Functional connectomics in depression: insights into therapies

Depression is a common mental disorder characterized by heterogeneous cognitive and behavioral symptoms. The emerging research paradigm of functional connectomics has provided a quantitative theoretical framework and analytic tools for parsing variations in the organization and function of brain networks in depression. In this review, we first discuss recent progress in depression-associated functional connectome variations. We then discuss treatment-specific brain network outcomes in depression and propose a hypothetical model highlighting the advantages and uniqueness of each treatment in relation to the modulation of specific brain network connectivity and symptoms of depression. Finally, we look to the future promise of combining multiple treatment types in clinical practice, using multisite datasets and multimodal neuroimaging approaches, and identifying biological depression subtypes.     

Brain Networks in Schizophrenia

Schizophrenia—a severe psychiatric condition characterized by hallucinations, delusions, loss of initiative and cognitive function—is hypothesized to result from abnormal anatomical neural connectivity and a consequent decoupling of the brain’s integrative thought processes. The rise of in vivo neuroimaging techniques has refueled the formulation of dysconnectivity hypotheses, linking schizophrenia to abnormal structural and functional connectivity in the brain at both microscopic and macroscopic levels. Over the past few years, advances in high-field structural and functional neuroimaging techniques have made it increasingly feasible to reconstruct comprehensive maps of the macroscopic neural wiring system of the human brain, know as the connectome. In parallel, advances in network science and graph theory have improved our ability to study the spatial and topological organizational layout of such neural connectivity maps in detail. Combined, the field of neural connectomics has created a novel platform that provides a deeper understanding of the overall organization of brain wiring, its relation to healthy brain function and human cognition, and conversely, how brain disorders such as schizophrenia arise from abnormal brain network wiring and dynamics. In this review we discuss recent findings of connectomic studies in schizophrenia that examine how the disorder relates to disruptions of brain connectivity.     

The organization of thinking: What functional brain imaging reveals about the neuroarchitecture of complex cognition

Recent findings in brain imaging, particularly in fMRI, are beginning to reveal some of the fundamental properties of the organization of the cortical systems that underpin complex cognition. We propose an emerging set of operating principles that govern this organization, characterizing the system as a set of collaborating cortical centers that operate as a large-scale cortical network. Two of the network’s critical features are that it is resource constrained and dynamically configured, with resource constraints and demands dynamically shaping the network topology. The operating principles are embodied in a cognitive neuroarchitecture, 4CAPS, consisting of a number of interacting computational centers that correspond to activating cortical areas. Each 4CAPS center is a hybrid production system, possessing both symbolic and connectionist attributes. We describe 4CAPS models of sentence comprehension, spatial problem solving, and complex multitasking and compare the accounts of these models with brain activation and behavioral results. Finally, we compare 4CAPS with other proposed neuroarchitectures.     

The processing of verbs and nouns in neural networks: insights from synthetic brain imaging

The paper presents a computational model of language in which linguistic abilities evolve in organisms that interact with an environment. Each individual's behavior is controlled by a neural network and we study the consequences in the network's internal functional organization of learning to process different classes of words. Agents are selected for reproduction according to their ability to manipulate objects and to understand nouns (objects' names) and verbs (manipulation tasks). The weights of the agents' neural networks are evolved using a genetic algorithm. Synthetic brain imaging techniques are then used to examine the functional organization of the neural networks. Results show that nouns produce more integrated neural activity in the sensory-processing hidden layer, while verbs produce more integrated synaptic activity in the layer where sensory information is integrated with proprioceptive input. Such findings are qualitatively compared with human brain imaging data that indicate that nouns activate more the posterior areas of the brain related to sensory and associative processing, while verbs activate more the anterior motor areas.     

反刍思维的脑功能网络机制

反刍思维是指个体在经历了消极生活事件后不由自主地反复思考该事件的产生原因、经过和结果, 表现出负性自我参照加工、消极情绪性以及持续性的特点。采用脑功能网络分析方法, 研究者发现反刍思维的上述三个特点分别与默认网络内部的异常活动模式、突显网络功能连接的改变以及注意相关网络之间的异常耦合有关。未来研究应进一步明确反刍思维与相关脑网络活动之间的因果关系, 探究反刍思维脑功能网络的结构基础, 同时也应关注反刍思维及其脑网络的老龄化特征, 并致力于探索有效干预反刍思维的神经调控技术。     

The organization of construal networks and functional adaptation

This review examined the association of construal network organizations with functional adaptation and psychological well-being. Recent neuropsychological research supports the presence of distinct construal networks in the brain that organize action at different levels of goals and tasks. Construal networks are sets of connected construals, or mental representations of objects, events, and behaviors. Little attention, however, has been given to how the organization of construal networks promotes functional adaptation. Cognitive processes, construal levels, personal meaning, cultures, and situations influence the configurations of construal networks. The reviewed evidence indicated that construal network organization facilitates functional adaptation and well-being, either though the coherence or fit of the assembled construals with each other or through the mediation of their fit with situations or contexts, like a culture. This review goes beyond previous studies by describing the constructive, creative, and hypothetical aspects of construal organizations and their effects on functional adaptation and psychological well-being.     

失眠患者静息态脑网络的改变：网络内与网络间的功能连接异常

失眠已成为现代人群中的一种高发健康问题。静息态功能磁共振以其数据采集便利性和无创性, 成为失眠研究的主要成像手段之一。基于近年来静息态功能磁共振的发现, 失眠患者存在前额叶、颞叶、前扣带回、脑岛等认知-情绪神经环路的异常。大尺度脑网络是涵盖多个脑区、功能相对单一的大脑结构。失眠患者存在默认网络、突显网络、认知控制网络和负性情绪网络内部活动与连接异常, 而且呈现出以默认网络为核心, 包含认知控制网络、突显网络、负性情绪网络的网络间连接异常模式。此外, 结合症状、治疗和大尺度脑网络的视角, 可为失眠的“精准治疗”提供神经理论依据。未来研究可结合大数据和多模态分析技术, 验证静息态功能磁共振已有发现。而失眠的纵向追踪和队列研究会有利于进一步阐释失眠的神经机制。     

What drives the organization of object knowledge in the brain?

Various forms of category-specificity have been described at both the cognitive and neural levels, inviting the inference that different semantic domains are processed by distinct, dedicated mechanisms. In this paper, we argue for an extension of a domain-specific interpretation to these phenomena that is based on network-level analyses of functional coupling among brain regions. On this view, domain-specificity in one region of the brain emerges because of innate connectivity with a network of regions that also process information about that domain. Recent findings are reviewed that converge with this framework, and a new direction is outlined for understanding the neural principles that shape the organization of conceptual knowledge.     

Combined functional and causal connectivity analyses of language networks in children: a feasibility study

Instead of assessing activation in distinct brain regions, approaches to investigating the networks underlying distinct brain functions have come into the focus of neuroscience research. Here, we provide a completely data-driven framework for assessing functional and causal connectivity in functional magnetic resonance imaging (fMRI) data, employing Granger's causality. We investigate the networks underlying story processing in 17 healthy children (8f, 9m, 10.4+/-2.8 years, 6.5-15.4 years). Extensive functional connectivity exists between brain regions, including some not detected in standard random effects analyses. Causal connectivity analyses demonstrate a clear dominance of left-sided language regions for both forward and backward interactions with other network nodes. We believe our approach to be useful in helping to assess language networks in the normal or pathological setting; it may also aid in providing better starting estimates for the more hypothesis-driven approaches like structural equation or dynamic causal modeling.     

Risk seeking for losses modulates the functional connectivity of the default mode and left frontoparietal networks in young males

Value-based decision making (VBDM) is a principle that states that humans and other species adapt their behavior according to the dynamic subjective values of the chosen or unchosen options. The neural bases of this process have been extensively investigated using task-based fMRI and lesion studies. However, the growing field of resting-state functional connectivity (RSFC) may shed light on the organization and function of brain connections across different decision-making domains. With this aim, we used independent component analysis to study the brain network dynamics in a large cohort of young males (N = 145) and the relationship of these dynamics with VBDM. Participants completed a battery of behavioral tests that evaluated delay aversion, risk seeking for losses, risk aversion for gains, and loss aversion, followed by an RSFC scan session. We identified a set of large-scale brain networks and conducted our analysis only on the default mode network (DMN) and networks comprising cognitive control, appetitive-driven, and reward-processing regions. Higher risk seeking for losses was associated with increased connectivity between medial temporal regions, frontal regions, and the DMN. Higher risk seeking for losses was also associated with increased coupling between the left frontoparietal network and occipital cortices. These associations illustrate the participation of brain regions involved in prospective thinking, affective decision making, and visual processing in participants who are greater risk-seekers, and they demonstrate the sensitivity of RSFC to detect brain connectivity differences associated with distinct VBDM parameters.     

第二语言学习与脑可塑性

脑可塑性指人脑会因为环境刺激、认知需求和行为经验而产生功能或结构改变。近10年来的单双语者对比和语言训练研究结果表明, 不论儿童、青年或老年人, 第二语言学习和使用都能改变其脑运行模式并带来相应结构变化, 包括灰质(GM)体积和白质(WM)密度增加, 且长期持续的双语经验还能形成认知优势, 帮助抵制由老化导致的负面认知影响。基于脑可塑性概念及其研究证据, 从双语经验与语言训练两方面, 对比分析了长期和短期第二语言学习引起脑功能或结构变化及其内在机制, 并对未来相关研究进行了展望。     

Learning in small connectionist networks does not generalize to large networks

Summary A number of recent models of human information processing have been based on connectionist architectures. Such models are designed to illustrate specific psychological principles and are usually implemented in small networks. The assumption implicit in the work is that principles illustrated in small networks can be applied easily to brain-size networks by scaling up as required. To consider the scaling question, we used a back-propagation algorithm to compare learning in both large and small networks and found that learning depended on the size of the network. In small networks, increasing (the rate-of-learning parameter) beyond 1 increased the rate of learning; in large networks, the same manipulation reduced the rate of learning. The example illustrates the difficulty of generalizing across network size and calls into question the assumption that principles illustrated in small networks can be applied to the brain by expansion of the network.Formerly at Queen's University at Kingston     

Functional Brain Connectivity Using fMRI in Aging and Alzheimer’s Disease

Normal aging and Alzheimer’s disease (AD) cause profound changes in the brain’s structure and function. AD in particular is accompanied by widespread cortical neuronal loss, and loss of connections between brain systems. This degeneration of neural pathways disrupts the functional coherence of brain activation. Recent innovations in brain imaging have detected characteristic disruptions in functional networks. Here we review studies examining changes in functional connectivity, measured through fMRI (functional magnetic resonance imaging), starting with healthy aging and then Alzheimer’s disease. We cover studies that employ the three primary methods to analyze functional connectivity—seed-based, ICA (independent components analysis), and graph theory. At the end we include a brief discussion of other methodologies, such as EEG (electroencephalography), MEG (magnetoencephalography), and PET (positron emission tomography). We also describe multi-modal studies that combine rsfMRI (resting state fMRI) with PET imaging, as well as studies examining the effects of medications. Overall, connectivity and network integrity appear to decrease in healthy aging, but this decrease is accelerated in AD, with specific systems hit hardest, such as the default mode network (DMN). Functional connectivity is a relatively new topic of research, but it holds great promise in revealing how brain network dynamics change across the lifespan and in disease.     

Anxious personality and functional efficiency of the insular-opercular network: A graph-analytic approach to resting-state fMRI

The brain is an intricate network, not only structurally but also functionally. On the functional level, connectivity in the brain is organized in separable yet interacting networks that support information processing by maintaining a ready state, even in the absence of external stimulation. It has been hypothesized that an insular-opercular network underlies the processing of emotionally salient information and that individual differences in functional connectivity within this network correspond to individual differences in trait anxiety. Here, we tested this relationship by applying graph analysis to multiple regions of interests delineating the insular-opercular network to estimate the characteristic path length that quantifies the overall information exchange efficiency within a given network. We found that people scoring high on the anxiety-related temperament-dimension harm avoidance had decreased insular-opercular network efficiency in the resting state, as indicated by a higher characteristic path length. Furthermore, people scoring high on harm avoidance showed generally reduced functional connectivity between brain regions; the relationship between harm avoidance and insular-opercular network efficiency remained significant when controlling for mean connectivity within this network. No such results were found for other resting-state networks. The results provide insights into how personality is organized in the human brain and point toward clinically relevant endophenotypes for affective and mood disorders.     

Short-term meditation induces changes in brain resting EEG theta networks

Many studies have reported meditation training has beneficial effects on brain structure and function. However, very little is known about meditation-induced changes in brain complex networks. We used network analysis of electroencephalography theta activity data at rest before and after 1-week of integrative body–mind training (IBMT) and relaxation training. The results demonstrated the IBMT group (but not the relaxation group) exhibited significantly smaller average path length and larger clustering coefficient of the entire network and two midline electrode nodes (Fz and Pz) after training, indicating enhanced capacity of local specialization and global information integration in the brain. The findings provide the evidence for meditation-induced network plasticity and suggest that IBMT might be helpful for alterations in brain networks.     

元认知的神经机制:元认知功能、脑区与脑网络

元认知是指个体对当前进行的认知活动的监测和调节。通过对元认知神经机制研究的梳理,归纳出两条研究主线:一条主线以具体元认知加工的脑区激活研究为主,主要考察元认知与认知过程的分离及典型元认知加工的共享性和特异性。研究发现元认知加工主要与前额叶有关,还涉及脑岛、顶叶、颞叶和楔前叶等部分脑区。另一条主线探讨广泛的元认知功能与广泛大脑网络的相关。研究提出了大脑的“元认知网络”的概念,发现脑损伤或病变造成的元认知功能损伤与脑功能网络有关,而非固定在某一特定脑区。建议未来的研究应重视四个方面的研究:元认知加工的具体脑区和脑功能网络,元认知神经机制的分离,元认知脑功能的损伤和元认知训练的改善作用,以及基于神经机制研究的元认知模型的建构与修正。     

Connecting the Dots: A Review of Resting Connectivity MRI Studies in Attention-Deficit/Hyperactivity Disorder

Psychopathology is increasingly viewed from a circuit perspective in which a disorder stems not from circumscribed anomalies in discrete brain regions, but rather from impairments in distributed neural networks. This focus on neural circuitry has rendered resting state functional connectivity MRI (rs-fcMRI) an increasingly important role in the elucidation of pathophysiology including attention-deficit/hyperactivity disorder (ADHD). Unlike many other MRI techniques that focus on the properties of discrete brain regions, rs-fcMRI measures the coherence of neural activity across anatomically disparate brain regions, examining the connectivity and organization of neural circuits. In this review, we explore the methods available to investigators using rs-fcMRI techniques, including a discussion of their relative merits and limitations. We then review findings from extant rs-fcMRI studies of ADHD focusing on neural circuits implicated in the disorder, especially the default mode network, cognitive control network, and cortico-striato-thalamo-cortical loops. We conclude by suggesting future directions that may help advance subsequent rs-fcMRI research in ADHD.     

Word and letter string processing networks in schizophrenia: evidence for anomalies and compensation

Imaging studies show that in normal language correlated activity between anterior and posterior brain regions increases as the linguistic and semantic content (i.e., from false fonts, letter strings, pseudo words, to words) of stimuli increase. In schizophrenia however, disrupted functional connectivity between frontal and posterior brain regions has been frequently reported and these disruptions may change the nature of language organization. We characterized basic linguistic operations in word and letter string processing in a region-of-interest network using structural equation modeling (SEM). Healthy volunteers and volunteers with schizophrenia performed an fMRI one-back matching task with real words and consonant letter strings. We hypothesized that left hemisphere network dysfunction in schizophrenia would be present during processes dealing with linguistic/semantic content. The modeling results suggest aberrant left hemisphere function in schizophrenia, even in tasks requiring minimal access to language. Alternative mechanisms included increases in right hemisphere involvement and increased top-down influence from frontal to posterior regions.     

Functional networks involved in spatial learning strategies in middle-aged rats

Our aim was to assess the way that middle-aged rats solve spatial learning tasks that can be performed using different strategies. We assessed the brain networks involved in these spatial learning processes using Principal Component Analysis. Two tasks were performed in a complex context, a four-arm radial maze, in which each group must use either an allocentric or an egocentric strategy. Another task was performed in a simple T-maze in which rats must use an egocentric strategy. Brain metabolic activity was quantified to evaluate neural changes related to spatial learning in the described tasks. Our findings revealed that two functional networks are involved in spatial learning in aged rats. One of the networks, spatial processing, is composed of brain regions involved in the integration of sensory and motivational information. The other network, context-dependent processing, mainly involves the dorsal hippocampus and is related to the processing of contextual information from the environment. Both networks work together to solve spatial tasks in a complex spatial environment.     

Bonds and boundaries: Network structure,organizational boundaries,and job performance

We propose and test a framework that describes the relationship between network structures and job performance. We provide an integration of the current conceptualizations of social capital as they pertain to job performance outcomes by taking a multi-dimensional view of job performance. We break down job performance into creativity, decision-making, task execution, and teamwork, and distinguish the effect of structural holes within and across the organizational boundary on these four job performance domains. In an analysis of 318 managers, we find that networks rich in structural holes that cross the organizational boundary had a positive association with creativity and decision-making, whereas networks with few structural holes within the organization had a positive association with task execution and teamwork. We discuss the theoretical implications for integrating the social capital, boundary spanning, and network structure literatures, as well as the practical benefits of giving much more precise advice to managers and employees regarding how to use networks to improve performance at work.