scholarly journals A novel dynamic network imaging analysis method reveals aging-related fragmentation of cortical networks in mouse

2019 ◽  
Author(s):  
Daniel A Llano ◽  
Chihua Ma ◽  
Umberto Di Fabrizio ◽  
Aynaz Taheri ◽  
Kevin A. Stebbings ◽  
...  
Keyword(s):  
Auditory Cortex ◽  
Large Scale ◽  
Brain Slices ◽  
Dynamic Network ◽  
Analytical Tool ◽  
Aging Brain ◽  
Autofluorescence Imaging ◽  
Imaging Data ◽  
Cortical Networks ◽  
Network Metrics

AbstractNetwork analysis of large-scale neuroimaging data has proven to be a particularly challenging computational problem. In this study, we adapt a novel analytical tool, known as the community dynamic inference method (CommDy), which was inspired by social network theory, for the study of brain imaging data from an aging mouse model. CommDy has been successfully used in other domains in biology; this report represents its first use in neuroscience. We used CommDy to investigate aging-related changes in network parameters in the auditory and motor cortices using flavoprotein autofluorescence imaging in brain slices and in vivo. Analysis of spontaneous activations in the auditory cortex of slices taken from young and aged animals demonstrated that cortical networks in aged brains were highly fragmented compared to networks observed in young animals. Specifically, the degree of connectivity of each activated node in the aged brains was significantly lower than those seen in the young brain, and multivariate analyses of all derived network metrics showed distinct clusters of these metrics in young vs. aged brains. CommDy network metrics were then used to build a random-forests classifier based on NMDA-receptor blockade data, which successfully recapitulated the aging findings, suggesting that the excitatory synaptic substructure of the auditory cortex may be altered during aging. A similar aging-related decline in network connectivity was also observed in spontaneous activity obtained from the awake motor cortex, suggesting that the findings in the auditory cortex are reflections of general mechanisms that occur during aging. Therefore, CommDy therefore provides a new dynamic network analytical tool to study the brain and provides links between network-level and synaptic-level dysfunction in the aging brain.

scholarly journals A novel dynamic network imaging analysis method reveals aging-related fragmentation of cortical networks in mouse

2021 ◽  
pp. 1-28
Author(s):  
Daniel A Llano ◽  
Chihua Ma ◽  
Umberto Di Fabrizio ◽  
Aynaz Taheri ◽  
Kevin A. Stebbings ◽  
...  
Keyword(s):  
Large Scale ◽  
Brain Slices ◽  
Dynamic Network ◽  
Network Connectivity ◽  
Analytical Tool ◽  
Autofluorescence Imaging ◽  
Imaging Data ◽  
Cortical Networks ◽  
Network Metrics

Abstract Network analysis of large-scale neuroimaging data is a particularly challenging computational problem. Here, we adapt a novel analytical tool, the community dynamic inference method (CommDy), for brain imaging data from young and aged mice. CommDy, which was inspired by social network theory, has been successfully used in other domains in biology; this report represents its first use in neuroscience. We used CommDy to investigate aging-related changes in network metrics in the auditory and motor cortices using flavoprotein autofluorescence imaging in brain slices and in vivo. We observed that auditory cortical networks in slices taken from aged brains were highly fragmented compared to networks observed in young animals. CommDy network metrics were then used to build a random-forests classifier based on NMDA-receptor blockade data, which successfully reproduced the aging findings, suggesting that the excitatory cortical connections may be altered during aging. A similar aging-related decline in network connectivity was also observed in spontaneous activity in the awake motor cortex, suggesting that the findings in the auditory cortex reflect general mechanisms during aging. These data suggest that CommDy provides a new dynamic network analytical tool to study the brain and that aging is associated with fragmentation of intracortical networks.

scholarly journals Responses of Auditory Cortex to Complex Stimuli: Functional Organization Revealed Using Intrinsic Optical Signals

2008 ◽  
Vol 99 (4) ◽  
pp. 1928-1941 ◽  
Author(s):  
Israel Nelken ◽  
Jennifer K. Bizley ◽  
Fernando R. Nodal ◽  
Bashir Ahmed ◽  
Andrew J. King ◽  
...  
Keyword(s):  
Auditory Cortex ◽  
Optical Imaging ◽  
Large Scale ◽  
Functional Organization ◽  
Broadband Noise ◽  
Spectral Structure ◽  
Imaging Data ◽  
Complex Sounds ◽  
Optical Signals ◽  
Interaural Level Differences

We used optical imaging of intrinsic signals to study the large-scale organization of ferret auditory cortex in response to complex sounds. Cortical responses were collected during continuous stimulation by sequences of sounds with varying frequency, period, or interaural level differences. We used a set of stimuli that differ in spectral structure, but have the same periodicity and therefore evoke the same pitch percept (click trains, sinusoidally amplitude modulated tones, and iterated ripple noise). These stimuli failed to reveal a consistent periodotopic map across the auditory fields imaged. Rather, gradients of period sensitivity differed for the different types of periodic stimuli. Binaural interactions were studied both with single contralateral, ipsilateral, and diotic broadband noise bursts and with sequences of broadband noise bursts with varying level presented contralaterally, ipsilaterally, or in opposite phase to both ears. Contralateral responses were generally largest and ipsilateral responses were smallest when using single noise bursts, but the extent of the activated area was large and comparable in all three aural configurations. Modulating the amplitude in counter phase to the two ears generally produced weaker modulation of the optical signals than the modulation produced by the monaural stimuli. These results suggest that binaural interactions seen in cortex are most likely predominantly due to subcortical processing. Thus our optical imaging data do not support the theory that the primary or nonprimary cortical fields imaged are topographically organized to form consistent maps of systematically varying sensitivity either to stimulus pitch or to simple binaural properties of the acoustic stimuli.

scholarly journals Fractional ridge regression: a fast, interpretable reparameterization of ridge regression

GigaScience ◽  
2020 ◽  
Vol 9 (12) ◽  
Author(s):  
Ariel Rokem ◽  
Kendrick Kay
Keyword(s):  
Ridge Regression ◽  
Large Scale ◽  
Imaging Data ◽  
Regularization Technique ◽  
Large Scale Data ◽  
Novel Approach ◽  
Manual Exploration ◽  
L2 Norm ◽  
Software Implementations ◽  
Brain Imaging Data

Abstract Background Ridge regression is a regularization technique that penalizes the L2-norm of the coefficients in linear regression. One of the challenges of using ridge regression is the need to set a hyperparameter (α) that controls the amount of regularization. Cross-validation is typically used to select the best α from a set of candidates. However, efficient and appropriate selection of α can be challenging. This becomes prohibitive when large amounts of data are analyzed. Because the selected α depends on the scale of the data and correlations across predictors, it is also not straightforwardly interpretable. Results The present work addresses these challenges through a novel approach to ridge regression. We propose to reparameterize ridge regression in terms of the ratio γ between the L2-norms of the regularized and unregularized coefficients. We provide an algorithm that efficiently implements this approach, called fractional ridge regression, as well as open-source software implementations in Python and matlab (https://github.com/nrdg/fracridge). We show that the proposed method is fast and scalable for large-scale data problems. In brain imaging data, we demonstrate that this approach delivers results that are straightforward to interpret and compare across models and datasets. Conclusion Fractional ridge regression has several benefits: the solutions obtained for different γ are guaranteed to vary, guarding against wasted calculations; and automatically span the relevant range of regularization, avoiding the need for arduous manual exploration. These properties make fractional ridge regression particularly suitable for analysis of large complex datasets.

Wireless parathyroid detection device using autofluorescence and smart glasses: A preliminary study

2021 ◽  
pp. 1-9
Author(s):  
Chang Hwan Ryu ◽  
Jungirl Seok ◽  
Yu Lim Choi ◽  
Seok-ki Kim ◽  
Yuh-Seog Jung ◽  
...  
Keyword(s):  
Prospective Study ◽  
Large Scale ◽  
Video Recording ◽  
Controlled Study ◽  
Autofluorescence Imaging ◽  
Agreement Rate ◽  
Smart Glasses ◽  
Practical Effect ◽  
Preliminary Study ◽  
Positive Agreement

BACKGROUND: Autofluorescence imaging technology has been utilized for preserving or identifying parathyroid glands (PTGs) during thyroid surgery. We developed a wireless PTGs detection device linked with smart glasses that allows for real-time video recording and screen switching according to the light source. OBJECTIVE: This study aimed to confirm the feasibility of the device and whether it would help preserve the PTG during the surgery. METHODS: This prospective study was conducted in 30 patients with 66 PTGs. The device’s agreement with the physician’s judgment was evaluated, and we determined how many PTGs were preserved from thyroidectomy. RESULTS: The positive agreement rate for PTGs detection between the surgeon and device was 70.9%. Inadvertent parathyroidectomy was identified in surgical specimens of 6 patients (20%). No PTG was removed when it was confirmed by the device (0/39). Of the 27 glands not detected by the device, there was inadvertent removal of 6 PTGs. CONCLUSIONS: PTGs can be preserved successfully when the detection of them by the device is consistent with the surgeon’s discretion. A large-scale controlled study is necessary to demonstrate the practical effect of this device on hypoparathyroidism after thyroidectomy.

Interferometric imaging condition for wave-equation migration

Geophysics ◽  
2008 ◽  
Vol 73 (2) ◽  
pp. S47-S61 ◽  
Author(s):  
Paul Sava ◽  
Oleg Poliannikov
Keyword(s):  
Large Scale ◽  
Wigner Distribution ◽  
Random Noise ◽  
Velocity Model ◽  
Distribution Functions ◽  
Small Scale ◽  
Interferometric Imaging ◽  
Imaging Data ◽  
Imaging Condition ◽  
Velocity Models

The fidelity of depth seismic imaging depends on the accuracy of the velocity models used for wavefield reconstruction. Models can be decomposed in two components, corresponding to large-scale and small-scale variations. In practice, the large-scale velocity model component can be estimated with high accuracy using repeated migration/tomography cycles, but the small-scale component cannot. When the earth has significant small-scale velocity components, wavefield reconstruction does not completely describe the recorded data, and migrated images are perturbed by artifacts. There are two possible ways to address this problem: (1) improve wavefield reconstruction by estimating more accurate velocity models and image using conventional techniques (e.g., wavefield crosscorrelation) or (2) reconstruct wavefields with conventional methods using the known background velocity model but improve the imaging condition to alleviate the artifacts caused by the imprecise reconstruction. Wedescribe the unknown component of the velocity model as a random function with local spatial correlations. Imaging data perturbed by such random variations is characterized by statistical instability, i.e., various wavefield components image at wrong locations that depend on the actual realization of the random model. Statistical stability can be achieved by preprocessing the reconstructed wavefields prior to the imaging condition. We use Wigner distribution functions to attenuate the random noise present in the reconstructed wavefields, parameterized as a function of image coordinates. Wavefield filtering using Wigner distribution functions and conventional imaging can be lumped together into a new form of imaging condition that we call an interferometric imaging condition because of its similarity to concepts from recent work on interferometry. The interferometric imaging condition can be formulated both for zero-offset and for multioffset data, leading to robust, efficient imaging procedures that effectively attenuate imaging artifacts caused by unknown velocity models.

Imaging and Cognitive Genetics: The Norwegian Cognitive NeuroGenetics Sample

2012 ◽  
Vol 15 (3) ◽  
pp. 442-452 ◽  
Author(s):  
Thomas Espeseth ◽  
Andrea Christoforou ◽  
Astri J. Lundervold ◽  
Vidar M. Steen ◽  
Stephanie Le Hellard ◽  
...  
Keyword(s):  
Sample Size ◽  
Cognitive Aging ◽  
Brain Function ◽  
Large Scale ◽  
Adult Life ◽  
Aging Brain ◽  
Adult Life Span ◽  
Cross Sectional ◽  
A Genome ◽  
Effects Of Aging

Data collection for the Norwegian Cognitive NeuroGenetics sample (NCNG) was initiated in 2003 with a research grant (to Ivar Reinvang) to study cognitive aging, brain function, and genetic risk factors. The original focus was on the effects of aging (from middle age and up) and candidate genes (e.g., APOE, CHRNA4) in cross-sectional and longitudinal designs, with the cognitive and MRI-based data primarily being used for this purpose. However, as the main topic of the project broadened from cognitive aging to imaging and cognitive genetics more generally, the sample size, age range of the participants, and scope of available phenotypes and genotypes, have developed beyond the initial project. In 2009, a genome-wide association (GWA) study was undertaken, and the NCNG proper was established to study the genetics of cognitive and brain function more comprehensively. The NCNG is now controlled by the NCNG Study Group, which consists of the present authors. Prominent features of the NCNG are the adult life-span coverage of healthy participants with high-dimensional imaging, and cognitive data from a genetically homogenous sample. Another unique property is the large-scale (sample size 300–700) use of experimental cognitive tasks focusing on attention and working memory. The NCNG data is now used in numerous ongoing GWA-based studies and has contributed to several international consortia on imaging and cognitive genetics. The objective of the following presentation is to give other researchers the information necessary to evaluate possible contributions from the NCNG to various multi-sample data analyses.

A Novel Real-Time Thermal Analysis and Layer Time Control Framework for Large Scale Additive Manufacturing

2020 ◽  
Author(s):  
Sepehr Fathizadan ◽  
Feng Ju ◽  
Kyle Rowe ◽  
Alex Fiechter ◽  
Nils Hofmann
Keyword(s):  
Additive Manufacturing ◽  
Surface Temperature ◽  
Real Time ◽  
Large Scale ◽  
Production Efficiency ◽  
Control Method ◽  
Data Extraction ◽  
Critical Role ◽  
Imaging Data ◽  
Time Data

Abstract Production efficiency and product quality need to be addressed simultaneously to ensure the reliability of large scale additive manufacturing. Specifically, print surface temperature plays a critical role in determining the quality characteristics of the product. Moreover, heat transfer via conduction as a result of spatial correlation between locations on the surface of large and complex geometries necessitates the employment of more robust methodologies to extract and monitor the data. In this paper, we propose a framework for real-time data extraction from thermal images as well as a novel method for controlling layer time during the printing process. A FLIR™ thermal camera captures and stores the stream of images from the print surface temperature while the Thermwood Large Scale Additive Manufacturing (LSAM™) machine is printing components. A set of digital image processing tasks were performed to extract the thermal data. Separate regression models based on real-time thermal imaging data are built on each location on the surface to predict the associated temperatures. Subsequently, a control method is proposed to find the best time for printing the next layer given the predictions. Finally, several scenarios based on the cooling dynamics of surface structure were defined and analyzed, and the results were compared to the current fixed layer time policy. It was concluded that the proposed method can significantly increase the efficiency by reducing the overall printing time while preserving the quality.

Sign in / Sign up

Export Citation Format

Share Document