Structural control energy of resting-state functional brain states reveals less cost-effective brain dynamics in psychosis vulnerability.
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ABSTRACT:
SUBMITTER: Zoller D
PROVIDER: S-EPMC8046160 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The human brain at rest exhibits intrinsic dynamics transitioning among the multiple metastable states of the inter-regional functional connectivity. Accordingly, the demand for exploring the state-specific functional connectivity increases for a deeper understanding of mental diseases. Functional connectivity, however, lacks information about the directed causal influences among the brain regions, called effective connectivity. This study presents the dynamic causal modeling (DCM) framework to explore the state-dependent effective connectivity using spectral DCM for the resting-state functional MRI (rsfMRI). We established the sequence of brain states using the hidden Markov model with the multivariate autoregressive coefficients of rsfMRI, summarizing the functional connectivity. We decomposed the state-dependent effective connectivity using a parametric empirical Bayes scheme that models the effective connectivity of consecutive windows with the time course of the discrete states as regressors. We showed the plausibility of the state-dependent effective connectivity analysis in a simulation setting. To test the clinical applicability, we applied the proposed method to characterize the state- and subtype-dependent effective connectivity of the default mode network in children with combined-type attention deficit hyperactivity disorder (ADHD-C) compared with age-matched, typically developed children (TDC). All 88 children were subtyped according to the occupation times (i.e., dwell times) of the three dominant functional connectivity states, independently of clinical diagnosis. The state-dependent effective connectivity differences between ADHD-C and TDC according to the subtypes and those between the subtypes of ADHD-C were expressed mainly in self-inhibition, magnifying the importance of excitation inhibition balance in the subtyping. These findings provide a clear motivation for decomposing the state-dependent dynamic effective connectivity and state-dependent analysis of the directed coupling in exploring mental diseases.
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Project description:Identifying predictors and elucidating the fundamental mechanisms underlying onset of psychosis are critical for the development of targeted preemptive interventions. This article presents a selective review of findings on risk prediction algorithms and potential mechanisms of onset in youth at clinical high-risk for psychosis, focusing principally on recent findings of the North American Prodrome Longitudinal Study (NAPLS). Multivariate models incorporating risk factors from clinical, demographic, neurocognitive, and psychosocial assessments achieve high levels of predictive accuracy when applied to individuals who meet criteria for a prodromal risk syndrome. An individualized risk calculator is available to scale the risk for newly ascertained cases, which could aid in clinical decision making. At risk individuals who convert to psychosis show elevated levels of proinflammatory cytokines, as well as disrupted resting state thalamo-cortical functional connectivity at baseline, compared with those who do not. Further, converters show a steeper rate of gray matter reduction, most prominent in prefrontal cortex, that in turn is predicted by higher levels of inflammatory markers at baseline. Microglia, resident immune cells in the brain, have recently been discovered to influence synaptic plasticity in health and impair plasticity in disease. Processes that modulate microglial activation may represent convergent mechanisms that influence brain dysconnectivity and risk for onset of psychosis and thus may be targetable in developing and testing preventive interventions.
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Project description:Resting state functional magnetic resonance imaging studies of psychosis have focused primarily on the amplitude of low-frequency fluctuations in the blood oxygen level dependent (BOLD) signal ranging from .01 to 0.1 Hz. Few studies, however, have investigated the amplitude of frequency fluctuations within discrete frequency bands and higher than 0.1 Hz in patients with psychosis at different illness stages. We investigated BOLD signal within three frequency ranges including slow-4 (.027-.073?Hz), slow-3 (.074-0.198?Hz) and slow-2 (0.199-0.25?Hz) in 89 patients with either first-episode or chronic psychosis and 119 healthy volunteers. We investigated the amplitude of frequency fluctuations within three frequency bands using 47 regions-of-interest placed within 14 known resting state networks derived using group independent component analysis. There were significant group x frequency interactions for the visual and motor cortex networks, with the largest significant group differences (patients < healthy volunteers) evident in slow-4 and slow-3, respectively. Also, healthy volunteers had an overall higher amplitude of frequency fluctuations compared to patients across the three frequency ranges in the visual cortex, dorsal attention and motor cortex networks with the opposite effect (patients > healthy volunteers) evident within the salience and frontal gyrus networks. Subsequent analyses indicated that these effects were evident in both first-episode and chronic patients. Our study provides new data regarding the importance of BOLD signal fluctuations within different frequency bands in the neurobiology of psychosis.
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Project description:Clarifying dynamic fluctuations in resting-state connectivity in individuals at risk for psychosis (termed clinical high risk [CHR]) may inform understanding of psychotic disorders, such as schizophrenia, which have been associated with dysconnectivity and aberrant salience processing. Dynamic functional connectivity (DFC) investigations provide insight into how neural networks exchange information over time. Currently, there are no published DFC studies involving CHR individuals. This is notable, because understanding how networks may come together and disassociate over time could lend insight into the neural communication that underlies psychosis development and symptomatology. A sliding-window analysis was utilized to examine DFC (defined as the standard deviation over a series of sliding windows) in resting-state scans in a total of 31 CHR individuals and 28 controls. Clinical assessments at baseline and 12 months later were conducted. CHR participants exhibited less DFC (lower standard deviation) in connectivity involving areas of both the salience network (SN) and default mode network (DMN) with regions involved in sensory, motor, attention, and internal cognitive functions relative to controls. Within CHR participants, this pattern was associated with greater positive symptoms 12 months later, possibly reflecting a mechanism behind aberrant salience processing. Higher SN-DMN internetwork DFC related to elevated baseline negative symptoms, anxiety, and depression in CHR participants, which may indicate neurological processes underlying worry and rumination. Overall, through highlighting unique DFC properties within CHR individuals and detecting informative links with clinically relevant symptomatology, results support dysconnectivity and aberrant salience processing models of psychosis. (PsycINFO Database Record
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Project description:Depleted of dopamine, the dynamics of the parkinsonian brain impact on both 'action' and 'resting' motor behaviour. Deep brain stimulation has become an established means of managing these symptoms, although its mechanisms of action remain unclear. Non-invasive characterizations of induced brain responses, and the effective connectivity underlying them, generally appeals to dynamic causal modelling of neuroimaging data. When the brain is at rest, however, this sort of characterization has been limited to correlations (functional connectivity). In this work, we model the 'effective' connectivity underlying low frequency blood oxygen level-dependent fluctuations in the resting Parkinsonian motor network-disclosing the distributed effects of deep brain stimulation on cortico-subcortical connections. Specifically, we show that subthalamic nucleus deep brain stimulation modulates all the major components of the motor cortico-striato-thalamo-cortical loop, including the cortico-striatal, thalamo-cortical, direct and indirect basal ganglia pathways, and the hyperdirect subthalamic nucleus projections. The strength of effective subthalamic nucleus afferents and efferents were reduced by stimulation, whereas cortico-striatal, thalamo-cortical and direct pathways were strengthened. Remarkably, regression analysis revealed that the hyperdirect, direct, and basal ganglia afferents to the subthalamic nucleus predicted clinical status and therapeutic response to deep brain stimulation; however, suppression of the sensitivity of the subthalamic nucleus to its hyperdirect afferents by deep brain stimulation may subvert the clinical efficacy of deep brain stimulation. Our findings highlight the distributed effects of stimulation on the resting motor network and provide a framework for analysing effective connectivity in resting state functional MRI with strong a priori hypotheses.
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Project description:BackgroundNeuropsychological deficits are a core feature of established psychosis and have been previously linked to fronto-temporo-limbic brain alterations. Both neurocognitive and neuroanatomical abnormalities characterize clinical at-risk mental states (ARMS) for psychosis. However, structure-cognition relationships in the ARMS have not been directly explored using multivariate neuroimaging techniques.MethodsVoxel-based morphometry and partial least squares were employed to study system-level covariance patterns between whole-brain morphological data and processing speed, working memory, verbal learning/IQ, and executive functions in 40 ARMS subjects and 30 healthy controls (HC). The detected structure-cognition covariance patterns were tested for significance and reliability using non-parametric permutation and bootstrap resampling.ResultsWe identified ARMS-specific covariance patterns that described a generalized association of neurocognitive measures with predominantly prefronto-temporo-limbic and subcortical structures as well as the interconnecting white matter. In the conversion group, this generalized profile particularly involved working memory and verbal IQ and was positively correlated with limbic, insular and subcortical volumes as well as negatively related to prefrontal, temporal, parietal, and occipital cortices. Conversely, the neurocognitive profiles in the HC group were confined to working memory, learning and IQ, which were diffusely associated with cortical and subcortical brain regions.ConclusionsThese findings suggest that the ARMS and prodromal phase of psychosis are characterized by a convergent mapping from multi-domain neurocognitive measures to a set of prefronto-temporo-limbic and subcortical structures. Furthermore, a neuroanatomical separation between positive and negative brain-cognition correlations may not only point to a biological process determining the clinical risk for disease transition, but also to possible compensatory or dysmaturational neural processes.
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Project description:Neuronal dynamics display a complex spatiotemporal structure involving the precise, context-dependent coordination of activation patterns across a large number of spatially distributed regions. Functional magnetic resonance imaging (fMRI) has played a central role in demonstrating the nontrivial spatial and topological structure of these interactions, but thus far has been limited in its capacity to study their temporal evolution. Here, using high-resolution resting-state fMRI data obtained from the Human Connectome Project, we mapped time-resolved functional connectivity across the entire brain at a subsecond resolution with the aim of understanding how nonstationary fluctuations in pairwise interactions between regions relate to large-scale topological properties of the human brain. We report evidence for a consistent set of functional connections that show pronounced fluctuations in their strength over time. The most dynamic connections are intermodular, linking elements from topologically separable subsystems, and localize to known hubs of default mode and fronto-parietal systems. We found that spatially distributed regions spontaneously increased, for brief intervals, the efficiency with which they can transfer information, producing temporary, globally efficient network states. Our findings suggest that brain dynamics give rise to variations in complex network properties over time, possibly achieving a balance between efficient information-processing and metabolic expenditure.
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Project description:A large amount of brain imaging research has focused on group studies delineating differences between males and females with respect to both cognitive performance as well as structural and functional brain organization. To supplement existing findings, the present study employed a machine learning approach to assess how accurately participants' sex can be classified based on spatially specific resting state (RS) brain connectivity, using 2 samples from the Human Connectome Project (n1 = 434, n2 = 310) and 1 fully independent sample from the 1000BRAINS study (n = 941). The classifier, which was trained on 1 sample and tested on the other 2, was able to reliably classify sex, both within sample and across independent samples, differing both with respect to imaging parameters and sample characteristics. Brain regions displaying highest sex classification accuracies were mainly located along the cingulate cortex, medial and lateral frontal cortex, temporoparietal regions, insula, and precuneus. These areas were stable across samples and match well with previously described sex differences in functional brain organization. While our data show a clear link between sex and regionally specific brain connectivity, they do not support a clear-cut dimorphism in functional brain organization that is driven by sex alone.
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