scholarly journals Multimodal Neuroimaging Predictors of Learning Performance of Sensorimotor Rhythm Up-Regulation Neurofeedback

2021 ◽  
Vol 15 ◽  
Author(s):  
Linling Li ◽  
Yinxue Wang ◽  
Yixuan Zeng ◽  
Shaohui Hou ◽  
Gan Huang ◽  
...  
Keyword(s):  
Individual Difference ◽  
Resting State ◽  
Resting State Fmri ◽  
Learning Performance ◽  
Multimodal Neuroimaging ◽  
Multimodal Features ◽  
Neuroimaging Data ◽  
Eeg Neurofeedback ◽  
Magnetic Resonance Imaging Mri ◽  
The Individual

Electroencephalographic (EEG) neurofeedback (NFB) is a popular neuromodulation method to help one selectively enhance or inhibit his/her brain activities by means of real-time visual or auditory feedback of EEG signals. Sensory motor rhythm (SMR) NFB protocol has been applied to improve cognitive performance, but a large proportion of participants failed to self-regulate their brain activities and could not benefit from NFB training. Therefore, it is important to identify the neural predictors of SMR up-regulation NFB training performance for a better understanding the mechanisms of individual difference in SMR NFB. Twenty-seven healthy participants (12 males, age: 23.1 ± 2.36) were enrolled to complete three sessions of SMR up-regulation NFB training and collection of multimodal neuroimaging data [resting-state EEG, structural magnetic resonance imaging (MRI), and resting-state functional MRI (fMRI)]. Correlation analyses were performed between within-session NFB learning index and anatomical and functional brain features extracted from multimodal neuroimaging data, in order to identify the neuroanatomical and neurophysiological predictors for NFB learning performance. Lastly, machine learning models were trained to predict NFB learning performance using features from each modality as well as multimodal features. According to our results, most participants were able to successfully increase the SMR power and the NFB learning performance was significantly correlated with a set of neuroimaging features, including resting-state EEG powers, gray/white matter volumes from MRI, regional and functional connectivity (FC) of resting-state fMRI. Importantly, results of prediction analysis indicate that NFB learning index can be better predicted using multimodal features compared with features of single modality. In conclusion, this study highlights the importance of multimodal neuroimaging technique as a tool to explain the individual difference in within-session NFB learning performance, and could provide a theoretical framework for early identification of individuals who cannot benefit from NFB training.

scholarly journals Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training

2021 ◽  
Author(s):  
Takashi Nakano ◽  
Masahiro Takamura ◽  
Haruki Nishimura ◽  
Maro Machizawa ◽  
Naho Ichikawa ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Posterior Cingulate Cortex ◽  
Fmri Data ◽  
Independent Test ◽  
The Individual ◽  
The Brain

AbstractNeurofeedback (NF) aptitude, which refers to an individual’s ability to change its brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical NF applications. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude independent of NF-targeting brain regions. We combined the data in fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect the resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Next we validated the prediction model using independent test data from another site. The result showed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting NF aptitude may be involved in the attentional mode-orientation modulation system’s characteristics in task-free resting-state brain activity.

scholarly journals 3D Segmentation of Perivascular Spaces on T1-Weighted 3 Tesla MR Images With a Convolutional Autoencoder and a U-Shaped Neural Network

2021 ◽  
Vol 15 ◽  
Author(s):  
Philippe Boutinaud ◽  
Ami Tsuchida ◽  
Alexandre Laurent ◽  
Filipa Adonias ◽  
Zahra Hanifehlou ◽  
...  
Keyword(s):  
Learning Performance ◽  
Similar Distribution ◽  
Perivascular Spaces ◽  
Visual Rating ◽  
Independent Expert ◽  
Convolutional Autoencoder ◽  
Mri Scans ◽  
Experienced Operator ◽  
Magnetic Resonance Imaging Mri ◽  
The Individual

We implemented a deep learning (DL) algorithm for the 3-dimensional segmentation of perivascular spaces (PVSs) in deep white matter (DWM) and basal ganglia (BG). This algorithm is based on an autoencoder and a U-shaped network (U-net), and was trained and tested using T1-weighted magnetic resonance imaging (MRI) data from a large database of 1,832 healthy young adults. An important feature of this approach is the ability to learn from relatively sparse data, which gives the present algorithm a major advantage over other DL algorithms. Here, we trained the algorithm with 40 T1-weighted MRI datasets in which all “visible” PVSs were manually annotated by an experienced operator. After learning, performance was assessed using another set of 10 MRI scans from the same database in which PVSs were also traced by the same operator and were checked by consensus with another experienced operator. The Sorensen-Dice coefficients for PVS voxel detection in DWM (resp. BG) were 0.51 (resp. 0.66), and 0.64 (resp. 0.71) for PVS cluster detection (volume threshold of 0.5 within a range of 0 to 1). Dice values above 0.90 could be reached for detecting PVSs larger than 10 mm3 and 0.95 for PVSs larger than 15 mm3. We then applied the trained algorithm to the rest of the database (1,782 individuals). The individual PVS load provided by the algorithm showed a high agreement with a semi-quantitative visual rating done by an independent expert rater, both for DWM and for BG. Finally, we applied the trained algorithm to an age-matched sample from another MRI database acquired using a different scanner. We obtained a very similar distribution of PVS load, demonstrating the interoperability of this algorithm.

scholarly journals The correspondence problem: which brain maps are significantly similar?

2017 ◽  
Author(s):  
Aaron Alexander-Bloch ◽  
Simon N. Vandekar ◽  
Russell T. Shinohara ◽  
Siyuan Liu ◽  
Theodore D. Satterthwaite ◽  
...  
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Critical Issue ◽  
Correspondence Problem ◽  
Anatomical Landmarks ◽  
Resting State Networks ◽  
Brain Maps ◽  
Access Code ◽  
Neuroimaging Data ◽  
Methodological Approaches

ABSTRACTA critical issue in many neuroimaging studies is the comparison between brain maps. How should we test the hypothesis that two or more brain maps are partially convergent or overlap to a significant extent? This “correspondence problem” affects, for example, the interpretation of comparisons between task-based patterns of functional activation, resting-state networks or modules, and neuroanatomical landmarks. In published work, this problem has been addressed with remarkable variability in terms of methodological approaches and statistical rigor. In this paper, we address the correspondence problem using a spatial permutation framework to generate null models of overlap, by applying random rotations to spherical representations of the cortical surface. We use this approach to derive clusters of cognitive functions that are significantly similar in terms of their functional neuroatomical substrates. In addition, using publicly available data, we formally demonstrate the correspondence between maps of task-based functional activity, resting-state fMRI networks and gyral-based anatomical landmarks. We provide open-access code to implement the methods presented for two commonly-used tools for surface based cortical analysis. This spatial permutation approach constitutes a useful advance over widely-used methods for the comparison of cortical maps, and thereby opens up new possibilities for the integration of diverse neuroimaging data.

Integration of Cognitive Tests and Resting State fMRI for the Individual Identification of Mild Cognitive Impairment

2015 ◽  
Vol 12 (6) ◽  
pp. 592-603 ◽  
Author(s):  
Leandro Beltrachini ◽  
Matteo Marco ◽  
Zeike Taylor ◽  
Jyrki Lotjonen ◽  
Alejandro Frangi ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Resting State ◽  
Resting State Fmri ◽  
Individual Identification ◽  
Cognitive Tests ◽  
The Individual

scholarly journals Reduction of Interhemispheric Functional Brain Connectivity in Early Blindness: A Resting-State fMRI Study

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Fen Hou ◽  
Xia Liu ◽  
Zhifeng Zhou ◽  
Jingyi Zhou ◽  
Hengguo Li
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Brain Connectivity ◽  
Gray Matter Volume ◽  
Resting State Fmri ◽  
Association Cortex ◽  
Middle Temporal ◽  
And Gender ◽  
The Individual ◽  
Early Blindness

Objective. The purpose of this study was to investigate the resting-state interhemispheric functional connectivity in early blindness by using voxel-mirrored homotopic connectivity (VMHC). Materials and Methods. Sixteen early blind patients (EB group) and sixteen age- and gender-matched sighted control volunteers (SC group) were recruited in this study. We used VMHC to identify brain areas with significant differences in functional connectivity between different groups and used voxel-based morphometry (VBM) to calculate the individual gray matter volume (GMV). Results. VMHC analysis showed a significantly lower connectivity in primary visual cortex, visual association cortex, and somatosensory association cortex in EB group compared to sighted controls. Additionally, VBM analysis revealed that GMV was reduced in the left lateral calcarine cortices in EB group compared to sighted controls, while it was increased in the left lateral middle occipital gyri. Statistical analysis showed the duration of blindness negatively correlated with VMHC in the bilateral middle frontal gyri, middle temporal gyri, and inferior temporal gyri. Conclusions. Our findings help elucidate the pathophysiological mechanisms of EB. The interhemispheric functional connectivity was impaired in EB patients. Additionally, the middle frontal gyri, middle temporal gyri, and inferior temporal gyri may be potential target regions for rehabilitation.

Exploring the brain contour of implicit infra-low frequency EEG neurofeedback: a resting state fMRI study

2018 ◽  
Vol 131 ◽  
pp. S76
Author(s):  
O.R. Dobrushina ◽  
E.V. Pechenkova ◽  
R.M. Vlasova ◽  
A.D. Rumshiskaya ◽  
L.D. Litvinova ◽  
...  
Keyword(s):  
Resting State ◽  
Low Frequency ◽  
Resting State Fmri ◽  
Fmri Study ◽  
Eeg Neurofeedback ◽  
The Brain

scholarly journals The individual functional connectome is unique and stable over months to years

2017 ◽  
Author(s):  
Corey Horien ◽  
Xilin Shen ◽  
Dustin Scheinost ◽  
R. Todd Constable
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Resting State ◽  
Resting State Fmri ◽  
List Type ◽  
Functional Connectome ◽  
Wide Range ◽  
The Individual ◽  
Brain Functional Connectivity ◽  
Parietal Cortices

AbstractFunctional connectomes computed from fMRI provide a means to characterize individual differences in the patterns of BOLD synchronization across regions of the entire brain. Using four resting-state fMRI datasets with a wide range of ages, we show that individual differences of the functional connectome are stable across three months to three years. Medial frontal and frontoparietal networks appear to be both unique and stable, resulting in high ID rates, as did a combination of these two networks. We conduct analyses demonstrating that these results are not driven by head motion. We also show that the edges demonstrating the most individualized features tend to connect nodes in the frontal and parietal cortices, while edges contributing the least tend to connect cross-hemispheric homologs. Our results demonstrate that the functional connectome is stable across years and is not an idiosyncratic aspect of a specific dataset, but rather reflects stable individual differences in the functional connectivity of the brain.Research highlightsWhole-brain functional connectivity profiles obtained from four resting-state fMRI datasets are unique and stable across 3 months-3 years in adolescents, young adults, and older adultsMedial frontal and frontoparietal networks tended to be both unique and stableIndividual edges in the frontal and parietal cortices tended to be most discriminative of individual subjects

scholarly journals Predicting multimodal MRI outcomes in children with neurodevelopmental conditions following MRI simulator training

2021 ◽  
Author(s):  
Anish K. Simhal ◽  
José O. A. Filho ◽  
Patricia Segura ◽  
Jessica Cloud ◽  
Eva Petkova ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Resting State ◽  
Resting State Fmri ◽  
Simulator Training ◽  
Success Rates ◽  
Hyperactivity Disorder ◽  
Or Practice ◽  
Neuroimaging Data ◽  
Pediatric Brain ◽  
And Diffusion

AbstractPediatric brain imaging holds significant promise for understanding neurodevelopment. However, the requirement to remain still inside a noisy, enclosed scanner remains a challenge. Verbal or visual descriptions of the process, and/or practice in MRI simulators are the norm in preparing children. Yet, the factors predictive of successfully obtaining neuroimaging data remain unclear. We examined data from 250 children (6-12 years, 197 males) with autism and/or attention-deficit/hyperactivity disorder. Children completed systematic MRI simulator training aimed to habituate to the scanner environment and minimize head motion. An MRI session comprised multiple structural, resting-state, task and diffusion scans. Of the 201 children passing simulator training and attempting scanning, nearly all (94%) successfully completed the first structural scan in the sequence, and 88% also completed the following resting state fMRI scan. The number of successful scans decreased as the sequence progressed. Multivariate analyses revealed that age was the strongest predictor of successful scans in the session, with younger children having lower success rates. After age, sensorimotor atypicalities contributed most to prediction. Results provide insights on factors to consider in designing pediatric brain imaging protocols.

scholarly journals 3D segmentation of perivascular spaces on T1-weighted 3 Tesla MR images with a convolutional autoencoder and a U-shaped neural network

2020 ◽  
Author(s):  
Philippe Boutinaud ◽  
Ami Tsuchida ◽  
Alexandre Laurent ◽  
Filipa Adonias ◽  
Zahra Hanifehlou ◽  
...  
Keyword(s):  
Learning Performance ◽  
Similar Distribution ◽  
Perivascular Spaces ◽  
Visual Rating ◽  
Independent Expert ◽  
Convolutional Autoencoder ◽  
Mri Scans ◽  
Experienced Operator ◽  
Magnetic Resonance Imaging Mri ◽  
The Individual

AbstractWe implemented a deep learning (DL) algorithm for the 3-dimensional segmentation of perivascular spaces (PVSs) in deep white matter (DWM) and basal ganglia (BG). This algorithm is based on an autoencoder and a U-shaped network (U-net), and was trained and tested using T1-weighted magnetic resonance imaging (MRI) data from a large database of 1,832 healthy young adults. An important feature of this approach is the ability to learn from relatively sparse data, which gives the present algorithm a major advantage over other DL algorithms. Here, we trained the algorithm with 40 T1-weighted MRI datasets in which all “visible” PVSs were manually annotated by an experienced operator. After learning, performance was assessed using another set of 10 MRI scans from the same database in which PVSs were also traced by the same operator and were checked by consensus with another experienced operator. The Sorensen-Dice coefficients for PVS voxel detection in DWM (resp. BG) were 0.51 (resp. 0.66), and 0.64 (resp. 0.71) for PVS cluster detection (volume threshold of 0.5 within a range of 0 to 1). Dice values above 0.90 could be reached for detecting PVSs larger than 10 mm3 and 0.95 for PVSs larger than 15 mm3. We then applied the trained algorithm to the rest of the database (1,782 individuals). The individual PVS load provided by the algorithm showed a high agreement with a semi-quantitative visual rating done by an independent expert rater, both for DWM and for BG. Finally, we applied the trained algorithm to an age-matched sample from another MRI database acquired using a different scanner. We obtained a very similar distribution of PVS load, demonstrating the interoperability of this algorithm.

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