked to disease risk.

METHODS

Using an established psychosocial stress task flanked by two resting states, we measured subjective, physiological, and brain responses to acute stress and recovery in 217 participants with and without mood and anxiety disorders. To estimate blockwise changes in stress-induced activation and FC, we used hierarchical mixed-effects models based on denoised time series within predefined stress-related regions. We predicted inter- and intraindividual differences in stress phases (anticipation vs. stress vs. recovery) and transdiagnostic dimensions of stress reactivity using elastic net and support vector machines.

RESULTS

We identified four subnetworks showing distinct changes in FC over time. FC but not activation trajectories predicted the stress phase (accuracy = 70%, p_perm < .001) and increases in heart rate (R² = 0.075, p_perm < .001). Critically, individual spatiotemporal trajectories of changes across networks also predicted negative affectivity (ΔR² = 0.075, p_perm = .030) but not the presence or absence of a mood and anxiety disorder.

CONCLUSIONS

Spatiotemporal dynamics of brain network reconfiguration induced by stress reflect individual differences in the psychopathology dimension of negative affectivity. These results support the idea that vulnerability for mood and anxiety disorders can be conceptualized best at the level of network dynamics, which may pave the way for improved prediction of individual risk.

Functional connectivity in the central executive network predicts changes in binge drinking behavior during emerging adulthood: an observational prospective study

BACKGROUND AND AIMS

Binge drinking contributes to the immense public health burden associated with alcohol use, especially among younger drinkers. Little is known about the underlying neurobiology of changes in this behavior over time. This preliminary study aimed to identify neurobiological markers of binge drinking behavior change during emerging adulthood.

DESIGN

Observational prospective investigation of neurobiological predictors of binge drinking behavior.

SETTING

Communities surrounding a large, public university in the northeastern United States.

PARTICIPANTS

A total of 42 emerging adults (48% female), approximately half meeting criteria for an alcohol use disorder.

MEASUREMENTS

Past month binge drinking, the dependent variable, was assessed at two time-points (T1, T2) via self-report. Ten indices of resting-state functional connectivity within the central executive network (CEN), a brain network involved in executive function, were collected at T1 and specified as independent variables in cross-sectional and prospective Poisson models. All models controlled for age, sex, and alcohol use disorder status.

FINDINGS

The cross-sectional model yielded five significant associations between CEN connectivity and binge drinking incidence. Connections anchored primarily in the anterior CEN exhibited negative associations with binge drinking incidence (P = 0.001, 0.004, 0.011), and connections stemming from the right posterior parietal cortex exhibited positive associations with binge drinking incidence (P = 0.041, 0.045). In prospective models, stronger frontoparietal connectivity between the right dorsolateral prefrontal cortex and left posterior parietal cortex predicted greater increases in binge drinking incidence over time (P = 0.003).

CONCLUSIONS

There is an association between central executive network connectivity and heavy drinking, as well as evidence that functional pathways within the central executive network may contribute to changes in problematic drinking behaviors.

The normative modeling framework for computational psychiatry

Normative modeling is an emerging and innovative framework for mapping individual differences at the level of a single subject or observation in relation to a reference model. It involves charting centiles of variation across a population in terms of mappings between biology and behavior, which can then be used to make statistical inferences at the level of the individual. The fields of computational psychiatry and clinical neuroscience have been slow to transition away from patient versus 'healthy' control analytic approaches, probably owing to a lack of tools designed to properly model biological heterogeneity of mental disorders. Normative modeling provides a solution to address this issue and moves analysis away from case-control comparisons that rely on potentially noisy clinical labels. Here we define a standardized protocol to guide users through, from start to finish, normative modeling analysis using the Predictive Clinical Neuroscience toolkit (PCNtoolkit). We describe the input data selection process, provide intuition behind the various modeling choices and conclude by demonstrating several examples of downstream analyses that the normative model may facilitate, such as stratification of high-risk individuals, subtyping and behavioral predictive modeling. The protocol takes ~1-3 h to complete.

Effect of Modafinil on functional connectivity in healthy young people using resting-state fMRI data

BACKGROUND

Examining the differences in the Functional Connectivity (FC) network while using Functional Magnetic Resonance Imaging (fMRI) between two groups can expand the understanding of neural processes and help diagnose and prevent neurological progression disorders. The present study evaluated the Modafinil effect on the FC of brain Regions of Interest (ROI) among healthy young individuals between the Modafinil and placebo groups.

METHOD

The data used in this study were downloaded from the open fMRI site and analyzed after preprocessing. Data included brain scan images of 26 healthy young men with no history of neurological disorders. These people are divided into two groups of drugs and a placebo. The drug group was given 100 mg of Modafinil, and the placebo group was assigned the same dose. Data were analyzed using a longitudinal variance component model.

RESULT

After taking the drug and placebo by the two groups, the study of the difference between FC in the drug and placebo group and the baseline effect showed a statistically significant difference in one pair of ROIs. Also, in examining the difference between FC in the drug and placebo groups of the longitudinal trend, there was a statistically significant difference between 5 pairs of ROIs.

CONCLUSION

After taking Modafinil and placebo, it was observed that FC in most areas in the drug group increased compared to the placebo group, indicating Modafinil has cognitive enhancement properties and has a role in visual, auditory, memory learning, and self-awareness functions and enhances these functions.

Functional Significance of Individual Differences in P3 Network Spatial Configuration

Abstract. The amplitude and latency of the P3 component in the electroencephalogram (EEG) event-related potentials (ERPs) are among the most extensively used markers for individual differences in normal and abnormal brain functions. In contrast, individual variations in spatial topography of the temporally-defined P3 component are relatively under-explored. Development in EEG-based source imaging opened up the possibility that individual-specific spatial configuration of the neural network underlying the temporally-defined P3 component bear a novel source of information for marking an individual difference in behavioral and cognitive function. In testing this hypothesis, a hybrid method consisting of blind source separation (BSS), equivalent current dipole (ECD) modeling, and hits-vector-based analysis was applied to continuous un-epoched EEG data collected from 13 healthy human participants performing a visual color oddball task. By analyzing the spatial configuration of the network underlying the temporally-defined P3 component, hereafter referred to as the P3N, we discovered that the contribution of each constituent structure within the P3N is not uniform. Instead, frontal lobe structures have significantly more involvement than other constituent structures, as quantitatively characterized by cross-individual reliability and a within-individual contribution to the P3N. A factor analysis of the hits vector data revealed that although P3 latency and amplitude did not show significant correlations with measures of the behavioral outcomes, scores of two factors derived from the hits vectors selectively predict behavioral reaction time and response correctness. These results support the hypothesis that variations in P3 spatial configuration reflect not merely noise but individual-specific features with functional significance.

Predicting Individual Task Contrasts From Resting-state Functional Connectivity using a Surface-based Convolutional Network

Task-based and resting-state represent the two most common experimental paradigms of functional neuroimaging. While resting-state offers a flexible and scalable approach for characterizing brain function, task-based techniques provide superior localization. In this paper, we build on recent deep learning methods to create a model that predicts task-based contrast maps from resting-state fMRI scans. Specifically, we propose BrainSurfCNN, a surface-based fully-convolutional neural network model that works with a representation of the brain's cortical sheet. BrainSurfCNN achieves exceptional predictive accuracy on independent test data from the Human Connectome Project, which is on par with the repeat reliability of the measured subject-level contrast maps. Conversely, our analyses reveal that a previously published benchmark is no better than group-average contrast maps. Finally, we demonstrate that BrainSurfCNN can generalize remarkably well to novel domains with limited training data.

Gene expression associated with individual variability in intrinsic functional connectivity

It has been revealed that intersubject variability (ISV) in intrinsic functional connectivity (FC) is associated with a wide variety of cognitive and behavioral performances. However, the underlying organizational principle of ISV in FC and its related gene transcriptional profiles remain unclear. Using resting-state fMRI data from the Human Connectome Project (299 adult participants) and microarray gene expression data from the Allen Human Brain Atlas, we conducted a transcription-neuroimaging association study to investigate the spatial configurations of ISV in intrinsic FC and their associations with spatial gene transcriptional profiles. We found that the multimodal association cortices showed the greatest ISV in FC, while the unimodal cortices and subcortical areas showed the least ISV. Importantly, partial least squares regression analysis revealed that the transcriptional profiles of genes associated with human accelerated regions (HARs) could explain 31.29% of the variation in the spatial distribution of ISV in FC. The top-related genes in the transcriptional profiles were enriched for the development of the central nervous system, neurogenesis and the cellular components of synapse. Moreover, we observed that the effect of gene expression profile on the heterogeneous distribution of ISV in FC was significantly mediated by the cerebral blood flow configuration. These findings highlighted the spatial arrangement of ISV in FC and their coupling with variations in transcriptional profiles and cerebral blood flow supply.

Individual Discriminative Ability of Resting Functional Brain Connectivity is Susceptible to the Time Span of MRI Scans

Recent studies have suggested that resting-state brain functional connectivity (RSFC) has the potential to discriminate among individuals in a population. These studies mostly utilized a pattern of RSFC obtained from one scan to identify a given individual from the set of patterns obtained from the second scan. However, it remains unclear whether the discriminative ability would change with the extension of the time span between the two brain scans. This study explores the variations in the discriminative ability of RSFC on eight time spans, including 6 hours, 12 hours, 1 day, 1 month, 3-6 months, 7-12 months, 1-2 years and 2-3 years. We first searched for a set of the most discriminative RSFCs using the data of 200 healthy adult subjects from the Human Connectome Project dataset, and we then utilized this set of RSFCs to identify individuals from a population. The variations in the discriminative accuracies over different time spans were evaluated on datasets from a total of 682 unseen adult subjects acquired from four different sites. We found that although the accuracies were detectable at above-chance levels, the discriminative accuracies showed a significant decrease (F = 17.87, p < 0.01) along with the extension of brain imaging time span, from over 90% within one month to 66% at 2-3 years. Furthermore, the decreasing trend was robust and not dependent on the training set or analysis method. Therefore, we suggest that the discriminative ability of RSFC in identifying individuals should be susceptible to the length of time between brain scans.

Hierarchical modelling of functional brain networks in population and individuals from big fMRI data

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We introduce stochastic PROFUMO (sPROFUMO) for inferring functional brain networks from big data.

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sPROFUMO hierarchically estimates fMRI networks for the population and every individual.

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We characterised high dimensional resting state fMRI networks from UK Biobank.

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Model outperforms ICA and dual regression for estimation of individual-specific network topography.

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We demonstrate the model's utility for predicting cognitive traits, and capturing subject variability in network topographies versus connectivity.

A major goal of large-scale brain imaging datasets is to provide resources for investigating heterogeneous populations. Characterisation of functional brain networks for individual subjects from these datasets will have an enormous potential for prediction of cognitive or clinical traits. We propose for the first time a technique, Stochastic Probabilistic Functional Modes (sPROFUMO), that is scalable to UK Biobank (UKB) with expected 100,000 participants, and hierarchically estimates functional brain networks in individuals and the population, while allowing for bidirectional flow of information between the two. Using simulations, we show the model's utility, especially in scenarios that involve significant cross-subject variability, or require delineation of fine-grained differences between the networks. Subsequently, by applying the model to resting-state fMRI from 4999 UKB subjects, we mapped resting state networks (RSNs) in single subjects with greater detail than has been possible previously in UKB (>100 RSNs), and demonstrate that these RSNs can predict a range of sensorimotor and higher-level cognitive functions. Furthermore, we demonstrate several advantages of the model over independent component analysis combined with dual-regression (ICA-DR), particularly with respect to the estimation of the spatial configuration of the RSNs and the predictive power for cognitive traits. The proposed model and results can open a new door for future investigations into individualised profiles of brain function from big data.

The Normative Modeling Framework for Computational Psychiatry.. [PMID: 35650452 PMCID: PMC7613648 DOI: 10.1101/2021.08.08.455583]

Normative modeling is an emerging and innovative framework for mapping individual differences at the level of a single subject or observation in relation to a reference model. It involves charting centiles of variation across a population in terms of mappings between biology and behavior which can then be used to make statistical inferences at the level of the individual. The fields of computational psychiatry and clinical neuroscience have been slow to transition away from patient versus “healthy” control analytic approaches, likely due to a lack of tools designed to properly model biological heterogeneity of mental disorders. Normative modeling provides a solution to address this issue and moves analysis away from case-control comparisons that rely on potentially noisy clinical labels. In this article, we define a standardized protocol to guide users through, from start to finish, normative modeling analysis using the Predictive Clinical Neuroscience toolkit (PCNtoolkit). We describe the input data selection process, provide intuition behind the various modeling choices, and conclude by demonstrating several examples of down-stream analyses the normative model results may facilitate, such as stratification of high-risk individuals, subtyping, and behavioral predictive modeling. The protocol takes approximately 1-3 hours to complete.

Individual Uniqueness in the Neonatal Functional Connectome

The functional connectome is highly distinctive in adults and adolescents, underlying individual differences in cognition and behavior. However, it remains unknown whether the individual uniqueness of the functional connectome is present in neonates, who are far from mature. Here, we utilized the multiband resting-state functional magnetic resonance imaging data of 40 healthy neonates from the Developing Human Connectome Project and a split-half analysis approach to characterize the uniqueness of the functional connectome in the neonatal brain. Through functional connectome-based individual identification analysis, we found that all the neonates were correctly identified, with the most discriminative regions predominantly confined to the higher-order cortices (e.g., prefrontal and parietal regions). The connectivities with the highest contributions to individual uniqueness were primarily located between different functional systems, and the short- (0-30 mm) and middle-range (30-60 mm) connectivities were more distinctive than the long-range (>60 mm) connectivities. Interestingly, we found that functional data with a scanning length longer than 3.5 min were able to capture the individual uniqueness in the functional connectome. Our results highlight that individual uniqueness is present in the functional connectome of neonates and provide insights into the brain mechanisms underlying individual differences in cognition and behavior later in life.

Beyond fingerprinting: Choosing predictive connectomes over reliable connectomes

Recent years have seen a surge of research on variability in functional brain connectivity within and between individuals, with encouraging progress toward understanding the consequences of this variability for cognition and behavior. At the same time, well-founded concerns over rigor and reproducibility in psychology and neuroscience have led many to question whether functional connectivity is sufficiently reliable, and call for methods to improve its reliability. The thesis of this opinion piece is that when studying variability in functional connectivity-both across individuals and within individuals over time-we should use behavior prediction as our benchmark rather than optimize reliability for its own sake. We discuss theoretical and empirical evidence to compel this perspective, both when the goal is to study stable, trait-level differences between people, as well as when the goal is to study state-related changes within individuals. We hope that this piece will be useful to the neuroimaging community as we continue efforts to characterize inter- and intra-subject variability in brain function and build predictive models with an eye toward eventual real-world applications.

Predicting individual variability in task-evoked brain activity in schizophrenia

What goes wrong in a schizophrenia patient's brain that makes it so different from a healthy brain? In this study, we tested the hypothesis that the abnormal brain activity in schizophrenia is tightly related to alterations in brain connectivity. Using functional magnetic resonance imaging (fMRI), we demonstrated that both resting‐state functional connectivity and brain activity during the well‐validated N‐back task differed significantly between schizophrenia patients and healthy controls. Nevertheless, using a machine‐learning approach we were able to use resting‐state functional connectivity measures extracted from healthy controls to accurately predict individual variability in the task‐evoked brain activation in the schizophrenia patients. The predictions were highly accurate, sensitive, and specific, offering novel insights regarding the strong coupling between brain connectivity and activity in schizophrenia. On a practical perspective, these findings may allow to generate task activity maps for clinical populations without the need to actually perform any tasks, thereby reducing patients inconvenience while saving time and money.

Developing functional network connectivity of the dorsal anterior cingulate cortex mediates externalizing psychopathology in adolescents with child neglect

Childhood adversity has been associated with elevated risk for psychopathology. We investigated whether development of functional brain networks important for executive function (EF) could serve as potential mediators of this association. We analyzed data of 475 adolescents, a subsample of the multisite longitudinal NCANDA (National Consortium on Alcohol and Neurodevelopment in Adolescence) cohort with completed measures of childhood trauma, resting-state functional brain connectivity data, and symptoms of internalizing and externalizing psychopathology at baseline and follow-up years 1–4. Using parallel process latent growth models, we found that childhood adversity was associated with increased risk for externalizing/internalizing behaviors. We specifically investigated whether functional connectivity of the dorsal anterior cingulate cortex (dACC) to brain regions within the cingulo-opercular (CO) network, a well-known EF network that underlies control of attention and self-regulation, mediates the association between adversity and symptoms of psychopathology. We found that childhood adversity, specifically child neglect was negatively associated with functional connectivity of the dACC within the CO network, and that this connectivity mediated the association between neglect and externalizing behaviors. Our study advances a mechanistic understanding of how childhood adversity may impact the development of psychopathology, highlighting the relevance of dACC functional networks particularly for externalizing psychopathology.

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.

Disrupted Intersubject Variability Architecture in Functional Connectomes in Schizophrenia

Schizophrenia (SCZ) is a highly heterogeneous disorder with remarkable intersubject variability in clinical presentations. Previous neuroimaging studies in SCZ have primarily focused on identifying group-averaged differences in the brain connectome between patients and healthy controls (HCs), largely neglecting the intersubject differences among patients. We acquired whole-brain resting-state functional MRI data from 121 SCZ patients and 183 HCs and examined the intersubject variability of the functional connectome (IVFC) in SCZ patients and HCs. Between-group differences were determined using permutation analysis. Then, we evaluated the relationship between IVFC and clinical variables in SCZ. Finally, we used datasets of patients with bipolar disorder (BD) and major depressive disorder (MDD) to assess the specificity of IVFC alteration in SCZ. The whole-brain IVFC pattern in the SCZ group was generally similar to that in HCs. Compared with the HC group, the SCZ group exhibited higher IVFC in the bilateral sensorimotor, visual, auditory, and subcortical regions. Moreover, altered IVFC was negatively correlated with age of onset, illness duration, and Brief Psychiatric Rating Scale scores and positively correlated with clinical heterogeneity. Although the SCZ shared altered IVFC in the visual cortex with BD and MDD, the alterations of IVFC in the sensorimotor, auditory, and subcortical cortices were specific to SCZ. The alterations of whole-brain IVFC in SCZ have potential implications for the understanding of the high clinical heterogeneity of SCZ and the future individualized clinical diagnosis and treatment of this disease.

Predictive modeling of neurobehavioral state and trait variation across development

A key goal of human neurodevelopmental research is to map neural and behavioral trajectories across both health and disease. A growing number of developmental consortia have begun to address this gap by providing open access to cross-sectional and longitudinal 'big data' repositories. However, it remains challenging to develop models that enable prediction of both within-subject and between-subject neurodevelopmental variation. Here, we present a conceptual and analytical perspective of two essential ingredients for mapping neurodevelopmental trajectories: state and trait components of variance. We focus on mapping variation across a range of neural and behavioral measurements and consider concurrent alterations of state and trait variation across development. We present a quantitative framework for combining both state- and trait-specific sources of neurobehavioral variation across development. Specifically, we argue that non-linear mixed growth models that leverage state and trait components of variance and consider environmental factors are necessary to comprehensively map brain-behavior relationships. We discuss this framework in the context of mapping language neurodevelopmental changes in early childhood, with an emphasis on measures of functional connectivity and their reliability for establishing robust neurobehavioral relationships. The ultimate goal is to statistically unravel developmental trajectories of neurobehavioral relationships that involve a combination of individual differences and age-related changes.

Idiosynchrony: From shared responses to individual differences during naturalistic neuroimaging

Two ongoing movements in human cognitive neuroscience have researchers shifting focus from group-level inferences to characterizing single subjects, and complementing tightly controlled tasks with rich, dynamic paradigms such as movies and stories. Yet relatively little work combines these two, perhaps because traditional analysis approaches for naturalistic imaging data are geared toward detecting shared responses rather than between-subject variability. Here, we review recent work using naturalistic stimuli to study individual differences, and advance a framework for detecting structure in idiosyncratic patterns of brain activity, or "idiosynchrony". Specifically, we outline the emerging technique of inter-subject representational similarity analysis (IS-RSA), including its theoretical motivation and an empirical demonstration of how it recovers brain-behavior relationships during movie watching using data from the Human Connectome Project. We also consider how stimulus choice may affect the individual signal and discuss areas for future research. We argue that naturalistic neuroimaging paradigms have the potential to reveal meaningful individual differences above and beyond those observed during traditional tasks or at rest.

Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis

With the wide adoption of functional magnetic resonance imaging (fMRI) by cognitive neuroscience researchers, large volumes of brain imaging data have been accumulated in recent years. Aggregating these data to derive scientific insights often faces the challenge that fMRI data are high-dimensional, heterogeneous across people, and noisy. These challenges demand the development of computational tools that are tailored both for the neuroscience questions and for the properties of the data. We review a few recently developed algorithms in various domains of fMRI research: fMRI in naturalistic tasks, analyzing full-brain functional connectivity, pattern classification, inferring representational similarity and modeling structured residuals. These algorithms all tackle the challenges in fMRI similarly: they start by making clear statements of assumptions about neural data and existing domain knowledge, incorporate those assumptions and domain knowledge into probabilistic graphical models, and use those models to estimate properties of interest or latent structures in the data. Such approaches can avoid erroneous findings, reduce the impact of noise, better utilize known properties of the data, and better aggregate data across groups of subjects. With these successful cases, we advocate wider adoption of explicit model construction in cognitive neuroscience. Although we focus on fMRI, the principle illustrated here is generally applicable to brain data of other modalities.

Individual structural features constrain the mouse functional connectome

Whole brain dynamics intuitively depend upon the internal wiring of the brain; but to which extent the individual structural connectome constrains the corresponding functional connectome is unknown, even though its importance is uncontested. After acquiring structural data from individual mice, we virtualized their brain networks and simulated in silico functional MRI data. Theoretical results were validated against empirical awake functional MRI data obtained from the same mice. We demonstrate that individual structural connectomes predict the functional organization of individual brains. Using a virtual mouse brain derived from the Allen Mouse Brain Connectivity Atlas, we further show that the dominant predictors of individual structure-function relations are the asymmetry and the weights of the structural links. Model predictions were validated experimentally using tracer injections, identifying which missing connections (not measurable with diffusion MRI) are important for whole brain dynamics in the mouse. Individual variations thus define a specific structural fingerprint with direct impact upon the functional organization of individual brains, a key feature for personalized medicine.

Multivariate consistency of resting-state fMRI connectivity maps acquired on a single individual over 2.5 years, 13 sites and 3 vendors

Studies using resting-state functional magnetic resonance imaging (rsfMRI) are increasingly collecting data at multiple sites in order to speed up recruitment or increase sample size. The main objective of this study was to assess the long-term consistency of rsfMRI connectivity maps derived at multiple sites and vendors using the Canadian Dementia Imaging Protocol (CDIP, www.cdip-pcid.ca). Nine to 10 min of functional BOLD images were acquired from an adult cognitively healthy volunteer scanned repeatedly at 13 Canadian sites on three scanner makes (General Electric, Philips and Siemens) over the course of 2.5 years. The consistency (spatial Pearson's correlation) of rsfMRI connectivity maps for seven canonical networks ranged from 0.3 to 0.8, with a negligible effect of time, but significant site and vendor effects. We noted systematic differences in data quality (i.e. head motion, number of useable time frames, temporal signal-to-noise ratio) across vendors, which may also confound some of these results, and could not be disentangled in this sample. We also pooled the long-term longitudinal data with a single-site, short-term (1 month) data sample acquired on 26 subjects (10 scans per subject), called HNU1. Using randomly selected pairs of scans from each subject, we quantified the ability of a data-driven unsupervised cluster analysis to match two scans of the same subjects. In this "fingerprinting" experiment, we found that scans from the Canadian subject (Csub) could be matched with high accuracy intra-site (>95% for some networks), but that the accuracy decreased substantially for scans drawn from different sites and vendors, even falling outside of the range of accuracies observed in HNU1. Overall, our results demonstrate good multivariate stability of rsfMRI measures over several years, but substantial impact of scanning site and vendors. How detrimental these effects are will depend on the application, yet our results demonstrate that new methods for harmonizing multisite analysis represent an important area for future work.

The individual functional connectome is unique and stable over months to years

Functional connectomes computed from fMRI provide a means to characterize individual differences in the patterns of BOLD synchronization across regions of the entire brain. Using four resting-state fMRI datasets with a wide range of ages, we show that individual differences of the functional connectome are stable across 3 months to 1-2 years (and even detectable at above-chance levels across 3 years). Medial frontal and frontoparietal networks appear to be both unique and stable, resulting in high ID rates, as did a combination of these two networks. We conduct analyses demonstrating that these results are not driven by head motion. We also show that edges contributing the most to a successful ID tend to connect nodes in the frontal and parietal cortices, while edges contributing the least tend to connect cross-hemispheric homologs. Our results demonstrate that the functional connectome is stable across years and that high ID rates are not an idiosyncratic aspect of a specific dataset, but rather reflect stable individual differences in the functional connectivity of the brain.

Dynamical brain connectivity estimation using GARCH models: An application to personality neuroscience

It has recently become evident that the functional connectome of the human brain is a dynamical entity whose time evolution carries important information underpinning physiological brain function as well as its disease-related aberrations. While simple sliding window approaches have had some success in estimating dynamical brain connectivity in a functional MRI (fMRI) context, these methods suffer from limitations related to the arbitrary choice of window length and limited time resolution. Recently, Generalized autoregressive conditional heteroscedastic (GARCH) models have been employed to generate dynamical covariance models which can be applied to fMRI. Here, we employ a GARCH-based method (dynamic conditional correlation - DCC) to estimate dynamical brain connectivity in the Human Connectome Project (HCP) dataset and study how the dynamic functional connectivity behaviors related to personality as described by the five-factor model. Openness, a trait related to curiosity and creativity, is the only trait associated with significant differences in the amount of time-variability (but not in absolute median connectivity) of several inter-network functional connections in the human brain. The DCC method offers a novel window to extract dynamical information which can aid in elucidating the neurophysiological underpinning of phenomena to which conventional static brain connectivity estimates are insensitive.

Multi-modal brain fingerprinting: A manifold approximation based framework

This work presents an efficient framework, based on manifold approximation, for generating brain fingerprints from multi-modal data. The proposed framework represents images as bags of local features which are used to build a subject proximity graph. Compact fingerprints are obtained by projecting this graph in a low-dimensional manifold using spectral embedding. Experiments using the T1/T2-weighted MRI, diffusion MRI, and resting-state fMRI data of 945 Human Connectome Project subjects demonstrate the benefit of combining multiple modalities, with multi-modal fingerprints more discriminative than those generated from individual modalities. Results also highlight the link between fingerprint similarity and genetic proximity, monozygotic twins having more similar fingerprints than dizygotic or non-twin siblings. This link is also reflected in the differences of feature correspondences between twin/sibling pairs, occurring in major brain structures and across hemispheres. The robustness of the proposed framework to factors like image alignment and scan resolution, as well as the reproducibility of results on retest scans, suggest the potential of multi-modal brain fingerprinting for characterizing individuals in a large cohort analysis.

Language network measures at rest indicate individual differences in naming decline after anterior temporal lobe resection

While anterior temporal lobe (ATL) resection is an effective treatment for temporal lobe epilepsy, surgery on the dominant hemisphere is associated with variable decline in confrontation naming. Accurate prediction of naming impairment is critical to inform clinical decision making, and while there has been some degree of success using task-based functional MRI (fMRI) paradigms, there remains a growing interest in the predictive utility of resting-state connectivity as it allows for relatively shorter scans with low task demands. Our objective was to assess the relationship between measures of preoperative resting-state connectivity and postoperative naming change in patients following left ATL resection. We compared the resting language network connectivity of each patient to a normative healthy control template using a novel measure called "matrix similarity," and found that patients with more abnormal global language-network connectivity-particularly of regions spared from surgery-showed greater postoperative naming decline than those with normative patterns of connectivity. When we interrogated the degree centrality of to-be-resected regions in a more targeted approach of the pathological temporal lobe, we found that greater functional integration of those regions with the rest of the language network at rest was related to greater decline in naming following surgery. Finally, we found that matrix similarity was a better predictor of postoperative outcome than degree within to-be-resected regions, network clustering, modularity, and language task fMRI laterality. We provide some of the first evidence that using this novel measure, a relatively short preoperative resting scan can be exploited to inform naming ability following ATL resection.

A longitudinal model for functional connectivity networks using resting-state fMRI

Many neuroimaging studies collect functional magnetic resonance imaging (fMRI) data in a longitudinal manner. However, the current fMRI literature lacks a general framework for analyzing functional connectivity (FC) networks in fMRI data obtained from a longitudinal study. In this work, we build a novel longitudinal FC model using a variance components approach. First, for all subjects' visits, we account for the autocorrelation inherent in the fMRI time series data using a non-parametric technique. Second, we use a generalized least squares approach to estimate 1) the within-subject variance component shared across the population, 2) the baseline FC strength, and 3) the FC's longitudinal trend. Our novel method for longitudinal FC networks seeks to account for the within-subject dependence across multiple visits, the variability due to the subjects being sampled from a population, and the autocorrelation present in fMRI time series data, while restricting the number of parameters in order to make the method computationally feasible and stable. We develop a permutation testing procedure to draw valid inference on group differences in the baseline FC network and change in FC over longitudinal time between a set of patients and a comparable set of controls. To examine performance, we run a series of simulations and apply the model to longitudinal fMRI data collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Overall, we found no difference in the global FC network between Alzheimer's disease patients and healthy controls, but did find differing local aging patterns in the FC between the left hippocampus and the posterior cingulate cortex.

Trait paranoia shapes inter-subject synchrony in brain activity during an ambiguous social narrative

Individuals often interpret the same event in different ways. How do personality traits modulate brain activity evoked by a complex stimulus? Here we report results from a naturalistic paradigm designed to draw out both neural and behavioral variation along a specific dimension of interest, namely paranoia. Participants listen to a narrative during functional MRI describing an ambiguous social scenario, written such that some individuals would find it highly suspicious, while others less so. Using inter-subject correlation analysis, we identify several brain areas that are differentially synchronized during listening between participants with high and low trait-level paranoia, including theory-of-mind regions. Follow-up analyses indicate that these regions are more active to mentalizing events in high-paranoia individuals. Analyzing participants’ speech as they freely recall the narrative reveals semantic and syntactic features that also scale with paranoia. Results indicate that a personality trait can act as an intrinsic “prime,” yielding different neural and behavioral responses to the same stimulus across individuals.

Reactions to the same event can vary vastly based on multiple factors. Here the authors show that people with high trait-level paranoia process ambiguous information in a narrative differently and this can be attributed to greater activity in mentalizing brain regions during the moments of ambiguity.

An exemplar-based approach to individualized parcellation reveals the need for sex specific functional networks

Recent work with functional connectivity data has led to significant progress in understanding the functional organization of the brain. While the majority of the literature has focused on group-level parcellation approaches, there is ample evidence that the brain varies in both structure and function across individuals. In this work, we introduce a parcellation technique that incorporates delineation of functional networks both at the individual- and group-level. The proposed technique deploys the notion of "submodularity" to jointly parcellate the cerebral cortex while establishing an inclusive correspondence between the individualized functional networks. Using this parcellation technique, we successfully established a cross-validated predictive model that predicts individuals' sex, solely based on the parcellation schemes (i.e. the node-to-network assignment vectors). The sex prediction finding illustrates that individualized parcellation of functional networks can reveal subgroups in a population and suggests that the use of a global network parcellation may overlook fundamental differences in network organization. This is a particularly important point to consider in studies comparing patients versus controls or even patient subgroups. Network organization may differ between individuals and global configurations should not be assumed. This approach to the individualized study of functional organization in the brain has many implications for both neuroscience and clinical applications.

Prediction complements explanation in understanding the developing brain

A central aim of human neuroscience is understanding the neurobiology of cognition and behavior. Although we have made significant progress towards this goal, reliance on group-level studies of the developed adult brain has limited our ability to explain population variability and developmental changes in neural circuitry and behavior. In this review, we suggest that predictive modeling, a method for predicting individual differences in behavior from brain features, can complement descriptive approaches and provide new ways to account for this variability. Highlighting the outsized scientific and clinical benefits of prediction in developmental populations including adolescence, we show that predictive brain-based models are already providing new insights on adolescent-specific risk-related behaviors. Together with large-scale developmental neuroimaging datasets and complementary analytic approaches, predictive modeling affords us the opportunity and obligation to identify novel treatment targets and individually tailor the course of interventions for developmental psychopathologies that impact so many young people today.

Topographic organization of the cerebral cortex and brain cartography

One of the most specific but also challenging properties of the brain is its topographic organization into distinct modules or cortical areas. In this paper, we first review the concept of topographic organization and its historical development. Next, we provide a critical discussion of the current definition of what constitutes a cortical area, why the concept has been so central to the field of neuroimaging and the challenges that arise from this view. A key aspect in this discussion is the issue of spatial scale and hierarchy in the brain. Focusing on in-vivo brain parcellation as a rapidly expanding field of research, we highlight potential limitations of the classical concept of cortical areas in the context of multi-modal parcellation and propose a revised interpretation of cortical areas building on the concept of neurobiological atoms that may be aggregated into larger units within and across modalities. We conclude by presenting an outlook on the implication of this revised concept for future mapping studies and raise some open questions in the context of brain parcellation.