Resting-State Functional Connectivity in the Human Connectome Project: Current Status and Relevance to Understanding Psychopathology

Macroscopic resting-state brain dynamics are best described by linear models

It is typically assumed that large networks of neurons exhibit a large repertoire of nonlinear behaviours. Here we challenge this assumption by leveraging mathematical models derived from measurements of local field potentials via intracranial electroencephalography and of whole-brain blood-oxygen-level-dependent brain activity via functional magnetic resonance imaging. We used state-of-the-art linear and nonlinear families of models to describe spontaneous resting-state activity of 700 participants in the Human Connectome Project and 122 participants in the Restoring Active Memory project. We found that linear autoregressive models provide the best fit across both data types and three performance metrics: predictive power, computational complexity and the extent of the residual dynamics unexplained by the model. To explain this observation, we show that microscopic nonlinear dynamics can be counteracted or masked by four factors associated with macroscopic dynamics: averaging over space and over time, which are inherent to aggregated macroscopic brain activity, and observation noise and limited data samples, which stem from technological limitations. We therefore argue that easier-to-interpret linear models can faithfully describe macroscopic brain dynamics during resting-state conditions.

The Complexity of Impulsivity Dimensions among Abstinent Individuals with Substance Use Disorders

Impulsivity is a complex construct that has been operationalized considering personality dimensions (e.g., negative urgency [NU], lack of perseverance [LPe], lack of premeditation [LPr], positive urgency [PU]), and neuropsychological processes (i.e., cognitive disinhibition, motor disinhibition, impulsive decision-making). Empirical research suggested that they could represent core features of substance use disorders (SUDs). However, there are no studies that have comprehensively assessed them among patients with SUDs. Furthermore, the quality of relationships among such domains remains unclear. The current case-control study included 59 abstinent patients with SUDs and 56 healthy controls (HCs). There were two independent assessment phases: i) the administration of UPPS-P impulsive behavior scale; ii) a computerized neuropsychological battery (i.e., Attentional Network Test, Go/No-Go task, Iowa Gambling task). Patients with SUDs reported higher levels of NU and PU than HCs. NU, LPe, and LPr were associated to the co-occurrence of multiple SUDs. Motor disinhibition was the core dimension of SUDs. Cognitive disinhibition and Impulsive decision-making were also associated to SUDs. Self-report and neuropsychological dimensions of impulsivity were not correlated within the clinical group. HCs showed significant associations among these domains of impulsivity. Impulsivity should be viewed as a complex system of personality traits and neuropsychological processes among individuals with SUDs.

Person-identifying brainprints are stably embedded in EEG mindprints

Electroencephalography (EEG) signals measured under fixed conditions have been exploited as biometric identifiers. However, what contributes to the uniqueness of one's brain signals remains unclear. In the present research, we conducted a multi-task and multi-week EEG study with ten pairs of monozygotic (MZ) twins to examine the nature and components of person-identifiable brain signals. Through machine-learning analyses, we uncovered a person-identifying EEG component that served as "base signals" shared across tasks and weeks. Such task invariance and temporal stability suggest that these person-identifying EEG characteristics are more of structural brainprints than functional mindprints. Moreover, while these base signals were more similar within than between MZ twins, it was still possible to distinguish twin siblings, particularly using EEG signals coming primarily from late rather than early developed areas in the brain. Besides theoretical clarifications, the discovery of the EEG base signals has practical implications for privacy protection and the application of brain-computer interfaces.

Differentiating Individuals with and without Alcohol Use Disorder Using Resting-State fMRI Functional Connectivity of Reward Network, Neuropsychological Performance, and Impulsivity Measures

Individuals with alcohol use disorder (AUD) may manifest an array of neural and behavioral abnormalities, including altered brain networks, impaired neurocognitive functioning, and heightened impulsivity. Using multidomain measures, the current study aimed to identify specific features that can differentiate individuals with AUD from healthy controls (CTL), utilizing a random forests (RF) classification model. Features included fMRI-based resting-state functional connectivity (rsFC) across the reward network, neuropsychological task performance, and behavioral impulsivity scores, collected from thirty abstinent adult males with prior history of AUD and thirty CTL individuals without a history of AUD. It was found that the RF model achieved a classification accuracy of 86.67% (AUC = 93%) and identified key features of FC and impulsivity that significantly contributed to classifying AUD from CTL individuals. Impulsivity scores were the topmost predictors, followed by twelve rsFC features involving seventeen key reward regions in the brain, such as the ventral tegmental area, nucleus accumbens, anterior insula, anterior cingulate cortex, and other cortical and subcortical structures. Individuals with AUD manifested significant differences in impulsivity and alterations in functional connectivity relative to controls. Specifically, AUD showed heightened impulsivity and hypoconnectivity in nine connections across 13 regions and hyperconnectivity in three connections involving six regions. Relative to controls, visuo-spatial short-term working memory was also found to be impaired in AUD. In conclusion, specific multidomain features of brain connectivity, impulsivity, and neuropsychological performance can be used in a machine learning framework to effectively classify AUD individuals from healthy controls.

Task effects on functional connectivity measures after stroke

Understanding the effect of task compared to rest on detecting stroke-related network abnormalities will inform efforts to optimize detection of such abnormalities. The goal of this work was to determine whether connectivity measures obtained during an overt task are more effective than connectivity obtained during a "resting" state for detecting stroke-related changes in network function of the brain. This study examined working memory, discrete pedaling, continuous pedaling and language tasks. Functional magnetic resonance imaging was used to examine regional and inter-regional brain network function in 14 stroke and 16 control participants. Independent component analysis was used to identify 149 regions of interest (ROI). Using the inter-regional connectivity measurements, the weighted sum was calculated across only regions associated with a given task. Both inter-regional connectivity and regional connectivity were greater during each of the tasks as compared to the resting state. The working memory and discrete pedaling tasks allowed for detection of stroke-related decreases in inter-regional connectivity, while the continuous pedaling and language tasks allowed for detection of stroke-related enhancements in regional connectivity. These observations illustrate that task-based functional connectivity allows for detection of stroke-related changes not seen during resting states. In addition, this work provides evidence that tasks emphasizing different cognitive domains reveal different aspects of stroke-related reorganization. We also illustrate that within the motor domain, different tasks can reveal inter-regional or regional stroke-related changes, in this case suggesting that discrete pedaling required more central drive than continuous pedaling.

Integrating Psychiatry and Medical Biotechnology as a Way to Achieve Scientific, Precision, and Personalized Psychiatry

Besides concerns about the increasing prevalence of psychiatric disorders and the significant burdens and costs, there are concerns about its validity. The dilemma of validity went so far that studies described the diagnoses in psychiatry as scientifically worthless. We suggest integrating psychiatry and medical biotechnology and using biotechnological products in psychiatric aspects help psychiatry become more precise, strengthen its position among other sciences, and increase its scientific credibility by giving examples. For this matter, we need different inputs to choose between the vast outputs. The most common inputs are clinical symptoms, cognitive function, individual and environmental risk factors, molecular markers, genetic markers, neuroimaging signs, and big data. Some molecular markers have been shown to have a relationship with psychiatric disorders such as Interleukin-6 (IL-6) and Tumor Necrosis Factor-α (TNF-α). Genetic studies might evolve the most accurate part of precision psychiatry. Currently, and through the developments in technology, genome-wide association studies have become available. In neuroimaging signs, psychiatric disorders are associated with generalized rather than focal brain network dysfunction, and functional magnetic resonance imaging could be performed to study them. It would exhibit different aberrancies in various psychiatric disorders. In big data, the constitution of predictive models and movement toward precision psychiatry can be led by using artificial intelligence and machine learning.

Resting State Cortico-Limbic Functional Connectivity and Dispositional Use of Emotion Regulation Strategies: A Replication and Extension Study

Neuroimaging functional connectivity analyses have shown that the negative coupling between the amygdala and cortical regions is linked to better emotion regulation (ER) in experimental task settings. However, less is known about the neural correlates of ER traits or dispositions. The present study aimed to: (1) replicate the findings of differential cortico-limbic coupling during resting-state depending on the dispositional use of emotion regulation strategies. Furthermore, the study aimed to: (2) extend prior findings by examining whether differences in cortico-limbic coupling during resting-state predict experiential and neuronal ER success in a standard ER task. To this end, N = 107 healthy adults completed the Emotion Regulation Questionnaire (ERQ), underwent an 8-min resting-state fMRI acquisition, and completed a reappraisal task during fMRI. Functional connectivity maps of basolateral and centromedial amygdala nuclei were estimated with a seed-based approach regarding associations with regions of the prefrontal cortex and were then correlated with ERQ scores as well as experiential and neuronal ER success. All hypotheses and the analysis plan are preregistered at https://osf.io/8wsgu. Opposed to prior findings, we were not able to replicate a correlation of dispositional ER strategy use with functional connectivity between the amygdala and PFC regions (p > 0.05, FWE-corrected). Furthermore, there was no association of experiential and neuronal reappraisal success with functional connectivity between amygdala and insula as well as PFC (p > 0.05, FWE-corrected). The present preregistered study calls into question the reported association between individual differences in resting-state cortico-limbic connectivity and dispositional use of ER strategies. However, ongoing advances in functional brain imaging and distributed network approaches may leverage the identification of reliable functional connectivity patterns that underlie successful emotion regulation.

Defining Individual-Specific Functional Neuroanatomy for Precision Psychiatry

Studies comparing diverse groups have shown that many psychiatric diseases involve disruptions across distributed large-scale networks of the brain. There is hope that functional magnetic resonance imaging (fMRI) functional connectivity techniques will shed light on these disruptions, providing prognostic and diagnostic biomarkers as well as targets for therapeutic interventions. However, to date, progress on clinical translation of fMRI methods has been limited. Here, we argue that this limited translation is driven by a combination of intersubject heterogeneity and the relatively low reliability of standard fMRI techniques at the individual level. We review a potential solution to these limitations: the use of new "precision" fMRI approaches that shift the focus of analysis from groups to single individuals through the use of extended data acquisition strategies. We begin by discussing the potential advantages of fMRI functional connectivity methods for improving our understanding of functional neuroanatomy and disruptions in psychiatric disorders. We then discuss the budding field of precision fMRI and findings garnered from this work. We demonstrate that precision fMRI can improve the reliability of functional connectivity measures, while showing high stability and sensitivity to individual differences. We close by discussing the application of these approaches to clinical settings.

Associations between personality and whole-brain functional connectivity at rest: Evidence across the adult lifespan

INTRODUCTION

Personality is associated with cognitive, emotional, and social functioning, and can play a role in age-related cognitive decline and dementia risk; however, little is known about the brain dynamics underlying personality characteristics, and whether they are moderated by age.

METHODS

We investigated the associations between personality and resting-state functional MRI data from 365 individuals across the adult lifespan (20-80 years). Participants completed the 50-item International Personality Item Pool and a resting-state imaging protocol on a 3T MRI scanner. Within-network connectivity values were computed based on predefined networks. Regression analyzes were conducted in order to investigate personality-connectivity associations, as well as moderation by age. All models controlled for potential confounders (such as age, sex, education, IQ, and the other personality traits).

RESULTS

We found that openness was positively associated with connectivity in the default-mode network, neuroticism was negatively associated with both the ventral and dorsal attention networks, and agreeableness was negatively associated with the dorsal attention network. In addition, age moderated the association between conscientiousness and the frontoparietal network, indicating that this association become stronger in older age.

CONCLUSIONS

Our findings demonstrate that personality is associated with brain connectivity, which may contribute to identifying personality profiles that play a role in protection against or risk for age-related brain changes and dementia.

Age-related change of auditory functional connectivity in Human Connectome Project data and tinnitus patients

BACKGROUND

We reported that tinnitus patients showed reduced levels of auditory functional connectivity (FC) in comparison with normal hearing control subjects, and that we succeeded in objective diagnosis of tinnitus with 86% sensitivity and 74% specificity by focusing only on auditory-related FC. However, the age-related change of auditory FC is not clarified. In this study, we examine age-related change of the auditory FC using the database of Human Connectome Project (HCP) and compared with our database of tinnitus patients.

METHOD

From the HCP database HCP Lifespan Pilot project, we studied five age groups, 8 to 9 years old, 14 to 15, 25 to 35, 45 to 55, and 65 to 75. We also applied our tinnitus patients' resting-state functional magnetic resonance imaging (fMRI) database, which is divided into three generations; 20 to 40 years old, 40 to 60, and 60 to 80 to compare with the HCP database. The resting state fMRI analyses were performed using the CONN toolbox version 18. As auditory-related regions, Heschl's gyrus, planum temporale, planum polare, operculum, insular cortex, and superior temporal gyrus were set as the regions of interest from our previous reports.

RESULT

Auditory FC is strongest among adolescents and reduces with age. But the auditory FC of tinnitus patients were significantly less than those of HCP data in each generation.

CONCLUSION

Although auditory FC decreases with age, tinnitus patients have less auditory FC compared with age-matched controls. The age-matched cutoff values are necessary for an objective diagnosis of tinnitus with resting state fMRI.

Lesion Studies in Contemporary Neuroscience

Studies of humans with focal brain damage and non-human animals with experimentally induced brain lesions have provided pivotal insights into the neural basis of behavior. As the repertoire of neural manipulation and recording techniques expands, the utility of studying permanent brain lesions bears re-examination. Studies on the effects of permanent lesions provide vital data about brain function that are distinct from those of reversible manipulations. Focusing on work carried out in humans and nonhuman primates, we address the inferential strengths and limitations of lesion studies, recent methodological developments, the integration of this approach with other methods, and the clinical and ecological relevance of this research. We argue that lesion studies are essential to the rigorous assessment of neuroscience theories.

Precision neuroradiology: mapping the nodes and networks that link genes to behaviour

What is the future of neuroradiology in the era of precision medicine? As with any big change, this transformation in medicine presents both challenges and opportunities, and to flourish in this new environment we will have to adapt. It is difficult to predict exactly how neuroradiology will evolve in this shifting landscape, but there will be changes in both what we image and what we do. In terms of imaging, we will need to move beyond simply imaging brain anatomy and toward imaging function, both at the molecular and circuit level. In terms of what we do, we will need to move from the periphery of the clinical enterprise toward its center, with a new emphasis on integrating imaging with genetic and clinical data to form a comprehensive picture of the patient that can be used to direct further testing and care.The payoff is that these changes will align neuroradiology with the emerging field of precision psychiatry, which promises to replace symptom-based diagnosis and trial-and-error treatment of psychiatric disorders with diagnoses based on quantifiable genetic, imaging, physiologic, and behavioural criteria and therapies targeted to the particular pathophysiology of individual patients. Here we review some of the recent developments in behavioural genetics and neuroscience that are laying the foundation for precision psychiatry. By no means comprehensive, our goal is to introduce some of the perspectives and techniques that are likely to be relevant to the precision neuroradiologist of the future.

Memory and the developing brain: From description to explanation with innovation in methods

Recent advances in human cognitive neuroscience show great promise in extending our understanding of the neural basis of memory development. We briefly review the current state of knowledge, highlighting that most work has focused on describing the neural correlates of memory in cross-sectional studies. We then delineate three examples of the application of innovative methods in addressing questions that go beyond description, towards a mechanistic understanding of memory development. First, structural brain imaging and the harmonization of measurements across laboratories may uncover ways in which the maturation of the brain constrains the development of specific aspects of memory. Second, longitudinal designs and sophisticated modeling of the data may identify age-driven changes and the factors that determine individual developmental trajectories. Third, recording memory-related activity directly from the developing brain presents an unprecedented opportunity to examine how distinct brain structures support memory in real time. Finally, the growing prevalence of data sharing offers additional means to tackle questions that demand large-scale datasets, ambitious designs, and access to rare samples. We propose that the use of such innovative methods will move our understanding of memory development from a focus on describing trends to explaining the causal factors that shape behavior.

Dynamic Computational Model of the Human Spinal Cord Connectome

Connectomes abound, but few for the human spinal cord. Using anatomical data in the literature, we constructed a draft connectivity map of the human spinal cord connectome, providing a template for the many calibrations of specialized behavior to be overlaid on it and the basis for an initial computational model. A thorough literature review gleaned cell types, connectivity, and connection strength indications. Where human data were not available, we selected species that have been studied. Cadaveric spinal cord measurements, cross-sectional histology images, and cytoarchitectural data regarding cell size and density served as the starting point for estimating numbers of neurons. Simulations were run using neural circuitry simulation software. The model contains the neural circuitry in all ten Rexed laminae with intralaminar, interlaminar, and intersegmental connections, as well as ascending and descending brain connections and estimated neuron counts for various cell types in every lamina of all 31 segments. We noted the presence of highly interconnected complex networks exhibiting several orders of recurrence. The model was used to perform a detailed study of spinal cord stimulation for analgesia. This model is a starting point for workers to develop and test hypotheses across an array of biomedical applications focused on the spinal cord. Each such model requires additional calibrations to constrain its output to verifiable predictions. Future work will include simulating additional segments and expanding the research uses of the model.