scholarly journals Topography of hippocampal connectivity with sensorimotor cortex revealed by optimizing smoothing kernel and voxel size

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260245
Author(s):  
Douglas D. Burman
Keyword(s):  
Sensorimotor Cortex ◽  
Longitudinal Axis ◽  
Processing Parameters ◽  
Cortical Activation ◽  
Connectivity Analysis ◽  
Optimal Parameters ◽  
Voxel Size ◽  
Optimal Processing ◽  
Smoothing Kernel ◽  
The Mean

Studies of the hippocampus use smaller voxel sizes and smoothing kernels than cortical activation studies, typically using a multivoxel seed with specified radius for connectivity analysis. This study identified optimal processing parameters for evaluating hippocampal connectivity with sensorimotor cortex (SMC), comparing effectiveness by varying parameters during both activation and connectivity analysis. Using both 3mm and 4mm isovoxels, smoothing kernels of 0-10mm were evaluated on the amplitude and extent of motor activation and hippocampal connectivity with SMC. Psychophysiological interactions (PPI) identified hippocampal connectivity with SMC during volitional movements, and connectivity effects from multivoxel seeds were compared with alternate methods; a structural seed represented the mean connectivity map from all voxels within a region, whereas a functional seed represented the regional voxel with maximal SMC connectivity. With few exceptions, the same parameters were optimal for activation and connectivity. Larger isovoxels showed larger activation volumes in both SMC and the hippocampus; connectivity volumes from structural seeds were also larger, except from the posterior hippocampus. Regardless of voxel size, the 10mm smoothing kernel generated larger activation and connectivity volumes from structural seeds, as well as larger beta estimates at connectivity maxima; structural seeds also produced larger connectivity volumes than multivoxel seeds. Functional seeds showed lesser effects from voxel size and smoothing kernels. Optimal parameters revealed topography in structural seed connectivity along both the longitudinal axis and mediolateral axis of the hippocampus. These results indicate larger voxels and smoothing kernels can improve sensitivity for detecting both cortical activation and hippocampal connectivity.

scholarly journals Topography of Hippocampal Connectivity with Sensorimotor Cortex Revealed by Optimizing Smoothing Kernel and Voxel Size

2020 ◽  
Author(s):  
Douglas D. Burman
Keyword(s):  
Sensorimotor Cortex ◽  
Longitudinal Axis ◽  
Processing Parameters ◽  
Voxel Size ◽  
Functional Connections ◽  
Optimal Analysis ◽  
Optimal Processing ◽  
Smoothing Kernel ◽  
The Mean ◽  
Task Conditions

AbstractEvaluating hippocampal connectivity across task conditions can be challenging, especially in the absence of activation to select the size and location of seeds; furthermore, optimal analysis parameters are unknown. Hippocampal task-specific connectivity with sensorimotor cortex was identified from psychophysiological interactions (PPI) during volitional movements; connectivity from a structural seed represented the mean of connectivity maps from all voxels in a hippocampal region, whereas connectivity from a functional seed represented maximal connectivity from an individual voxel. Motor activation and hippocampal connectivity were both evaluated across different processing parameters (3mm vs. 4mm isovoxels, smoothing kernels of 6-10mm). Topography in hippocampal connectivity was apparent along both its longitudinal axis and mediolateral axis, but only with optimal processing. Larger voxels improved signal-to-noise during individual and group analyses, lowering the statistical threshold and increasing the area of activation. The 10mm smoothing kernel was optimal for detecting connectivity from structural seeds, whereas the size of the smoothing kernel had little effect on connectivity from functional seeds. Unlike other approaches, structural seeds in this study allowed task-related connectivity to be examined throughout the hippocampus. By optimizing processing parameters, topography in hippocampal connectivity became demonstrable from both structural seeds and individual voxels. Larger voxels and smoothing kernels improved sensitivity for detecting activation and hippocampal connectivity, with the use of structural / functional seeds uncovering details about the topography of its functional connections.

scholarly journals Bayesian Optimisation for Neuroimaging Pre-processing in Brain Age Prediction

2017 ◽  
Author(s):  
Jenessa Lancaster ◽  
Romy Lorenz ◽  
Rob Leech ◽  
James H Cole
Keyword(s):  
Absolute Error ◽  
Processing Parameters ◽  
Training Dataset ◽  
Chronological Age ◽  
Support Vector ◽  
Voxel Size ◽  
Smoothing Kernel ◽  
And Performance ◽  
Brain Age ◽  
Age Prediction

AbstractNeuroimaging-based age predictions using machine learning have been shown to relate to cognitive performance, health outcomes and progression of neurodegenerative disease. However, even leading age-prediction algorithms contain measurement error, motivating efforts to improve experimental pipelines. T1-weighted MRI is commonly used for age prediction, and the pre-processing of these scans involves normalisation to a common template and resampling to a common voxel size, followed by spatial smoothing. Resampling parameters are often selected arbitrarily. Here, we sought to improve brain-age prediction accuracy by optimising resampling parameters using Bayesian optimisation.Using data on N=2001 healthy individuals (aged 16-90 years) we trained support vector machines to i) distinguish between young (<50 years) and old (>50 years) brains and ii) predict chronological age, with accuracy assessed using cross-validation. We also evaluated model generalisability to the Cam-CAN dataset (N=648, aged 18-88 years). Bayesian optimisation was used to identify optimal voxel size and smoothing kernel size for each task. This procedure adaptively samples the parameter space to evaluate accuracy across a range of possible parameters, using independent sub-samples to iteratively assess different parameter combinations to arrive at optimal values.When distinguishing between young and old brains a classification accuracy of 96.25% was achieved, with voxel size = 11.5mm3 and smoothing kernel = 2.3mm. For predicting chronological age, a mean absolute error (MAE) of 5.08 years was achieved, with voxel size = 3.73mm3 and smoothing kernel = 3.68mm. This was compared to performance using default values of 1.5mm3 and 4mm respectively, which gave a MAE = 5.48 years, a 7.3% improvement. When assessing generalisability, best performance was achieved when applying the entire Bayesian optimisation framework to the new dataset, out-performing the parameters optimised for the initial training dataset.Our study demonstrates the proof-of-principle that neuroimaging models for brain age prediction can be improved by using Bayesian optimisation to select more appropriate pre-processing parameters. Our results suggest that different parameters are selected and performance improves when optimisation is conducted in specific contexts. This motivates use of optimisation techniques at many different points during the experimental process, which may result in improved statistical sensitivity and reduce opportunities for experimenter-led bias.

Creating hippocampal PPI connectivity maps with SPM12 batch files v1

2021 ◽  
Author(s):  
Douglas D. Burman
Keyword(s):  
Effective Connectivity ◽  
A Priori ◽  
Group Analysis ◽  
Region Of Interest ◽  
Connectivity Analysis ◽  
Voxel Size ◽  
Smoothing Kernel ◽  
Corrected Data ◽  
Data Files ◽  
And Control

Protocol for task-specific, effective connectivity analysis of hippocampus using SPM12 batch files and PPI analysis. This protocol requires input from an SPM analysis on smoothed, normalized, slice-time corrected data (files with swa prefix) using 4mm isovoxels, with the first contrast specifying effects of interest (an F-contrast, excluding any movement covariates but specifying every experimental and control condition). The batch files in this protocol generate connectivity maps from each voxel in both the left and right hippocampus, then generates averaged connectivity maps from specified regions of the hippocampus, eliminating the necessity to identify seed regions a priori from methods (such as activation) not directly related to connectivity analysis. This procedure can be repeated for as many as 30 subjects. The final step creates a random effects group analysis for each of 9 bilateral structural seeds, as described elsewhere (e.g., Burman, 2021 "Topography of hippocampal connectivity with sensorimotor cortex revealed by optimizing smoothing kernel and voxel size", doi: https://doi.org/10.1101/2020.05.14.096339); however, connectivity maps created by the protocol allow an investigator to run group analysis on any region of interest.

Context-dependent relationship in high-resolution micro-ECoG studies during finger movements

2020 ◽  
Vol 132 (5) ◽  
pp. 1358-1366
Author(s):  
Chao-Hung Kuo ◽  
Timothy M. Blakely ◽  
Jeremiah D. Wander ◽  
Devapratim Sarma ◽  
Jing Wu ◽  
...  
Keyword(s):  
High Resolution ◽  
Sensorimotor Cortex ◽  
Index Finger ◽  
Hand Movement ◽  
Cortical Activation ◽  
Hand Movements ◽  
Finger Movements ◽  
Thumb Movement ◽  
Neurosurgical Patients ◽  
The Relationship

OBJECTIVEThe activation of the sensorimotor cortex as measured by electrocorticographic (ECoG) signals has been correlated with contralateral hand movements in humans, as precisely as the level of individual digits. However, the relationship between individual and multiple synergistic finger movements and the neural signal as detected by ECoG has not been fully explored. The authors used intraoperative high-resolution micro-ECoG (µECoG) on the sensorimotor cortex to link neural signals to finger movements across several context-specific motor tasks.METHODSThree neurosurgical patients with cortical lesions over eloquent regions participated. During awake craniotomy, a sensorimotor cortex area of hand movement was localized by high-frequency responses measured by an 8 × 8 µECoG grid of 3-mm interelectrode spacing. Patients performed a flexion movement of the thumb or index finger, or a pinch movement of both, based on a visual cue. High-gamma (HG; 70–230 Hz) filtered µECoG was used to identify dominant electrodes associated with thumb and index movement. Hand movements were recorded by a dataglove simultaneously with µECoG recording.RESULTSIn all 3 patients, the electrodes controlling thumb and index finger movements were identifiable approximately 3–6-mm apart by the HG-filtered µECoG signal. For HG power of cortical activation measured with µECoG, the thumb and index signals in the pinch movement were similar to those observed during thumb-only and index-only movement, respectively (all p > 0.05). Index finger movements, measured by the dataglove joint angles, were similar in both the index-only and pinch movements (p > 0.05). However, despite similar activation across the conditions, markedly decreased thumb movement was observed in pinch relative to independent thumb-only movement (all p < 0.05).CONCLUSIONSHG-filtered µECoG signals effectively identify dominant regions associated with thumb and index finger movement. For pinch, the µECoG signal comprises a combination of the signals from individual thumb and index movements. However, while the relationship between the index finger joint angle and HG-filtered signal remains consistent between conditions, there is not a fixed relationship for thumb movement. Although the HG-filtered µECoG signal is similar in both thumb-only and pinch conditions, the actual thumb movement is markedly smaller in the pinch condition than in the thumb-only condition. This implies a nonlinear relationship between the cortical signal and the motor output for some, but importantly not all, movement types. This analysis provides insight into the tuning of the motor cortex toward specific types of motor behaviors.

scholarly journals Warpage Optimisation Using Recycled Polycar-bonates (PC) on Front Panel Housing

Materials ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1416
Author(s):  
Nur Aisyah Miza Ahmad Tamizi ◽  
Shayfull Zamree Abd Rahim ◽  
Abdellah El-hadj Abdellah ◽  
Mohd Mustafa Al Bakri Abdullah ◽  
Marcin Nabiałek ◽  
...  
Keyword(s):  
Simulation Analysis ◽  
Processing Parameters ◽  
Front Panel ◽  
Processing Parameter ◽  
Mechanical Recycling ◽  
Great Opportunity ◽  
Average Value ◽  
Optimal Processing ◽  
Materials Used ◽  
Optimisation Methods

Many studies have been done using recycled waste materials to minimise environmental problems. It is a great opportunity to explore mechanical recycling and the use of recycled and virgin blend as a material to produce new products with minimum defects. In this study, appropriate processing parameters were considered to mould the front panel housing part using R0% (virgin), R30% (30% virgin: 70% recycled), R40% (40% virgin: 60% recycled) and R50% (50% virgin: 50% recycled) of Polycarbonate (PC). The manufacturing ability and quality during preliminary stage can be predicted through simulation analysis using Autodesk Moldflow Insight 2012 software. The recommended processing parameters and values of warpage in x and y directions can also be obtained using this software. No value of warpage was obtained from simulation studies for x direction on the front panel housing. Therefore, this study only focused on reducing the warpage in the y direction. Response Surface Methodology (RSM) and Genetic Algorithm (GA) optimisation methods were used to find the optimal processing parameters. As the results, the optimal ratio of recycled PC material was found to be R30%, followed by R40% and R50% materials using RSM and GA methods as compared to the average value of warpage on the moulded part using R0%. The most influential processing parameter that contributed to warpage defect was packing pressure for all materials used in this study.

scholarly journals Binary Self-Assembly of Nanocolloidal Arrays using Concurrent and Sequential Spin Coating Techniques

Materials ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 274
Author(s):  
Shih-Jyun Shen ◽  
Demei Lee ◽  
Yu-Chen Wu ◽  
Shih-Jung Liu
Keyword(s):  
Silicon Substrate ◽  
Self Assembly ◽  
Spin Coating ◽  
Processing Parameters ◽  
The Self ◽  
Silicon Substrates ◽  
Processing Conditions ◽  
Polystyrene Spheres ◽  
Optimal Processing ◽  
Spin Coater

This paper reports the binary colloid assembly of nanospheres using spin coating techniques. Polystyrene spheres with sizes of 900 and 100 nm were assembled on top of silicon substrates utilizing a spin coater. Two different spin coating processes, namely concurrent and sequential coatings, were employed. For the concurrent spin coating, 900 and 100 nm colloidal nanospheres of latex were first mixed and then simultaneously spin coated onto the silicon substrate. On the other hand, the sequential coating process first created a monolayer of a 900 nm nanosphere array on the silicon substrate, followed by the spin coating of another layer of a 100 nm colloidal array on top of the 900 nm array. The influence of the processing parameters, including the type of surfactant, spin speed, and spin time, on the self-assembly of the binary colloidal array were explored. The empirical outcomes show that by employing the optimal processing conditions, binary colloidal arrays can be achieved by both the concurrent and sequential spin coating processes.

scholarly journals Multiscale Copper-µDiamond Nanostructured Composites

2012 ◽  
Vol 730-732 ◽  
pp. 925-930
Author(s):  
Daniela Nunes ◽  
Vanessa Livramento ◽  
Horácio Fernandes ◽  
Carlos Silva ◽  
Nobumitsu Shohoji ◽  
...  
Keyword(s):  
Pure Copper ◽  
Thermal Stabilization ◽  
Hot Extrusion ◽  
Processing Parameters ◽  
High Energy ◽  
X Ray Diffraction ◽  
X Ray ◽  
Optimal Processing ◽  
Novel Approach ◽  
Transmission Electron

Nanostructured copper-diamond composites can be tailored for thermal management applications at high temperature. A novel approach based on multiscale diamond dispersions is proposed for the production of this type of materials: a Cu-nDiamond composite produced by high-energy milling is used as a nanostructured matrix for further dispersion of micrometer sized diamond. The former offers strength and microstructural thermal stability while the latter provides high thermal conductivity. A series of Cu-nDiamond mixtures have been milled to define the minimum nanodiamond fraction suitable for matrix refinement and thermal stabilization. A refined matrix with homogenously dispersed nanoparticles could be obtained with 4 at.% nanodiamond for posterior mixture with mDiamond and subsequent consolidation. In order to define optimal processing parameters, consolidation by hot extrusion has been carried out for a Cu-nDiamond composite and, in parallel, for a mixture of pure copper and mDiamond. The materials produced were characterized by X-ray diffraction, scanning and transmission electron microscopy and microhardness measurements.

Determination of optimal microwave curing cycle for fly ash mortars

2008 ◽  
Vol 35 (4) ◽  
pp. 349-357 ◽  
Author(s):  
İlker Bekir Topçu ◽  
Mehmet Uğur Toprak ◽  
Devrim Akdağ
Keyword(s):  
Fly Ash ◽  
Processing Parameters ◽  
Microwave Energy ◽  
Strength Prediction ◽  
Strength Development ◽  
Microwave Curing ◽  
Optimal Processing ◽  
Cement Mortars ◽  
Curing Cycle

Microwave energy can accelerate the hydration of cement, which results in the rapid strength development of concrete. In this paper, prediction of later age compressive strength of fly ash cement mortars, based on the accelerated strength of mortars cured with microwave energy, was investigated. To accelerate curing properly, optimal processing parameters of microwave curing (MC) on Portland cement mortars (CM) and fly ash cement mortars (FA) were first determined and then were applied to mortars. The possible early ages for the strength prediction were found to be at 6 and 8 h for CM and FA, respectively. The error percentages for prediction of CM were ±2.22% and 2.91% for 7 and 28 d, respectively. Error percentages for FA, on the other hand, were ±4.36% and 5.20% for 7 and 28 d, respectively.

Low frequency vibratory cleaning of paint and rust contaminants from machines parts

2021 ◽  
pp. 095440542110314
Author(s):  
Dieudonne Essola ◽  
Achille Pandong Njomoue ◽  
Florence Offole ◽  
Cyrille Adiang Mezoue ◽  
Crick Nelson Zanga ◽  
...  
Keyword(s):  
Processing Time ◽  
Low Frequency ◽  
Processing Parameters ◽  
Vibration Machine ◽  
Optimal Processing ◽  
Low Frequency Oscillations ◽  
Experimental Parameters ◽  
Process Fluid ◽  
Full Factorial ◽  
Fluid Parameters

This work investigates the effect of low frequency vibratory processing for cleaning and washing various machine components parts from rusts and old paints deposits. The experimental investigation was carried out with special prepared samples that were weighted and exposed to paints and rust contaminants. These samples were treated in universal horizontal vibration machine UVHM 4 × 10 with different combination of instrumental processing medium, process fluid, machine amplitude and frequency of oscillations. They were periodically reweighted after processing and compared to etalon with control of quantity of dust that have been removed, sample cleanliness and also other functional parameters. Statistical analysis has been used to characterize ongoing process and full factorial analysis to establish experimental parameters dependency. The result is showing the complex dependence of samples cleanliness to each processing parameters like processing time, amplitude of oscillations, frequency of oscillations, process fluid parameters, instrumental medium, etc. Between this parameters although the most important successively the amplitude of oscillations, the frequency of oscillations the processing medium and the processing fluid depending to his considered composition, the optimal processing time can be reach only by complex combination of all this parameters every of them carry an amplify coefficient. Low frequency oscillations can be used to monitor and optimize washing and cleaning operations of paints and rusts contaminations. That guarantees process automation, its effectiveness for a large industrial application.

scholarly journals The Effect of Anterior Uterocervical Angle on Primary Dysmenorrhea and Disease Severity

2018 ◽  
Vol 2018 ◽  
pp. 1-5 ◽  
Author(s):  
Mefkure Eraslan Sahin ◽  
Erdem Sahin ◽  
Yusuf Madendag ◽  
Ilknur Col Madendag ◽  
Ahter Tanay Tayyar ◽  
...  
Keyword(s):  
Disease Severity ◽  
Severe Pain ◽  
Longitudinal Axis ◽  
Primary Dysmenorrhea ◽  
Uterine Corpus ◽  
Significant Family History ◽  
Significant Difference ◽  
History Of ◽  
Secondary Dysmenorrhea ◽  
The Mean

Background. Primary dysmenorrhea, defined as painful menstrual cramps originating in the uterus without underlying pathology, is a gynecological disease that affects quality of life and school success. Our goal was to determine the effect of anterior uterocervical angle on primary dysmenorrhea and disease severity. Methods. A total of 200 virgin adolescents, 16 to 20 years of age, were included in the study. The Andersch and Milsom scale was used to determine dysmenorrhea severity. Those with pathologies causing secondary dysmenorrhea were excluded from the study. Study subjects were grouped based on severity of pain. Demographic characteristics and uterocervical ultrasonographic measurements were compared among groups. Results. Of the 200 participants enrolled in the study, 50 were healthy controls and 150 had primary dysmenorrhea. Those with primary dysmenorrhea had a significant family history of primary dysmenorrhea compared with controls (P<0.001). Age (P=0.668), body mass index (P=0.898), menarche age (P=0.915), and length of menstrual cycles (P=0.740) were similar in all groups. The uterine corpus longitudinal axis, uterine corpus transverse axis, and uterine cervix longitudinal axis were also similar (P=0.359, P=0.279, and P=0.369, resp.). The mean uterocervical angle was 146.8 ± 6.0 in controls and 143.3 ± 7.3 in those with mild pain with no significant difference between the groups. In those with moderate pain, the mean uterocervical angle was 121.2 ± 7.3 compared with 101 ± 9.2 in those with severe pain, which was a significant difference. Additionally, there was also a significant difference in the uterocervical angle among those with mild, moderate, and severe pain (P<0.001). Conclusion. Our results indicate that a narrower anterior uterocervical angle is associated with primary dysmenorrhea and disease severity.

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