Hurst Exponent Analysis of Resting-State fMRI Signal Complexity across the Adult Lifespan

Dong, J Z; Jing, Bin; Ma, Xiangyu; Li, Han; Mo, Xiao; Li, Haiyun

doi:10.3389/fnins.2018.00034

Cited by 49 publications

(60 citation statements)

References 49 publications

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“…In an initial analysis performed on the entire HCP dataset (n=1003), we found that 725 regions (out of 1114 across seven atlases) exhibited significant sex differences in HE. The regions identified in this analysis matched those previously observed [3]. However, upon repeating the analysis on the grey-matter volume matched subset (n=390) to eliminate the effects of volumetric differences, we found that only a subset of the originally identified regions were significantly different between males and females.…”

Section: Discussionsupporting

confidence: 71%

“…However, upon repeating the analysis on the grey-matter volume matched subset (n=390) to eliminate the effects of volumetric differences, we found that only a subset of the originally identified regions were significantly different between males and females. This suggests that previous results [3] may be confounded by volume differences and these differences must be accounted for when analysing sex differences.…”

Section: Discussionmentioning

confidence: 99%

“…The use of HE to study signal fluctuations in functional MRI (fMRI) in both healthy and clinical populations has recently emerged [1][2][3][4][5]. In recent years, it has been observed that a baseline state of wakefulness in healthy subjects is associated with critical dynamics in the resting state time series and unconscious brain states (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…This departure corresponds to a reduction in long-range temporal dependence in the frontal lobe, salience network, and thalamus during unconsciousness [1], suggesting that there is a relationship between consciousness and regional temporal dependence in the brain. Another recent study looked at the relationship between long-range temporal dependence and sex in healthy subjects and observed sex differences in frontal, parietal, occipital, and limbic lobes [3].…”

Section: Introductionmentioning

confidence: 99%

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Sex classification using long-range temporal dependence of resting-state functional MRI time series

Dhamala

Jamison

Sabuncu

et al. 2019

Preprint

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A thorough understanding of sex differences, if any, that exist in the brains of healthy individuals is crucial for the study of neurological illnesses that exhibit differences in clinical and behavioural phenotypes between males and females. In this work, we evaluate sex differences in regional temporal dependence of resting-state brain activity using 195 male-female pairs (aged 22-37) from the Human Connectome Project. Malefemale pairs are strictly matched for total grey matter volume. We find that males have more persistent long-range temporal dependence than females in regions within temporal, parietal, and occipital cortices. Machine learning algorithms trained on regional temporal dependence measures achieve sex classification accuracies of up to 81%.Regions with the strongest feature importance in the sex classification task included cerebellum, amygdala, frontal cortex, and occipital cortex. Additionally, we find that even after males and females are strictly matched on total grey matter volume, significant regional volumetric sex differences persist in many cortical and subcortical regions. Our results indicate males have larger cerebella, hippocampi, parahippocampi, thalami, caudates, and amygdalae while females have larger cingulates, precunei, frontal cortices, and parietal cortices. Sex classification based on regional volume achieves accuracies of up to 85%; cerebellum, cingulate cortex, and temporal cortex are the most important features. These findings highlight the important role of strict volume matching when studying brain-based sex differences. Differential patterns in regional temporal dependence between males and females identifies a potential neurobiological substrate underlying sex differences in functional brain activation patterns and the behaviours with which they correlate.

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Section: Discussionsupporting

confidence: 71%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sex classification using long-range temporal dependence of resting-state functional MRI time series

Dhamala

Jamison

Sabuncu

et al. 2019

Preprint

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“…Since early demonstrations of using the Hurst exponent from the toolbox of chaos theory and fractals to analyse fMRI BOLD signal complexity (e.g., [7,8,9]), interest has grown in this novel method for characterizing complexity of fMRI activation. The Hurst exponent has been applied to many domains of research including Alzheimer's disease [10], signal complexity change over the adult lifespan [11], distress related to medical treatment [12], task versus rest states [13], and alcohol-induced intoxication versus non-intoxicated states [14]. Other measures of complexity such as functional variability have also demonstrated meaningful real-world associations (e.g., children develop more complex brain activity with age, see [15]).…”

Section: Introductionmentioning

confidence: 99%

Brain structural connectivity predicts brain functional complexity: DTI derived centrality accounts for variance in fractal properties of fMRI signal

Neudorf

Ekstrand

Kress

et al. 2019

Preprint

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AbstractThe complexity of brain activity has recently been investigated using the Hurst (H) exponent, which describes the extent to which functional magnetic resonance imaging (fMRI) blood oxygen-level dependent (BOLD) activity is self-similar vs. complex. For example, research has demonstrated that fMRI activity is more complex before than after consumption of alcohol and during task than resting state. The measurement of H in fMRI is a novel method that requires the investigation of additional factors contributing to complexity. Graph theory metrics of centrality can assess how centrally important to the brain network each region is, based on diffusion tensor imaging (DTI) counts of probabilistic white matter (WM) tracts. DTI derived centrality was hypothesized to account for the complexity of functional activity, based on the supposition that more sources of information to integrate should result in more complex activity. FMRI BOLD complexity as measured by H was associated with five brain region centrality measures: degree, eigenvector, PageRank, current flow betweenness, and current flow closeness centrality. Multiple regression analyses demonstrated that degree centrality was the most robust predictor of complexity, whereby greater centrality was associated with increased complexity (lower H). Regions known to be highly connected, including the thalamus and hippocampus, notably were among the highest in centrality and complexity. This research has led to a greater understanding of how brain region characteristics such as DTI centrality relate to the novel Hurst exponent approach for assessing brain activity complexity, and implications for future research that employ these measures are discussed.

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Thorax computed tomography (CTX) guided ground truth annotation of CHEST radiographs (CXR) for improved classification and detection of COVID‐19

Ertürk,

Toprak,

Cömert

et al. 2024

Numer Methods Biomed Eng

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Several data sets have been collected and various artificial intelligence models have been developed for COVID‐19 classification and detection from both chest radiography (CXR) and thorax computed tomography (CTX) images. However, the pitfalls and shortcomings of these systems significantly limit their clinical use. In this respect, improving the weaknesses of advanced models can be very effective besides developing new ones. The inability to diagnose ground‐glass opacities by conventional CXR has limited the use of this modality in the diagnostic work‐up of COVID‐19. In our study, we investigated whether we could increase the diagnostic efficiency by collecting a novel CXR data set, which contains pneumonic regions that are not visible to the experts and can only be annotated under CTX guidance. We develop an ensemble methodology of well‐established deep CXR models for this new data set and develop a machine learning‐based non‐maximum suppression strategy to boost the performance for challenging CXR images. CTX and CXR images of 379 patients who applied to our hospital with suspected COVID‐19 were evaluated with consensus by seven radiologists. Among these, CXR images of 161 patients who also have had a CTX examination on the same day or until the day before or after and whose CTX findings are compatible with COVID‐19 pneumonia, are selected for annotating. CTX images are arranged in the main section passing through the anterior, middle, and posterior according to the sagittal plane with the reformed maximum intensity projection (MIP) method in the coronal plane. Based on the analysis of coronal MIP reconstructed CTX images, the regions corresponding to the pneumonia foci are annotated manually in CXR images. Radiologically classified posterior to anterior (PA) CXR of 218 patients with negative thorax CTX imaging were classified as COVID‐19 pneumonia negative group. Accordingly, we have collected a new data set using anonymized CXR (JPEG) and CT (DICOM) images, where the PA CXRs contain pneumonic regions that are hidden or not easily recognized and annotated under CTX guidance. The reference finding was the presence of pneumonic infiltration consistent with COVID‐19 on chest CTX examination. COVID‐Net, a specially designed convolutional neural network, was used to detect cases of COVID‐19 among CXRs. Diagnostic performances were evaluated by ROC analysis by applying six COVID‐Net variants (COVIDNet‐CXR3‐A, ‐B, ‐C/COVIDNet‐CXR4‐A, ‐B, ‐C) to the defined data set and combining these models in various ways via ensemble strategies. Finally, a convex optimization strategy is carried out to find the outperforming weighted ensemble of individual models. The mean age of 161 patients with pneumonia was 49.31 ± 15.12, and the median age was 48 years. The mean age of 218 patients without signs of pneumonia in thorax CTX examination was 40.04 ± 14.46, and the median was 38. When working with different combinations of COVID‐Net's six variants, the area under the curve (AUC) using the ensemble COVID‐Net CXR 4A‐4B‐3C was .78, sensitivity 67%, specificity 95%; COVID‐Net CXR 4a‐3b‐3c was .79, sensitivity 69% and specificity 94%. When diverse and complementary COVID‐Net models are used together through an ensemble, it has been determined that the AUC values are close to other studies, and the specificity is significantly higher than other studies in the literature.

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Hurst Exponent Analysis of Resting-State fMRI Signal Complexity across the Adult Lifespan

Cited by 49 publications

References 49 publications

Sex classification using long-range temporal dependence of resting-state functional MRI time series

Sex classification using long-range temporal dependence of resting-state functional MRI time series

Brain structural connectivity predicts brain functional complexity: DTI derived centrality accounts for variance in fractal properties of fMRI signal

Thorax computed tomography (CTX) guided ground truth annotation of CHEST radiographs (CXR) for improved classification and detection of COVID‐19

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