Applications of Resting State Functional MR Imaging to Neuropsychiatric Diseases

Pearlson, Godfrey D.

doi:10.1016/j.nic.2017.06.005

Cited by 18 publications

(12 citation statements)

References 142 publications

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“…Resting-state spontaneous activity (Mitra, Snyder, Hacker, & Raichle, 2014;Raichle, & Mintun, 2006;Rogers, Morgan, Newton, & Gore, 2007;Vincent et al, 2007) is presently well established as a substrate to understand normative brain patterns and intrinsic functional organization of macroscopic brain network dynamics. Furthermore, resting-state brain dynamics and its departure from normative patterns are important markers for characterizing various neurological/ neuropsychiatric symptoms (Pearlson, 2017;Zhou, Liu, Kwun, & Wang, 2017). Numerous studies have attempted to understand the underlying mechanisms that govern the spatiotemporal dynamics of large-scale resting-state networks (Deco & Jirsa, 2012;Deco, Ponce-Alvarez, et al, 2014;Demirtas et al, 2017; see also Cabral, Kringelbach, & Deco, 2017, for a review).…”

Section: Introductionmentioning

confidence: 99%

Multiscale dynamic mean field (MDMF) model relates resting-state brain dynamics with local cortical excitatory–inhibitory neurotransmitter homeostasis

Naskar

Vattikonda

Deco

et al. 2021

Network Neuroscience

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Previous computational models have related spontaneous resting-state brain activity with local excitatory−inhibitory balance in neuronal populations. However, how underlying neurotransmitter kinetics associated with E-I balance governs resting state spontaneous brain dynamics remains unknown. Understanding the mechanisms by virtue of which fluctuations in neurotransmitter concentrations, a hallmark of a variety of clinical conditions relate to functional brain activity is of critical importance. We propose a multi-scale dynamic mean field model (MDMF) – a system of coupled differential equations for capturing the synaptic gating dynamics in excitatory and inhibitory neural populations as a function of neurotransmitter kinetics. Individual brain regions are modelled as population of MDMF and are connected by realistic connection topologies estimated from Diffusion Tensor Imaging data. First, MDMF successfully predicts resting-state functionalconnectivity. Second, our results show that optimal range of glutamate and GABA neurotransmitter concentrations subserve as the dynamic working point of the brain, that is, the state of heightened metastability observed in empirical blood-oxygen-level dependent signals. Third, for predictive validity the network measures of segregation (modularity and clustering coefficient) and integration (global efficiency and characteristic path length) from existing healthy and pathological brain network studies could be captured by simulated functional connectivity from MDMF model.

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Section: Introductionmentioning

confidence: 99%

Multiscale dynamic mean field (MDMF) model relates resting-state brain dynamics with local cortical excitatory–inhibitory neurotransmitter homeostasis

Naskar

Vattikonda

Deco

et al. 2021

Network Neuroscience

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“…Asimismo, la detección de circuitos neuronales ha permitido encontrar y estudiar las diferencias funcionales en patologías tan complejas como el Alzheimer o el Parkinson [7], [8], [9], [10].…”

Section: Introductionunclassified

Redes neuronales convolucionales para la clasificación de componentes independientes de rs-fMRI

Mera-Jiménez

Gómez

2021

TecnoL.

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La resonancia magnética funcional en estado de reposo (rs-fMRI) es una de las técnicas más relevantes en exploración cerebral. No obstante, la misma es susceptible a muchos factores externos que pueden ocluir la señal de interés. En este orden de ideas, las imágenes rs-fMRI han sido estudiadas desde diferentes enfoques, existiendo un especial interés en las técnicas de eliminación de artefactos a través del Análisis de Componentes Independientes (ICA por sus siglas en inglés). El enfoque es una herramienta poderosa para la separación ciega de fuentes donde es posible eliminar los elementos asociados a ruido. Sin embargo, dicha eliminación está sujeta a la identificación o clasificación de las componentes entregadas por ICA. En ese sentido, esta investigación se centró en encontrar una estrategia alternativa para la clasificación de las componentes independientes. El problema se abordó en dos etapas. En la primera de ellas, se redujeron las componentes (volúmenes 3D) a imágenes mediante el Análisis de Componentes Principales (PCA por sus siglas en inglés) y con la obtención de los planos medios. Los métodos lograron una reducción de hasta dos órdenes de magnitud en peso de los datos y, además, demostraron conservar las características espaciales de las componentes independientes. En la segunda etapa, se usaron las reducciones para entrenar seis modelos de redes neuronales convolucionales. Las redes analizadas alcanzaron precisiones alrededor de 98 % en la clasificación e incluso se encontró una red con una precisión del 98.82 %, lo cual refleja la alta capacidad de discriminación de las redes neuronales convolucionales.

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“…The technology is non-invasive, non-radioactive, and has the advantages of repeatability and high temporal and spatial resolution 7,8 . In the past, fMRI has been applied to various psychoneurotic diseases, providing new perspectives for further understanding of their neuropathological mechanisms 9,10 . The amplitude of low-frequency uctuation (ALFF) method is a resting state-fMRI (rs-fMRI) approach.…”

Section: Introductionmentioning

confidence: 99%

Abnormal Fractional Amplitude of Low-frequency Fluctuation Changes in Patients With Diabetic Optic Neuropathy: a Functional MRI Study

Liu

Pan

et al. 2021

Preprint

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The present study was to assess the spontaneous changes in brain activity in patients with DON using functional magnetic resonance imaging (fMRI). 14 DON patients and 14 healthy controls (HCs) were enrolled. All participants underwent resting-state fMRI (rs-fMRI). The fractional amplitude of low-frequency fluctuation (fALFF) method was applied to evaluate neural activity changes. The Hospital Anxiety and Depression Scale (HADS) was used to assess the anxiety and depression status of participants. The independent sample t test and chi-squared test were applied to analyze demographics of DON patients and HCs. Receiver operating characteristic (ROC) curves were applied to analyze the variation in mean fALFF values between DON patients and HCs. The relationships between the fALFF values of brain regions and clinical behaviors in the DON group were analyzed using Pearson’s correlation analysis. In contrast to HCs, the fALFF value of DON patients was significantly higher in the right precentral gyrus (RPCG). However, the fALFF values in right anterior cingulate gyrus (RACG) and left middle cingulate gyrus (LMCG) were markedly decreased in DON patients. The area under the curve (AUC) of ROCs for each brain region showed high accuracy. Pearson's correlation analysis showed that fALFF values of the right anterior cingulate gyrus and left middle cingulate gyrus were negatively correlated with HADS scores, while fALFF values of the left middle cingulate gyrus were negatively correlated with DON disease duration. To sum up, we found abnormal spontaneous brain activities in regions related to cognitive and emotional dysfunction, eye movement disorder, and vision loss in patients with DON. These results may indicate the underlying neuropathological mechanisms of DON, and show that fALFF may be an effective method by which to distinguish patients with DON from healthy individuals.

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Applications of Resting State Functional MR Imaging to Neuropsychiatric Diseases

Abstract: SYNOPSIS Resting state studies in neuropsychiatric disorders have already provided much useful information, but the field is regarded as being at a relatively preliminary stage, and subject to a number of design issues that set limits on the overall utility.

Cited by 18 publications

References 142 publications

Multiscale dynamic mean field (MDMF) model relates resting-state brain dynamics with local cortical excitatory–inhibitory neurotransmitter homeostasis

Multiscale dynamic mean field (MDMF) model relates resting-state brain dynamics with local cortical excitatory–inhibitory neurotransmitter homeostasis

Redes neuronales convolucionales para la clasificación de componentes independientes de rs-fMRI

Abnormal Fractional Amplitude of Low-frequency Fluctuation Changes in Patients With Diabetic Optic Neuropathy: a Functional MRI Study

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