scholarly journals A ‘Naturalistic Neuroimaging Database’ for understanding the brain using ecological stimuli

2020 ◽  
Author(s):  
Sarah Aliko ◽  
Jiawen Huang ◽  
Florin Gheorghiu ◽  
Stefanie Meliss ◽  
Jeremy I Skipper
Keyword(s):  
Resting State ◽  
Signal To Noise Ratio ◽  
Data Driven ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Complete Understanding ◽  
Movement Data ◽  
Good Signal ◽  
Functional Analyses ◽  
The Brain

AbstractNeuroimaging has advanced our understanding of human psychology using reductionist stimuli that often do not resemble information the brain naturally encounters. It has improved our understanding of the network organization of the brain mostly through analyses of ‘resting-state’ data for which the functions of networks cannot be verifiably labelled. We make a ‘Naturalistic Neuroimaging Database’ (NNDb v1.0) publically available to allow for a more complete understanding of the brain under more ecological conditions during which networks can be labelled. Eighty-six participants underwent behavioural testing and watched one of 10 full-length movies while functional magnetic resonance imaging was acquired. Resulting timeseries data are shown to be of high quality, with good signal-to-noise ratio, few outliers and low movement. Data-driven functional analyses provide further evidence of data quality. They also demonstrate accurate timeseries/movie alignment and how movie annotations might be used to label networks. The NNDb can be used to answer questions previously unaddressed with standard neuroimaging approaches, progressing our knowledge of how the brain works in the real world.

scholarly journals A naturalistic neuroimaging database for understanding the brain using ecological stimuli

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Sarah Aliko ◽  
Jiawen Huang ◽  
Florin Gheorghiu ◽  
Stefanie Meliss ◽  
Jeremy I. Skipper
Keyword(s):  
Resting State ◽  
Signal To Noise Ratio ◽  
Data Driven ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Complete Understanding ◽  
Movement Data ◽  
Good Signal ◽  
Functional Analyses ◽  
The Brain

Abstract Neuroimaging has advanced our understanding of human psychology using reductionist stimuli that often do not resemble information the brain naturally encounters. It has improved our understanding of the network organization of the brain mostly through analyses of ‘resting-state’ data for which the functions of networks cannot be verifiably labelled. We make a ‘Naturalistic Neuroimaging Database’ (NNDb v1.0) publically available to allow for a more complete understanding of the brain under more ecological conditions during which networks can be labelled. Eighty-six participants underwent behavioural testing and watched one of 10 full-length movies while functional magnetic resonance imaging was acquired. Resulting timeseries data are shown to be of high quality, with good signal-to-noise ratio, few outliers and low movement. Data-driven functional analyses provide further evidence of data quality. They also demonstrate accurate timeseries/movie alignment and how movie annotations might be used to label networks. The NNDb can be used to answer questions previously unaddressed with standard neuroimaging approaches, progressing our knowledge of how the brain works in the real world.

Resting state functional magnetic resonance imaging processing techniques in stroke studies

2016 ◽  
Vol 27 (8) ◽  
pp. 871-885 ◽  
Author(s):  
Golrokh Mirzaei ◽  
Hojjat Adeli
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Resting State ◽  
Brain Connectivity ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Stroke Patients ◽  
Processing Techniques ◽  
The Brain

AbstractIn recent years, there has been considerable research interest in the study of brain connectivity using the resting state functional magnetic resonance imaging (rsfMRI). Studies have explored the brain networks and connection between different brain regions. These studies have revealed interesting new findings about the brain mapping as well as important new insights in the overall organization of functional communication in the brain network. In this paper, after a general discussion of brain networks and connectivity imaging, the brain connectivity and resting state networks are described with a focus on rsfMRI imaging in stroke studies. Then, techniques for preprocessing of the rsfMRI for stroke patients are reviewed, followed by brain connectivity processing techniques. Recent research on brain connectivity using rsfMRI is reviewed with an emphasis on stroke studies. The authors hope this paper generates further interest in this emerging area of computational neuroscience with potential applications in rehabilitation of stroke patients.

scholarly journals Brain Functional Magnetic Resonance Imaging Highlights Altered Connections and Functional Networks in Patients With Hypertension

Hypertension ◽  
2020 ◽  
Vol 76 (5) ◽  
pp. 1480-1490 ◽  
Author(s):  
Lorenzo Carnevale ◽  
Angelo Maffei ◽  
Alessandro Landolfi ◽  
Giovanni Grillea ◽  
Daniela Carnevale ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Resting State ◽  
Diffusion Tensor ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Attention Network ◽  
Dorsal Attention Network ◽  
The Brain

Hypertension is one of the main risk factors for vascular dementia and Alzheimer disease. To predict the onset of these diseases, it is necessary to develop tools to detect the early effects of vascular risk factors on the brain. Resting-state functional magnetic resonance imaging can investigate how the brain modulates its resting activity and analyze how hypertension impacts cerebral function. Here, we used resting-state functional magnetic resonance imaging to explore brain functional-hemodynamic coupling across different regions and their connectivity in patients with hypertension, as compared to subjects with normotension. In addition, we leveraged multimodal imaging to identify the signature of hypertension injury on the brain. Our study included 37 subjects (18 normotensives and 19 hypertensives), characterized by microstructural integrity by diffusion tensor imaging and cognitive profile, who were subjected to resting-state functional magnetic resonance imaging analysis. We mapped brain functional connectivity networks and evaluated the connectivity differences among regions, identifying the altered connections in patients with hypertension compared with subjects with normotension in the (1) dorsal attention network and sensorimotor network; (2) dorsal attention network and visual network; (3) dorsal attention network and frontoparietal network. Then we tested how diffusion tensor imaging fractional anisotropy of superior longitudinal fasciculus correlates with the connections between dorsal attention network and default mode network and Montreal Cognitive Assessment scores with a widespread network of functional connections. Finally, based on our correlation analysis, we applied a feature selection to highlight those most relevant to describing brain injury in patients with hypertension. Our multimodal imaging data showed that hypertensive brains present a network of functional connectivity alterations that correlate with cognitive dysfunction and microstructural integrity. Registration— URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02310217.

scholarly journals Image Features of Resting-State Functional Magnetic Resonance Imaging in Evaluating Poor Emotion and Sleep Quality in Patients with Chronic Pain under Artificial Intelligence Algorithm

2022 ◽  
Vol 2022 ◽  
pp. 1-10
Author(s):  
Shuqin Yang ◽  
Xiaoyan Bie ◽  
Yanmei Wang ◽  
Junnan Li ◽  
Yujing Wang ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Chronic Pain ◽  
Sleep Quality ◽  
Resting State ◽  
Brain Function ◽  
Control Group ◽  
Observation Group ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
The Brain

The balanced iterative reducing and clustering using hierarchies (BIRCH) method was adopted to optimize the results of the resting-state functional magnetic resonance imaging (RS-fMRI) to analyze the changes in the brain function of patients with chronic pain accompanied by poor emotion or abnormal sleep quality in this study, so as to provide data support for the prevention and treatment of clinical chronic pain with poor emotion or sleep quality. 159 patients with chronic pain who visited the hospital were selected as the research objects, and they were grouped according to the presence or absence of abnormalities in emotion and sleep. The patients without poor emotion and sleep quality were set as the control group (60 cases), and the patients with the above symptoms were defined in the observation group (90 cases). The brain function was detected by RS-fMRI technology based on the BIRCH algorithm. The results showed that the rand index (RI), adjustment of RI (ARI), and Fowlkes–Mallows index (FMI) results in the k-means, flow cytometry (FCM), and BIRCH algorithms were 0.82, 0.71, and 0.88, respectively. The scores of Hamilton Depression Scale (HAHD), Hamilton Anxiety Scale (HAMA), and Pittsburgh Sleep Quality Index (PSQI) were 7.26 ± 3.95, 7.94 ± 3.15, and 8.03 ± 4.67 in the observation group and 4.03 ± 1.95, 5.13 ± 2.35, and 4.43 ± 2.07 in the control group; the higher proportion of RS-fMRI was with abnormal brain signal connections. A score of 7 or more meant that the number of brain abnormalities was more than 90% and that of less than 7 was less than 40%, showing a statistically obvious difference in contrast P < 0.05 . Therefore, the BIRCH clustering algorithm showed reliable value in the optimization of RS-fMRI images, and RS-fMRI showed high application value in evaluating the emotion and sleep quality of patients with chronic pain.

scholarly journals Resting-state functional magnetic resonance imaging of high altitude patients with obstructive sleep apnoea hypopnoea syndrome

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Zongyuan Qin ◽  
Dongjie Kang ◽  
Xiang Feng ◽  
Demin Kong ◽  
Fangfang Wang ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
High Altitude ◽  
Resting State ◽  
Brain Function ◽  
Sleep Apnoea ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Obstructive Sleep ◽  
The Brain

Abstract The objective of the study was to observe brain function changes in Obstructive Sleep Apnoea Hypopnoea Syndrome (OSAHS) patients at high altitude. Resting-state functional magnetic resonance imaging (rs-fMRI) in patients with OSAHS was assessed using regional homogeneity (ReHo), amplitude of low frequency fluctuation (ALFF) and functional connectivity (FC). In this study, 36 male patients with OSAHS and 38 healthy male subjects were recruited from high-altitude areas, specifically, altitudes of 2,000–3,000 m. OSAHS was diagnosed by polysomnography (PSG). The blood oxygen level-dependent (BOLD) signals of OSAHS patients and healthy controls in the resting state were obtained and compared using ReHo, ALFF and FC methods. The posterior cingulate cortex (PCC) was selected as the seed region in the comparison of FC between the two groups. Compared with the healthy control group, multiple brain functions in the OSAHS patient group were different. There were correlations between the brain function values of some brain regions and demographic data. We also found that in contrast to earlier findings with individuals in plains areas, the brain function at the frontal lobe and the precuneus were higher in OSAHS patients, and the PCC showed higher FC with the left caudate, which may be due to the high-altitude hypoxic environment.

scholarly journals A Transdiagnostic Data-driven Study of Children’s Behaviour and the Functional Connectome

2021 ◽  
Author(s):  
J. S. Jones ◽  
D. E. Astle ◽  
Keyword(s):  
Executive Function ◽  
Data Driven ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Functional Connectome ◽  
Behavioural Difficulties ◽  
Intrinsic Connectivity ◽  
Intrinsic Connectivity Networks ◽  
Children’S Behaviour ◽  
The Brain

AbstractBehavioural difficulties are seen as hallmarks of many neurodevelopmental conditions. Differences in functional brain organisation have been observed in these conditions, but little is known about how they are related to a child’s profile of behavioural difficulties. We investigated whether behavioural difficulties are associated with how the brain is functionally organised in an intentionally heterogeneous and transdiagnostic sample of 957 children aged 5-15. We used consensus community detection to derive data-driven profiles of behavioural difficulties and constructed functional connectomes from a subset of 238 children with resting-state functional Magnetic Resonance Imaging (fMRI) data. We identified three distinct profiles of behaviour that were characterised by principal difficulties with hot executive function, cool executive function, and learning. Global organisation of the functional connectome did not differ between the groups, but multivariate patterns of connectivity at the level of Intrinsic Connectivity Networks (ICNs), nodes, and hubs significantly predicted group membership in held-out data. Fronto-parietal connector hubs were under-connected in all groups relative to a comparison sample, and children with hot vs cool executive function difficulties were distinguished by connectivity in ICNs associated with cognitive control, emotion processing, and social cognition. This demonstrates both general and specific neurodevelopmental risk factors in the functional connectome.

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