scholarly journals Shim optimization with region of interest‐specific Tikhonov regularization: Application to second‐order slice‐wise shimming of the brain

2021 ◽  
Author(s):  
Yuhang Shi ◽  
Stuart Clare ◽  
Signe Johanna Vannesjo
Keyword(s):  
Tikhonov Regularization ◽  
Region Of Interest ◽  
Second Order ◽  
The Brain

scholarly journals A probabilistic atlas of the human ventral tegmental area (VTA) based on 7 Tesla MRI data

2021 ◽  
Vol 226 (4) ◽  
pp. 1155-1167 ◽  
Author(s):  
Anne C. Trutti ◽  
Laura Fontanesi ◽  
Martijn J. Mulder ◽  
Pierre-Louis Bazin ◽  
Bernhard Hommel ◽  
...  
Keyword(s):  
Ventral Tegmental Area ◽  
Region Of Interest ◽  
Reward Processing ◽  
7 Tesla ◽  
Subcortical Structures ◽  
7 Tesla Mri ◽  
Subcortical Nuclei ◽  
Ventral Tegmental ◽  
The Brain

AbstractFunctional magnetic resonance imaging (fMRI) BOLD signal is commonly localized by using neuroanatomical atlases, which can also serve for region of interest analyses. Yet, the available MRI atlases have serious limitations when it comes to imaging subcortical structures: only 7% of the 455 subcortical nuclei are captured by current atlases. This highlights the general difficulty in mapping smaller nuclei deep in the brain, which can be addressed using ultra-high field 7 Tesla (T) MRI. The ventral tegmental area (VTA) is a subcortical structure that plays a pivotal role in reward processing, learning and memory. Despite the significant interest in this nucleus in cognitive neuroscience, there are currently no available, anatomically precise VTA atlases derived from 7 T MRI data that cover the full region of the VTA. Here, we first provide a protocol for multimodal VTA imaging and delineation. We then provide a data description of a probabilistic VTA atlas based on in vivo 7 T MRI data.

scholarly journals Mapping the common and distinct neural correlates of visual, rule and motor conflict

2020 ◽  
Author(s):  
Bryony Goulding Mew ◽  
Darije Custovic ◽  
Eyal Soreq ◽  
Romy Lorenz ◽  
Ines Violante ◽  
...  
Keyword(s):  
Region Of Interest ◽  
Neural Correlates ◽  
Brain Regions ◽  
Control Mechanisms ◽  
Activation Patterns ◽  
The Common ◽  
Full Factorial ◽  
The Brain ◽  
Categorical Scale ◽  
Conflict Types

AbstractFlexible behaviour requires cognitive-control mechanisms to efficiently resolve conflict between competing information and alternative actions. Whether a global neural resource mediates all forms of conflict or this is achieved within domainspecific systems remains debated. We use a novel fMRI paradigm to orthogonally manipulate rule, response and stimulus-based conflict within a full-factorial design. Whole-brain voxelwise analyses show that activation patterns associated with these conflict types are distinct but partially overlapping within Multiple Demand Cortex (MDC), the brain regions that are most commonly active during cognitive tasks. Region of interest analysis shows that most MDC sub-regions are activated for all conflict types, but to significantly varying levels. We propose that conflict resolution is an emergent property of distributed brain networks, the functional-anatomical components of which place on a continuous, not categorical, scale from domain-specialised to domain general. MDC brain regions place towards one end of that scale but display considerable functional heterogeneity.

scholarly journals Alterations of the Brain Microstructure and Corresponding Functional Connectivity in Early-Blind Adolescents

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Zhifeng Zhou ◽  
Jinping Xu ◽  
Leilei Shi ◽  
Xia Liu ◽  
Fen Hou ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Diffusion Tensor ◽  
Mean Diffusivity ◽  
Region Of Interest ◽  
Visual Deprivation ◽  
Light Perception ◽  
Resting State Functional Connectivity ◽  
Structural Reorganization ◽  
Brain Microstructure ◽  
The Brain

Although evidence from studies on blind adults indicates that visual deprivation early in life leads to structural and functional disruption and reorganization of the brain, whether young blind people show similar patterns remains unknown. Therefore, this study is aimed at exploring the structural and functional alterations of the brain of early-blind adolescents (EBAs) compared to normal-sighted controls (NSCs) and investigating the effects of residual light perception on brain microstructure and function in EBAs. We obtained magnetic resonance imaging (MRI) data from 23 EBAs (8 with residual light perception (LPs), 15 without light perception (NLPs)) and 21 NSCs (age range 11-19 years old). Whole-brain voxel-based analyses of diffusion tensor imaging metrics and region-of-interest analyses of resting-state functional connectivity (RSFC) were performed to compare patterns of brain microstructure and the corresponding RSFC between the groups. The results showed that structural disruptions of LPs and NLPs were mainly located in the occipital visual pathway. Compared with NLPs, LPs showed increased fractional anisotropy (FA) in the superior frontal gyrus and reduced diffusivity in the caudate nucleus. Moreover, the correlations between FA of the occipital cortices or mean diffusivity of the lingual gyrus and age were consistent with the development trajectory of the brain in NSCs, but inconsistent or even opposite in EBAs. Additionally, we found functional, but not structural, reorganization in NLPs compared with NSCs, suggesting that functional neuroplasticity occurs earlier than structural neuroplasticity in EBAs. Altogether, these findings provided new insights into the mechanisms underlying the neural reorganization of the brain in adolescents with early visual deprivation.

scholarly journals New optical method for analyzing cortical blood flow heterogeneity in small animals: validation of the method

2000 ◽  
Vol 279 (3) ◽  
pp. H1291-H1298 ◽  
Author(s):  
Istvan Schiszler ◽  
Minoru Tomita ◽  
Yasuo Fukuuchi ◽  
Norio Tanahashi ◽  
Koji Inoue
Keyword(s):  
Blood Flow ◽  
Mean Transit Time ◽  
Region Of Interest ◽  
Brain Parenchyma ◽  
Sprague Dawley ◽  
Doppler Flowmetry ◽  
Cranial Window ◽  
Brain Surface ◽  
Fine Glass ◽  
The Brain

In pentobarbital-anesthetized male Sprague-Dawley rats, a small cranial window was trephined, and the cortex was transilluminated with a fine glass fiber inserted into the brain parenchyma. The light intensity at the surface area of 2 × 2 mm was recorded during intracarotid injection of 25 μl of carbon black (CB) solution. The region of interest (ROI) was divided into a 50 × 50 matrix, and the mean transit time of CB transport was calculated in each matrix element. We found rapid transits of CB along the microvasculature, with considerable heterogeneity in the avascular area, and heterogeneous efficiency in autoregulatory capacity in the ROI during hypotension. The method was validated by comparison with laser-Doppler flowmetry. The average mean difference was 0.03 ± 0.05%. Five percent CO2 inhalation increased the flow by 85%, but heterogeneously. We concluded that the technique is exclusively sensitive to indicator transits in a very small area on the brain surface with potential usefulness in detecting regional heterogeneity in blood flow.

scholarly journals Transcranial alternating current stimulation attenuates BOLD adaptation and increases functional connectivity

2020 ◽  
Vol 123 (1) ◽  
pp. 428-438 ◽  
Author(s):  
Kohitij Kar ◽  
Takuya Ito ◽  
Michael W. Cole ◽  
Bart Krekelberg
Keyword(s):  
Functional Connectivity ◽  
Electric Fields ◽  
Full Range ◽  
Region Of Interest ◽  
Alternating Current ◽  
Human Motion ◽  
Dependent Manner ◽  
Bold Imaging ◽  
Transcranial Alternating Current Stimulation ◽  
The Brain

Transcranial alternating current stimulation (tACS) is used as a noninvasive tool for cognitive enhancement and clinical applications. The physiological effects of tACS, however, are complex and poorly understood. Most studies of tACS focus on its ability to entrain brain oscillations, but our behavioral results in humans and extracellular recordings in nonhuman primates support the view that tACS at 10 Hz also affects brain function by reducing sensory adaptation. Our primary goal in the present study is to test this hypothesis using blood oxygen level-dependent (BOLD) imaging in human subjects. Using concurrent functional magnetic resonance imaging (fMRI) and tACS, and a motion adaptation paradigm developed to quantify BOLD adaptation, we show that tACS significantly attenuates adaptation in the human motion area (hMT+). In addition, an exploratory analysis shows that tACS increases functional connectivity of the stimulated hMT+ with the rest of the brain and the dorsal attention network in particular. Based on field estimates from individualized head models, we relate these changes to the strength of tACS-induced electric fields. Specifically, we report that functional connectivity (between hMT+ and any other region of interest) increases in proportion to the field strength in the region of interest. These findings add support for the claim that weak 10-Hz currents applied to the scalp modulate both local and global measures of brain activity. NEW & NOTEWORTHY Concurrent transcranial alternating current stimulation (tACS) and functional MRI show that tACS affects the human brain by attenuating adaptation and increasing functional connectivity in a dose-dependent manner. This work is important for our basic understanding of what tACS does, but also for therapeutic applications, which need insight into the full range of ways in which tACS affects the brain.

scholarly journals Independent transmission of convergent visceral primary afferents in the solitary tract nucleus

2013 ◽  
Vol 109 (2) ◽  
pp. 507-517 ◽  
Author(s):  
Stuart J. McDougall ◽  
Michael C. Andresen
Keyword(s):  
Potential Interaction ◽  
Primary Afferents ◽  
Second Order ◽  
Solitary Tract Nucleus ◽  
Solitary Tract ◽  
Multiple Contacts ◽  
Large Numbers ◽  
Response Profiles ◽  
Active Release ◽  
The Brain

Cranial primary afferents from the viscera enter the brain at the solitary tract nucleus (NTS), where their information is integrated for homeostatic reflexes. The organization of sensory inputs is poorly understood, despite its critical impact on overall reflex performance characteristics. Single afferents from the solitary tract (ST) branch within NTS and make multiple contacts onto individual neurons. Many neurons receive more than one ST input. To assess the potential interaction between converging afferents and proximal branching near to second-order neurons, we probed near the recorded soma in horizontal slices from rats with focal electrodes and minimal shocks. Remote ST shocks evoked monosynaptic excitatory postsynaptic currents (EPSCs), and nearby focal shocks also activated monosynaptic EPSCs. We tested the timing and order of stimulation to determine whether focal shocks influenced ST responses and vice versa in single neurons. Focal-evoked EPSC response profiles closely resembled ST-EPSC characteristics. Mean synaptic jitters, failure rates, depression, and phenotypic segregation by capsaicin responsiveness were indistinguishable between focal and ST-evoked EPSCs. ST-EPSCs failed to affect focal-EPSCs within neurons, indicating that release sites and synaptic terminals were functionally independent and isolated from cross talk or neurotransmitter overflow. In only one instance, focal shocks intercepted and depleted the ST axon generating evoked EPSCs. Despite large numbers of functional contacts, multiple afferents do not appear to interact, and ST axon branches may be limited to close to the soma. Thus single or multiple primary afferents and their presynaptic active release sites act independently when they contact single second-order NTS neurons.

scholarly journals Image processing for early detection of Alzheimer’s disease using Iridology

2020 ◽  
Vol 11 (4) ◽  
pp. 5555-5559
Author(s):  
Asuntha A ◽  
Sai Kalyan Reddy R ◽  
Vamshikrishna K ◽  
Premsagar N
Keyword(s):  
Alzheimer’S Disease ◽  
Image Processing ◽  
Alzheimer's Disease ◽  
Memory Loss ◽  
Region Of Interest ◽  
Colour Pattern ◽  
Training Methods ◽  
Iris Image ◽  
Alternative Research ◽  
The Brain

Alzheimer's disease is caused by genetics, personal lifestyle and other environmental factors. It is an irreversible disease that slowly destroys the brain memory cells. There are no specific methods for the detection of Alzheimer's disease. The primary symptoms of Alzheimer's disease are memory loss, difficulty in thinking, a problem in writing and speaking and others. Iridology is alternative research that has gained more popularity in recent years, which studies the alterations of the iris in correspondence with the organs of the human body. The combination of digital image processing with Iridology gives an excellent opportunity to explore and learn about different neuronal diseases, specifically Alzheimer's disease. In this work, MATLAB software is applied to determine the colour, pattern and other factors that show the existence of Alzheimer's disease. The noise in the iris image is removed by the Gaussian filter, followed by histogram analyses and cropping. The Hough circle transform is used to identify the region of interest and to convert the circular iris image into rectangle form. In the training methods, the SVM and CNN classifiers are used to classify whether the person has Alzheimer's disease. Finally, the results are compared with the real-time images.

scholarly journals Brain2Pix: Fully convolutional naturalistic video reconstruction from brain activity

2021 ◽  
Author(s):  
Lynn Le ◽  
Luca Ambrogioni ◽  
Katja Seeliger ◽  
Yağmur Güçlütürk ◽  
Marcel van Gerven ◽  
...  
Keyword(s):  
Visual Field ◽  
Spatial Information ◽  
Brain Activity ◽  
Region Of Interest ◽  
Magnetic Resonance Data ◽  
Recent Success ◽  
Machine Learning Applications ◽  
Video Reconstruction ◽  
The Brain ◽  
Image Network

AbstractReconstructing complex and dynamic visual perception from brain activity remains a major challenge in machine learning applications to neuroscience. Here we present a new method for reconstructing naturalistic images and videos from very large single-participant functional magnetic resonance data that leverages the recent success of image-to-image transformation networks. This is achieved by exploiting spatial information obtained from retinotopic mappings across the visual system. More specifically, we first determine what position each voxel in a particular region of interest would represent in the visual field based on its corresponding receptive field location. Then, the 2D image representation of the brain activity on the visual field is passed to a fully convolutional image-to-image network trained to recover the original stimuli using VGG feature loss with an adversarial regularizer. In our experiments, we show that our method offers a significant improvement over existing video reconstruction techniques.

scholarly journals FiberNET: An ensemble deep learning framework for clustering white matter fibers

2017 ◽  
Author(s):  
Vikash Gupta ◽  
Sophia I. Thomopoulos ◽  
Faisal M. Rashid ◽  
Paul M. Thompson
Keyword(s):  
Deep Learning ◽  
White Matter ◽  
Structural Integrity ◽  
Region Of Interest ◽  
Fiber Bundles ◽  
Anatomical Connectivity ◽  
Learning Framework ◽  
Fiber Clustering ◽  
Learning Techniques ◽  
The Brain

AbstractWhite matter tracts are commonly analyzed in studies of micro-structural integrity and anatomical connectivity in the brain. Over the last decade, it has been an open problem as to how best to cluster white matter fibers, extracted from whole-brain tractography, into anatomically meaningful groups. Some existing techniques use region of interest (ROI) based clustering, atlas-based labeling, or unsupervised spectral clustering. ROI-based clustering is popular for analyzing anatomical connectivity among a set of ROIs, but it does not always partition the brain into recognizable fiber bundles. Here we propose an approach using convolutional neural networks (CNNs) to learn shape features of the fiber bundles, which are then exploited to cluster white matter fibers. To achieve such clustering, we first need to re-parameterize the fibers in an intrinsic space. The clustering is performed in induced parameterized coordinates. To our knowledge, this is one of the first approaches for fiber clustering using deep learning techniques. The results show strong accuracy - on a par with or better than other state-of-the-art methods.

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