A joint time-frequency analysis of resting-state functional connectivity reveals novel patterns of connectivity shared between or unique to schizophrenia patients and healthy controls

Yaesoubi, Maziar; Miller, Robyn L.; Bustillo, Juan; Lim, Kelvin O.; Vaidya, Jatin G.; Calhoun, Vince D.

doi:10.1016/j.nicl.2017.06.023

Cited by 38 publications

(46 citation statements)

References 42 publications

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“…The most well-known methods in this category utilize wavelets (Mallat, 1999). While these methods have resulted in many interesting findings in functional magnetic resonance imaging (fMRI) (Chang and Glover, 2010;Yaesoubi, et al, 2015;Yaesoubi, et al, 2017), these studies have several limitations. Firstly, the interpretation of the results in these studies can be challenging.…”

Section: Dfnc Estimation Approachesmentioning

confidence: 99%

“…A natural evolution of these studies is to explore the frequency profile of connectivity level information. An earlier attempt at this utilized wavelet coherence methods (Yaesoubi, et al, 2017); But as mentioned earlier, this method implemented frequency tiling in activity space, therefore the relationship between frequency and connectivity patterns is not direct.…”

Section: Schizophrenia-related Findingsmentioning

confidence: 99%

“…To explain the benefits of our proposed approach we designed a toy example. Furthermore, to showcase the utilization of the proposed approach, we implemented it on a dataset used to study sFNC and dFNC previously Yaesoubi, et al, 2017). The reason we chose to use a dataset that has been studied extensively is that then we can situate our results in an existing rich literature, enabling us to compare results from different approaches.…”

Section: Frequency Tiling In the Connectivity Domainmentioning

confidence: 99%

“…In another type of study, Yaesoubi et al, used the wavelet transform to decompose the activity time series into different time-frequency bands and then calculate coherence. Several observations about connectivity frequencies are then made (Yaesoubi, et al, 2017). In our view, these kinds of statements can be misleading as the frequency profile is not directly studied in the connectivity space and therefore it does not enable us to make claims about the connectivity frequency profile.…”

Section: Activation Frequency Vs Connectivity Frequencymentioning

confidence: 99%

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A unified approach for characterizing static/dynamic connectivity frequency profiles using filter banks

Faghiri

Iraji

Damaraju

et al. 2019

Preprint

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Studying dynamic functional connectivity (dFC) has been the focus of many studies in recent years. The most commonly used estimator for dFC uses a sliding window in combination with a connectivity estimator such as Pearson correlation. Here, we propose a new approach to estimate connectivity while preserving its full frequency range and subsequently examine both static and dynamic connectivity in one unified approach. This approach which we call filter banked connectivity (FBC), implements frequency tiling directly in the connectivity domain contrary to other studies where frequency tiling is done in the activity domain. This leads to more accurate modeling, and a unified approach to capture connectivity ranging from static to highly dynamic, avoiding the need to pick a specific band as in a sliding window approach.First, we demonstrated that our proposed approach, can estimate connectivity at frequencies that sliding window approach fails. Next we evaluated the ability of the approach to identify group differences by using the FBC approach to estimate dFNC in a resting fMRI data set including schizophrenia patients (SZ, n=151) and typical controls (TC, n=163). To summarize the results, we used k-means to cluster the FBC values into different clusters. Some states showed very weak low frequency strength and as such SWPC was not well suited to capture them. Additionally, we found that SZs tend to spend more time in states exhibiting higher frequencies and engaging the default mode network and its anticorrelations with other networks compared to TCs which spent more time in lower frequency states which primarily includes strong intercorrelations within the sensorimotor domains. In summary, the proposed approach offers a novel way to estimate connectivity while unifying static and dynamic connectivity analyses and can provide additional otherwise missed information about the frequency profile of connectivity patterns.

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Section: Dfnc Estimation Approachesmentioning

confidence: 99%

Section: Schizophrenia-related Findingsmentioning

confidence: 99%

Section: Frequency Tiling In the Connectivity Domainmentioning

confidence: 99%

Section: Activation Frequency Vs Connectivity Frequencymentioning

confidence: 99%

See 2 more Smart Citations

A unified approach for characterizing static/dynamic connectivity frequency profiles using filter banks

Faghiri

Iraji

Damaraju

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…A wavelet coherence-based clustering of EEG signals has been developed to estimate the brain connectivity in absence epileptic patients [24]. It has also been applied to studies on autism [25], traumatic brain injury [26], schizophrenia [27], and poor sleep quality [28]. However, there is very limited research focusing on its application on ET.…”

Section: Methodsmentioning

confidence: 99%

A Wavelet‐Based Correlation Analysis Framework to Study Cerebromuscular Activity in Essential Tremor

et al. 2018

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Objective. Deep brain stimulation (DBS) provides dramatic tremor relief in patients with severe essential tremor (ET). Typically, the VIM nucleus is the most effective brain area to target for high-frequency electrical stimulation in these patients. Correlation analysis between electrical local field potential (LFP) recordings from the thalamic DBS leads and electrical muscle activity from the contralateral tremulous limb has become an attractive practical tool to interpret the LFPs and their association with the tremulous clinical manifestations. Although functional connectivity analysis between brain electrical recordings and electromyographic (EMG) signals from the tremor has been of interest to an increasing number of engineering researchers, there is no well-accepted tailored framework to consistently characterise the association between thalamic electrical recordings and the tremorogenic EMG activity. Methods. This paper proposes a novel framework to address this challenge, including an estimation of the interaction strength using wavelet cross-spectrum and phase lag index while demonstrating the statistical significance of the findings. Results. Consistent results were estimated for single and multiple trials of consecutive or partially overlapping epochs of data. The latter approach reveals a substantial increase on the range of statistically significant dynamic low-frequency interrelationships while decreasing the dynamic range of high-frequency interactions. Conclusion. Results from both simulation and real data demonstrate the feasibility and robustness of the proposed framework. Significance. This study offers the proof of principle required to implement this methodology to uncover VIM thalamic LFP-EMG interactions for (i) better understanding of the pathophysiology of tremor; (ii) objective selection of the DBS electrode contacts with the highest strength of association with the tremorogenic EMG, a particularly useful feature for the implementation of novel multicontact directional leads in clinical practice; and (iii) future research on DBS closed-loop devices.

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MultimodalMRIassessment for first episode psychosis: A major change in the thalamus and an efficient stratification of a subgroup

Faria

Zhao

et al. 2020

Human Brain Mapping

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Multi‐institutional brain imaging studies have emerged to resolve conflicting results among individual studies. However, adjusting multiple variables at the technical and cohort levels is challenging. Therefore, it is important to explore approaches that provide meaningful results from relatively small samples at institutional levels. We studied 87 first episode psychosis (FEP) patients and 62 healthy subjects by combining supervised integrated factor analysis (SIFA) with a novel pipeline for automated structure‐based analysis, an efficient and comprehensive method for dimensional data reduction that our group recently established. We integrated multiple MRI features (volume, DTI indices, resting state fMRI—rsfMRI) in the whole brain of each participant in an unbiased manner. The automated structure‐based analysis showed widespread DTI abnormalities in FEP and rs‐fMRI differences between FEP and healthy subjects mostly centered in thalamus. The combination of multiple modalities with SIFA was more efficient than the use of single modalities to stratify a subgroup of FEP (individuals with schizophrenia or schizoaffective disorder) that had more robust deficits from the overall FEP group. The information from multiple MRI modalities and analytical methods highlighted the thalamus as significantly abnormal in FEP. This study serves as a proof‐of‐concept for the potential of this methodology to reveal disease underpins and to stratify populations into more homogeneous sub‐groups.

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A joint time-frequency analysis of resting-state functional connectivity reveals novel patterns of connectivity shared between or unique to schizophrenia patients and healthy controls

Cited by 38 publications

References 42 publications

A unified approach for characterizing static/dynamic connectivity frequency profiles using filter banks

A unified approach for characterizing static/dynamic connectivity frequency profiles using filter banks

A Wavelet‐Based Correlation Analysis Framework to Study Cerebromuscular Activity in Essential Tremor

MultimodalMRIassessment for first episode psychosis: A major change in the thalamus and an efficient stratification of a subgroup

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