Aberrant static and dynamic functional patterns of frontoparietal control network in antipsychotic‐naïve first‐episode psychosis subjects

Briend, Frederic; Armstrong, William P.; Kraguljac, Nina; Keilhloz, Shella D.; Lahti, Adrienne C.

doi:10.1002/hbm.24992

Cited by 19 publications

(15 citation statements)

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“…QPP1 was selected as the template with the maximum sum, which reflects a combination of high strength and frequent occurrence. In humans, QPP1 is ~20s long (Majeed et al, 2011;Yousefi et al, 2018;Abbas et al, 2019a,b;Briend et al, 2020); hence, we preset the segment duration to 30 timepoints (21.6s). In the prior studies (Majeed et al, 2011;Yousefi et al, 2018;Abbas et al, 2019aAbbas et al, , 2019bBelloy et al, 2018aBelloy et al, , 2018b) the correlation threshold was set to 0.1 for the first three iterations of the main algorithm, and 0.2 for the remaining iterations; however, we increased this threshold to 0.2 for the first two iterations and 0.3 for the remaining iterations, mainly because of high quality and large dataset used here.…”

Section: Methodsmentioning

confidence: 99%

“…One of the recurring spatiotemporal patterns of activity is the quasiperiodic pattern (QPP), obtained by identifying and averaging similar segments of rsfMRI timeseries (Majeed et al, 2011(Majeed et al, , 2009. The primary QPP (QPP1) in humans is approximately 20 seconds in duration and involves a cycle of activation and deactivation of different brain areas with various phases; most prominently, the cortical areas recognized as the task positive network (TPN) exhibit the opposite phase relative to the cortical areas known as the task negative or default mode network (DMN) (Majeed et al, 2011;Yousefi et al, 2018;Abbas et al, 2019aAbbas et al, , 2019bBriend et al, 2020). Focal propagation of activity is also observed, for example, along the medial prefrontal cortex (Majeed et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

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Propagating patterns of intrinsic activity along macroscale gradients coordinate functional connections across the whole brain

Yousefi

Keilholz

2020

Preprint

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The intrinsic activity of the human brain, observed with resting-state fMRI (rsfMRI) and functional connectivity, exhibits macroscale spatial organization such as resting-state networks (RSNs) and functional connectivity gradients (FCGs). Dynamic analysis techniques have shown that the time-averaged maps captured by functional connectivity are mere summaries of time-varying patterns with distinct spatial and temporal characteristics. A better understanding of these patterns might provide insight into aspects of the brain’s intrinsic activity that cannot be inferred by functional connectivity, RSNs or FCGs. Here, we describe three spatiotemporal patterns of coordinated activity across the whole brain obtained by averaging similar ~20-second-long segments of rsfMRI timeseries. In each of these patterns, activity propagates along a particular macroscale FCG, simultaneously across the cortical sheet and in most other brain regions. In some areas, like the thalamus, the propagation suggests previously-undescribed FCGs. The coordinated activity across areas is consistent with known tract-based connections, and nuanced differences in the timing of peak activity between brain regions point to plausible driving mechanisms. The magnitude of correlation within and particularly between RSNs is remarkably diminished when these patterns are regressed from the rsfMRI timeseries, a quantitative demonstration of their significant role in functional connectivity. Taken together, our results suggest that a few recurring patterns of propagating intrinsic activity along macroscale gradients give rise to and coordinate functional connections across the whole brain.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Propagating patterns of intrinsic activity along macroscale gradients coordinate functional connections across the whole brain

Yousefi

Keilholz

2020

Preprint

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“…More focused analyses assessing the functional correlates of auditory hallucinations found that CV in schizophrenic subjects was reduced between the left auditory perception and speech-production brain areas [64]. Psychotic-like experiences CS [66] First episode psychosis Quasi-periodic patterns [67] High risk of psychosis CAPs [68] Clinical high risk of psychosis and early illness schizophrenia CS [69] 22q11 deletion syndrome iCAPs [70] Major depressive disorder MDD CS [33] MDD CS [71] MDD CS, CV [72] MDD Multilayer GA [73] First episode MDD, untreated CS [74] MDD in remission LEiDA [75] When focusing on hallucination severity, a dynamic GA revealed that nodal flexibility of the lateral occipital cortex was perturbed in people with schizophrenia, and this was related to symptom strength [65].…”

Section: Dynamic Approaches Have Been Leveraged To Study Behaviormentioning

confidence: 99%

“…Using dynamic conditional correlation, a framewise alternative to sliding window analysis (Box 2), a study showed that healthy subjects undergoing psychoticlike experiences spent more time in a state characterized by hypoconnectivity within the DMN, and hyperconnectivity within visual regions [66]. Moreover, measuring quasi-periodic patterns in participants with first-episode psychosis revealed hypoconnectivity in the frontoparietal network [67]. Similarly, a CAP analysis with an anterior insula seed demonstrated that individuals at high risk of psychosis have prolonged expression of a CAP that resembles the DMN, as well as decreased CAP switching, compared to healthy controls [68].…”

Section: Dynamic Approaches Have Been Leveraged To Study Behaviormentioning

confidence: 99%

Tapping into Multi-Faceted Human Behavior and Psychopathology Using fMRI Brain Dynamics

Bolton

Morgenroth

Preti

et al. 2020

Trends in Neurosciences

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Human behavior comprises many aspects that stand out by their dynamic nature. To quantify its neural underpinnings, time-resolved fMRI methods have blossomed over the past decade. In this review we conceptually organize a broad repertoire of dynamic analytical pipelines and extract general observations on their application to the study of behavior and brain disorders. We aim to provide an extensive overview instead of examining only selected methodological families or specific behavioral domains. We consider behavioral aspects with distinct long-term stability (e.g., physiological state versus personality), and also address selected brain disorders with complementary genetics and symptomatology. This synthesis exposes the somewhat limited consistency of dynamic findings in the literature, as well as the unbalanced application of the multitude of available approaches which would, owing to their technical specificities, have potential to reveal distinct aspects of dynamics. We call for further comparative and collaborative efforts in the future. Brain Dynamics Inferred from Functional Neuroimaging Are Relevant to the Study of Human BehaviorPerhaps the most remarkable feature of humanity is the profound behavioral diversity across different individuals, which pertains to all factors involved in interactions with the physical and social environment. This diversity underlies variability in personality, physiology, and mental capacity, which in turn are not only constituted by biological influences (e.g., fatigue, the influence of drugs, genetic makeup) but also shaped by experience (e.g., social learning, trauma). Arguably, the brain is the most complex system known to humankind, and understanding this organ is crucial for explaining behavior. Studying the brain at rest has demonstrated that, although the environment has an influence on it, the brain operates intrinsically and is modulated rather than controlled by the environment [1]. This modulation is a recursive process between the brain and the environment mediated by perception and action [2]. Evidently, this process is highly dynamic, as are the environment and the brain [3].Neuroscience, in particular neuroimaging research, aims to relate variability in behavior to changes in the brain. Since its discovery in the early 1990s, functional magnetic resonance imaging (fMRI; see Glossary) has become one of the most prominent methods to this end. fMRI is a non-invasive tool to probe whole-brain activity and enables the study of sophisticated processes that involve functional integration and segregation of different brain areas over time. The study of brain signals during task or other forms of stimulation has been a productive way to decode the representation of specific processes in the brain; however, studies on the intrinsic organization of the brain at rest are equally valuable, and have been shown to predict behavior and psychopathology [4,5].

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“…Spatial patterns of dysconnectivity resemble those in unmedicated, chronic schizophrenia patients ( 71 ). When examining dynamic connectivity, we found that quasi-periodic patterns had a greater impact of fronto-parietal control network connectivity in first episode patients compared to controls, suggesting that brain network dynamics are already altered in this patient group ( 70 ). Taken together, connectivity alterations are clearly already present in medication-naïve patients, which is consistent with the hypothesis that brain network dysconnectivity is a core feature of the illness ( 73 – 75 ).…”

Section: Key Scientific Questions In Neuroimaging Research In Schizopmentioning

confidence: 95%

Neuroimaging as a Window Into the Pathophysiological Mechanisms of Schizophrenia

Kraguljac

Lahti

2021

Front. Psychiatry

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Schizophrenia is a complex neuropsychiatric disorder with a diverse clinical phenotype that has a substantial personal and public health burden. To advance the mechanistic understanding of the illness, neuroimaging can be utilized to capture different aspects of brain pathology in vivo, including brain structural integrity deficits, functional dysconnectivity, and altered neurotransmitter systems. In this review, we consider a number of key scientific questions relevant in the context of neuroimaging studies aimed at unraveling the pathophysiology of schizophrenia and take the opportunity to reflect on our progress toward advancing the mechanistic understanding of the illness. Our data is congruent with the idea that the brain is fundamentally affected in the illness, where widespread structural gray and white matter involvement, functionally abnormal cortical and subcortical information processing, and neurometabolic dysregulation are present in patients. Importantly, certain brain circuits appear preferentially affected and subtle abnormalities are already evident in first episode psychosis patients. We also demonstrated that brain circuitry alterations are clinically relevant by showing that these pathological signatures can be leveraged for predicting subsequent response to antipsychotic treatment. Interestingly, dopamine D2 receptor blockers alleviate neural abnormalities to some extent. Taken together, it is highly unlikely that the pathogenesis of schizophrenia is uniform, it is more plausible that there may be multiple different etiologies that converge to the behavioral phenotype of schizophrenia. Our data underscore that mechanistically oriented neuroimaging studies must take non-specific factors such as antipsychotic drug exposure or illness chronicity into consideration when interpreting disease signatures, as a clear characterization of primary pathophysiological processes is an imperative prerequisite for rational drug development and for alleviating disease burden in our patients.

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Aberrant static and dynamic functional patterns of frontoparietal control network in antipsychotic‐naïve first‐episode psychosis subjects

Cited by 19 publications

References 61 publications

Propagating patterns of intrinsic activity along macroscale gradients coordinate functional connections across the whole brain

Propagating patterns of intrinsic activity along macroscale gradients coordinate functional connections across the whole brain

Tapping into Multi-Faceted Human Behavior and Psychopathology Using fMRI Brain Dynamics

Neuroimaging as a Window Into the Pathophysiological Mechanisms of Schizophrenia

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