Time-varying spectral power of resting-state fMRI networks reveal cross-frequency dependence in dynamic connectivity
نویسندگان
چکیده
Brain oscillations and synchronicity among brain regions (brain connectivity) have been studied in resting-state (RS) and task-induced settings. RS-connectivity which captures brain functional integration during an unconstrained state is shown to vary with the frequency of oscillations. Indeed, high temporal resolution modalities have demonstrated both between and cross-frequency connectivity spanning across frequency bands such as theta and gamma. Despite high spatial resolution, functional magnetic resonance imaging (fMRI) suffers from low temporal resolution due to modulation with slow-varying hemodynamic response function (HRF) and also relatively low sampling rate. This limits the range of detectable frequency bands in fMRI and consequently there has been no evidence of cross-frequency dependence in fMRI data. In the present work we uncover recurring patterns of spectral power in network timecourses which provides new insight on the actual nature of frequency variation in fMRI network activations. Moreover, we introduce a new measure of dependence between pairs of rs-fMRI networks which reveals significant cross-frequency dependence between functional brain networks specifically default-mode, cerebellar and visual networks. This is the first strong evidence of cross-frequency dependence between functional networks in fMRI and our subject group analysis based on age and gender supports usefulness of this observation for future clinical applications.
منابع مشابه
Analysis of Resting-State fMRI Topological Graph Theory Properties in Methamphetamine Drug Users Applying Box-Counting Fractal Dimension
Introduction: Graph theoretical analysis of functional Magnetic Resonance Imaging (fMRI) data has provided new measures of mapping human brain in vivo. Of all methods to measure the functional connectivity between regions, Linear Correlation (LC) calculation of activity time series of the brain regions as a linear measure is considered the most ubiquitous one. The strength of the dependence obl...
متن کاملطبقهبندی بیماری پارکینسون بر مبنای شاخصهای درون-ناحیهای و بین-ناحیهای شبکه حرکتی مغز با استفاده از دادگان fMRI حالت استراحت
Parkinson’s disease (PD) is a progressive neurological disorder characterized by tremor, rigidity, and slowness of movement. Recent studies on investigation of the brain function show that there are spontaneous fluctuations between regions at rest as resting state network affected in various disorders. In this paper, we used amplitude of low frequency fluctuation (ALFF) for the study of intra-r...
متن کاملTask- and stimulus-related cortical networks in language production: Exploring similarity of MEG- and fMRI-derived functional connectivity
Large-scale networks support the dynamic integration of information across multiple functionally specialized brain regions. Network analyses of haemodynamic modulations have revealed such functional brain networks that show high consistency across subjects and different cognitive states. However, the relationship between the slowly fluctuating haemodynamic responses and the underlying neural me...
متن کاملResting State fMRI Functional Connectivity Analysis Using Dynamic Time Warping
Traditional resting-state network concept is based on calculating linear dependence of spontaneous low frequency fluctuations of the BOLD signals of different brain areas, which assumes temporally stable zero-lag synchrony across regions. However, growing amount of experimental findings suggest that functional connectivity exhibits dynamic changes and a complex time-lag structure, which cannot ...
متن کاملBrain Activity Map Extraction from Multiple Sclerosis Patients Using Resting-State fMRI Data Based on Amplitude of Low Frequency Fluctuations and Regional Homogeneity Analysis
Introduction: Multiple Sclerosis (MS) is the most common non-traumatic neurological diseases of young adults. MS often reported during ages 20-62. MS affects the various anatomical parts of the central nervous system. Up to 65% of multiple sclerosis patients MS patients suffer from various problems, such as fatigue, depression, pain and sleep disorders. Unlike MRI, that only sh...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره 12 شماره
صفحات -
تاریخ انتشار 2017