Fronto-limbic dysconnectivity leads to impaired brain network controllability in young people with bipolar disorder and those at high genetic risk

Jeganathan, Jayson; Perry, Alistair; Bassett, Danielle S.; Roberts, Gloria; Mitchell, Philip B.; Breakspear, Michael

doi:10.1101/222216

Cited by 25 publications

(39 citation statements)

References 74 publications

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“…Those at familial risk who remained well, may have a protective mechanism in place that homeostatically downregulates SN connectivity (even below control level), specifically with the ECN and frontal brain regions in our sample. This potential explanation is partly consistent with controllability deficits found in individuals at familial risk for mood disorder in prefrontal, superior temporal and striatal regions compared to controls …”

Section: Discussionsupporting

confidence: 80%

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Aberrant structural covariance networks in youth at high familial risk for mood disorder

et al. 2019

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Objectives Current research suggests significant disruptions in functional brain networks in individuals with mood disorder, and in those at familial risk. Studies of structural brain networks provide important insights into synchronized maturational change but have received less attention. We aimed to investigate developmental relationships of large‐scale brain networks in mood disorder using structural covariance (SC) analyses. Methods We conducted SC analysis of baseline structural imaging data from 121 at the time of scanning unaffected high risk (HR) individuals (29 later developed mood disorder after a median time of 4.95 years), and 89 healthy controls (C‐well) with no familial risk from the Scottish Bipolar Family Study (age 15‐27, 64% female). Voxel‐wise analyses of covariance were conducted to compare the associations between each seed region in visual, auditory, motor, speech, semantic, executive‐control, salience and default‐mode networks and the whole brain signal. SC maps were compared for (a) HR(all) versus C‐well individuals, and (b) between those who remained well (HR‐well), versus those who subsequently developed mood disorder (HR‐MD), and C‐well. Results There were no significant differences between HR(all) and C‐well individuals. On splitting the HR group based on subsequent clinical outcome, the HR‐MD group however displayed greater baseline SC in the salience and executive‐control network, and HR‐well individuals showed less SC in the salience network, compared to C‐well, respectively (P < .001). Conclusions These findings indicate differences in network‐level inter‐regional relationships, especially within the salience network, which precede onset of mood disorder in those at familial risk.

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Section: Discussionsupporting

confidence: 80%

“…This potential explanation is partly consistent with controllability deficits found in individuals at familial risk for mood disorder in prefrontal, superior temporal and striatal regions compared to controls. 43…”

Section: Markers Of Non-transitionmentioning

confidence: 99%

Aberrant structural covariance networks in youth at high familial risk for mood disorder

et al. 2019

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“…Are brain networks designed to facilitate control 240 ? What are the principles that allow brain networks to control themselves toward desired activity states 241,242 ?…”

Section: [Box 3 Here]mentioning

confidence: 99%

The physics of brain network structure, function and control

2019

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The brain is a complex organ characterized by heterogeneous patterns of structural connections supporting unparalleled feats of cognition and a wide range of behaviors. New noninvasive imaging techniques now allow these patterns to be carefully and comprehensively mapped in individual humans and animals. Yet, it remains a fundamental challenge to understand how the brain's structural wiring supports cognitive processes, with major implications for the personalized treatment of mental health disorders. Here, we review recent efforts to meet this challenge that draw on intuitions, models, and theories from physics, spanning the domains of statistical mechanics, information theory, and dynamical systems and control. We begin by considering the organizing principles of brain network architecture instantiated in structural wiring under constraints of symmetry, spatial embedding, and energy minimization. We next consider models of brain network function that stipulate how neural activity propagates along these structural connections, producing the long-range interactions and collective dynamics that support a rich repertoire of system functions. Finally, we consider perturbative experiments and models for brain network control, which leverage the physics of signal transmission along structural wires to infer intrinsic control processes that support goal-directed behavior and to inform stimulation-based therapies for neurological disease and psychiatric disorders. Throughout, we highlight several open questions in the physics of brain network structure, function, and control that will require creative efforts from physicists willing to brave the complexities of living matter. resents 36 , whether it be a brain, a granular material 306 , or a quantum lattice 307 . By far the simplest network model is represented by a binary undirected graph in which identical nodes represent system components and identical edges indicate relations or connections between pairs of nodes (see the figure). Such a network can be encoded in an adjacency matrix A, where each element A ij indicates the strength of connectivity between nodes i and j. When all edge strengths are unity, the network is said to be binary. When edges have a range of weights, the network represented by the adjacency matrix is said to be weighted. When A = A , the network is undirected; otherwise, the network is directed.One can extend this simple encoding to study multilayer, multislice, and multiplex networks 308 ; dynamic or temporal networks 127, 309 ; annotated networks 310 ; hypergraphs 311 ; and simplicial complexes 230 . One can also calculate various statistics to quantify the architecture of a network and to infer the function thereof (see figure). Intuitively, these statistics range from measures of the local structure in the network, which depend solely on the links directly emanating from a given node (e.g., degree and clustering), to measures of the network's global structure, which depend on the complex pattern of interconnections between all nodes (e.g.,...

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“…Such linear estimation of the dynamics makes available powerful computational tools in matrix and linear systems theory, and allowed us to capitalize on recent advances in network control [39,47]. Network control theory is a formal approach to modeling, predicting, and tuning the response of a networked system to exogenous input, and has been recently applied to neural systems at both the cellular [73,78,80] and regional [13,22,31,67] scales (for a recent review, see [66]). In these previous efforts, linear dynamics have been assumed, whereas here such dynamics have been proven, to be relevant for the neural system under study.…”

Section: Discussionmentioning

confidence: 99%

Network topology of neural systems supporting avalanche dynamics predicts stimulus propagation and recovery

Kim

Bassett

2018

Preprint

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Many neural systems display avalanche behavior characterized by uninterrupted sequences of neuronal firing whose distributions of size and durations are heavy-tailed. Theoretical models of such systems suggest that these dynamics support optimal information transmission and storage. However, the unknown role of network structure precludes an understanding of how variations in network topology manifest in neural dynamics and either support or impinge upon information processing. Here, using a generalized spiking model, we develop a mechanistic understanding of how network topology supports information processing through network dynamics. First, we show how network topology determines network dynamics by analytically and numerically demonstrating that network topology can be designed to propagate stimulus patterns for long durations. We then identify strongly connected cycles as empirically observable network motifs that are prevalent in such networks. Next, we show that within a network, mathematical intuitions from network control theory are tightly linked with dynamics initiated by node-specific stimulation and can identify stimuli that promote long-lasting cascades. Finally, we use these network-based metrics and control-based stimuli to demonstrate that long-lasting cascade dynamics facilitate delayed recovery of stimulus patterns from network activity, as measured by mutual information. Collectively, our results provide evidence that cortical networks are structured with architectural motifs that support long-lasting propagation and recovery of a few crucial patterns of stimulation, especially those consisting of activity in highly controllable neurons. Broadly, our results imply that avalanching neural networks could contribute to cognitive faculties that require persistent activation of neuronal patterns, such as working memory or attention.keywords linear dynamical systems, network control, mutual information 2/25

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Fronto-limbic dysconnectivity leads to impaired brain network controllability in young people with bipolar disorder and those at high genetic risk

Cited by 25 publications

References 74 publications

Aberrant structural covariance networks in youth at high familial risk for mood disorder

Aberrant structural covariance networks in youth at high familial risk for mood disorder

The physics of brain network structure, function and control

Network topology of neural systems supporting avalanche dynamics predicts stimulus propagation and recovery

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