scholarly journals Estimating Brain Functional Networks Based on Adaptively-Weighted fMRI Signals for MCI Identification

2021 ◽  
Vol 12 ◽  
Author(s):  
Huihui Chen ◽  
Yining Zhang ◽  
Limei Zhang ◽  
Lishan Qiao ◽  
Dinggang Shen
Keyword(s):  
Time Series ◽  
Statistical Power ◽  
Estimation Model ◽  
Learning Framework ◽  
New Learning ◽  
Flexible Framework ◽  
Fmri Time Series ◽  
The Brain ◽  
Normal Controls

Brain functional network (BFN) analysis is becoming a crucial way to explore the inherent organized pattern of the brain and reveal potential biomarkers for diagnosing neurological or psychological disorders. In so doing, a well-estimated BFN is of great concern. In practice, however, noises or artifacts involved in the observed data (i.e., fMRI time series in this paper) generally lead to a poor estimation of BFN, and thus a complex preprocessing pipeline is often used to improve the quality of the data prior to BFN estimation. One of the popular preprocessing steps is data-scrubbing that aims at removing “bad” volumes from the fMRI time series according to the amplitude of the head motion. Despite its helpfulness in general, this traditional scrubbing scheme cannot guarantee that the removed volumes are necessarily unhelpful, since such a step is fully independent to the subsequent BFN estimation task. Moreover, the removal of volumes would reduce the statistical power, and different numbers of volumes are generally scrubbed for different subjects, resulting in an inconsistency or bias in the estimated BFNs. To address these issues, we develop a new learning framework that conducts BFN estimation and data-scrubbing simultaneously by an alternating optimization algorithm. The newly developed algorithm adaptively weights volumes (instead of removing them directly) for the task of BFN estimation. As a result, the proposed method can not only reduce the difficulty of threshold selection involved in the traditional scrubbing scheme, but also provide a more flexible framework that scrubs the data in the subsequent FBN estimation model. Finally, we validate the proposed method by identifying subjects with mild cognitive impairment (MCI) from normal controls based on the estimated BFNs, achieving an 80.22% classification accuracy, which significantly improves the baseline methods.

Simulation of sports movement training based on machine learning and brain-computer interface

2020 ◽  
pp. 1-12
Author(s):  
Linuo Wang
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Joint Learning ◽  
Scientific Methods ◽  
Learning Framework ◽  
Brain Functions ◽  
Movement Training ◽  
Practical Effect ◽  
Machine Interface ◽  
The Brain

Injuries and hidden dangers in training have a greater impact on athletes ’careers. In particular, the brain function that controls the motor function area has a greater impact on the athlete ’s competitive ability. Based on this, it is necessary to adopt scientific methods to recognize brain functions. In this paper, we study the structure of motor brain-computer and improve it based on traditional methods. Moreover, supported by machine learning and SVM technology, this study uses a DSP filter to convert the preprocessed EEG signal X into a time series, and adjusts the distance between the time series to classify the data. In order to solve the inconsistency of DSP algorithms, a multi-layer joint learning framework based on logistic regression model is proposed, and a brain-machine interface system of sports based on machine learning and SVM is constructed. In addition, this study designed a control experiment to improve the performance of the method proposed by this study. The research results show that the method in this paper has a certain practical effect and can be applied to sports.

scholarly journals Slow Cortical Waves via Cyclicity

2021 ◽  
Author(s):  
Ivan Abraham ◽  
Bahar Shahsavarani ◽  
Ben Zimmerman ◽  
Fatima Husain ◽  
yuliy baryshnikov
Keyword(s):  
Time Series ◽  
Dynamic Structure ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Fine Grained ◽  
Human Connectome Project ◽  
Fmri Time Series ◽  
The Brain ◽  
Cortical Waves ◽  
Temporal Ordering

Fine-grained information about dynamic structure of cortical networks is crucial in unpacking brain function. Here,we introduced a novel analytical method to characterize the dynamic interaction between distant brain regions,based on cyclicity analysis, and applied it to data from the Human Connectome Project. Resting-state fMRI time series are aperiodic and, hence, lack a base frequency. Cyclicity analysis, which is time-reparametrization invariant, is effective in recovering dynamic temporal ordering of such time series along a circular trajectory without assuming any time scale. Our analysis detected the propagation of slow cortical waves across thebrain with consistent shifts in lead-lag relationships between specific brain regions. We also observed short bursts of strong temporal ordering that dominated overall lead-lag relationships between pairs of regions in the brain, which were modulated by tasks. Our results suggest the possible role played by slow waves of ordered information between brain regions that underlie emergent cognitive function.

On Identifying Micro Level Error in Realignment Phase of Statistical Parametric Mapping

2007 ◽  
Vol 20 (5) ◽  
pp. 491-493 ◽  
Author(s):  
R. Rajesh ◽  
J. Satheeshkumar ◽  
S. Arumugaperumal ◽  
C. Kesavdas
Keyword(s):  
Time Series ◽  
Statistical Parametric Mapping ◽  
Cognitive Tasks ◽  
Parametric Mapping ◽  
Fmri Time Series ◽  
Micro Level ◽  
The Brain

Statistical parametric map of an fMRI time series is used to identify the sensor, motor and cognitive tasks in the specific regions of the brain. This process of obtaining statistical parametric map includes realignment of the slices in various volume acquired during the scanning. This article presents the identification of micro level (10−6) error in the realignment phase.

scholarly journals Study on Linear Combination of Long Memory Processes Corrupted by Additive Noises for fMRI Time Series Analysis

2017 ◽  
Author(s):  
Wonsang You ◽  
Catherine Limperopoulos
Keyword(s):  
Time Series ◽  
Neural Activity ◽  
Long Memory ◽  
Physiological Noise ◽  
Memory Processes ◽  
Fractal Properties ◽  
Long Memory Processes ◽  
Fmri Time Series ◽  
The Brain ◽  
Memory Parameter

AbstractEstimating the long memory parameter of the fMRI time series enables us to understand the fractal behavior of neural activity of the brain through fMRI time series. However, the existence of white noise and physiological noise compounds which also have fractal properties prevent us from making the estimation precise. As basic strategies to overcome noises, we address how to estimate the long memory parameter in the presence of additive noises, and how to estimate the long memory parameters of linearly combined long memory processes.

scholarly journals Bilinear dynamical systems

2005 ◽  
Vol 360 (1457) ◽  
pp. 983-993 ◽  
Author(s):  
W Penny ◽  
Z Ghahramani ◽  
K Friston
Keyword(s):  
Time Series ◽  
Dynamical Systems ◽  
Functional Integration ◽  
Model Parameters ◽  
Neuronal Responses ◽  
Linear Convolution ◽  
Neurodynamical Model ◽  
Convolution Kernels ◽  
Fmri Time Series ◽  
The Brain

In this paper, we propose the use of bilinear dynamical systems (BDS)s for model-based deconvolution of fMRI time-series. The importance of this work lies in being able to deconvolve haemodynamic time-series, in an informed way, to disclose the underlying neuronal activity. Being able to estimate neuronal responses in a particular brain region is fundamental for many models of functional integration and connectivity in the brain. BDSs comprise a stochastic bilinear neurodynamical model specified in discrete time, and a set of linear convolution kernels for the haemodynamics. We derive an expectation-maximization (EM) algorithm for parameter estimation, in which fMRI time-series are deconvolved in an E-step and model parameters are updated in an M-Step. We report preliminary results that focus on the assumed stochastic nature of the neurodynamic model and compare the method to Wiener deconvolution.

Classification of methods for generating pseudo-CT from MRI images for MRI-alone RT

2017 ◽  
Vol 1 (3) ◽  
pp. 54
Author(s):  
BOUKELLOUZ Wafa ◽  
MOUSSAOUI Abdelouahab
Keyword(s):  
Performance Metrics ◽  
Radiation Treatment ◽  
Hybrid Methods ◽  
Dose Calculation ◽  
Absolute Error ◽  
Ct Images ◽  
General Technique ◽  
Therapy Field ◽  
The Brain

Background: Since the last decades, research have been oriented towards an MRI-alone radiation treatment planning (RTP), where MRI is used as the primary modality for imaging, delineation and dose calculation by assigning to it the needed electron density (ED) information. The idea is to create a computed tomography (CT) image or so-called pseudo-CT from MRI data. In this paper, we review and classify methods for creating pseudo-CT images from MRI data. Each class of methods is explained and a group of works in the literature is presented in detail with statistical performance. We discuss the advantages, drawbacks and limitations of each class of methods. Methods: We classified most recent works in deriving a pseudo-CT from MR images into four classes: segmentation-based, intensity-based, atlas-based and hybrid methods. We based the classification on the general technique applied in the approach. Results: Most of research focused on the brain and the pelvis regions. The mean absolute error (MAE) ranged from 80 HU to 137 HU and from 36.4 HU to 74 HU for the brain and pelvis, respectively. In addition, an interest in the Dixon MR sequence is increasing since it has the advantage of producing multiple contrast images with a single acquisition. Conclusion: Radiation therapy field is emerging towards the generalization of MRI-only RT thanks to the advances in techniques for generation of pseudo-CT images. However, a benchmark is needed to set in common performance metrics to assess the quality of the generated pseudo-CT and judge on the efficiency of a certain method.

Neuro-Optometric Treatment of Complications from a Cerebellar Astrocytoma

2020 ◽  
pp. 304-312
Keyword(s):  
Visual System ◽  
Common Site ◽  
Us Army ◽  
Cranial Nerve Palsies ◽  
Pilocytic Astrocytomas ◽  
The Us ◽  
Extent Of Damage ◽  
Functional Components ◽  
The Brain

Background: Insult to the brain, whether from trauma or other etiologies, can have a devastating effect on an individual. Symptoms can be many and varied, depending on the location and extent of damage. This presentation can be a challenge to the optometrist charged with treating the sequelae of this event as multiple functional components of the visual system can be affected. Case Report: This paper describes the diagnosis and subsequent ophthalmic management of an acquired brain injury in a 22 year old male on active duty in the US Army. After developing acute neurological symptoms, the patient was diagnosed with a pilocytic astrocytoma of the cerebellum. Emergent neurosurgery to treat the neoplasm resulted in iatrogenic cranial nerve palsies and a hemispheric syndrome. Over the next 18 months, he was managed by a series of providers, including a strabismus surgeon, until presenting to our clinic. Lenses, prism, and in-office and out-of-office neurooptometric rehabilitation therapy were utilized to improve his functioning and make progress towards his goals. Conclusions: Pilocytic astrocytomas are the most common primary brain tumors, and the vast majority are benign with excellent surgical prognosis. Although the most common site is the cerebellum, the visual pathway is also frequently affected. If the eye or visual system is affected, optometrists have the ability to drastically improve quality of life with neuro-optometric rehabilitation.

PENGARUH PIJAT BAYI TERHADAP KUALITAS TIDUR BAYI UMUR 0 – 3 BULAN DI WILAYAH KERJA PUSKESMAS SOSIAL PALEMBANG TAHUN 2016

2019 ◽  
Vol 6 (2) ◽  
Author(s):  
Juliana Widyastuti Wahyuningsih Juliana Widyastuti Wahyuningsih
Keyword(s):  
Experimental Design ◽  
Growth And Development ◽  
Sampling Technique ◽  
P Value ◽  
Development Phase ◽  
Quality Of Sleep ◽  
Infant Massage ◽  
Before And After ◽  
The Brain

ABSTRAK Tidur merupakan kebutuhan yang harus terpenuhi terutama pada fase perkembangan karena selama tidur akan terjadi perkembangan otak maupun tubuh, sehingga gangguan tidur merupakan masalah yang akan menimbulkan dampak buruk terhadap pertumbuhan dan perkembangan bayi. Kualitas tidur bayi yang baik dapat diciptakan dengan memberikan pemijatan bayi secara rutin. Penelitian ini bertujuan untuk membuktikan bahwa pemijatan dapat mempengaruhi kualitas tidur bayi umur 0-3 bulan. Penelitian ini menggunakan desain penelitian Quasy Eksperimental dengan metode One Group Pretest-Postest. Sampel 22 bayi yang dipilih dengan tehnik Total Sampling yang di observasi sebelum dan sesudah diberikan pemijatan. Variabel yang diukur dalam penelitian ini adalah kualitas tidur bayi 0-3 bulan. Hasil penelitian menunjukkan bahwa ada pengaruh pijat bayi terhadap kualitas tidur bayi umur 0-3 bulan (p value  0,008 < α = 0,05).Berdasarkan hasil penelitian ini disarankan agar keluarga dan masyarakat memberikan pemijatan secara rutin dan mandiri untuk meningkatkan kebutuhan tidur bayi yang berkualitas.   ABSTRACT Sleep is a human necessity that must be met, especially in the development phase because during sleep will occur the brain and body developments, so that sleep disturbance is a problem that would cause adverse effects on infants’ growth and development. The good quality of sleep can be created by providing the infants massage routinely. This study aimed to prove that the massage could affect the quality of sleep on the 0-3 months old baby. This study used Quasy-experimental design with One Group Pretest-Posttest. The sample 22 infants selected by total sampling technique observed on before and after the massage. The variables measured in this study are the quality of sleep. The results of study indicate that there is an effect of infant massage to the sleep quality on 0-3 months old babies (p value 0,008 < α = 0,05).Based on the results of this study it recommended for the families and communities to provide infant massage regularly and independently to increase the quality of sleep on the baby.  

Integrated Biomarkers for Depression in Alzheimer’s Disease: A Critical Review

2017 ◽  
Vol 14 (4) ◽  
pp. 441-452 ◽  
Author(s):  
Sofia Wenzler ◽  
Christian Knochel ◽  
Ceylan Balaban ◽  
Dominik Kraft ◽  
Juliane Kopf ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Affect Regulation ◽  
Current Evidence ◽  
Diagnostic Process ◽  
Neuropsychiatric Manifestation ◽  
The Brain ◽  
Control Functional

Depression is a common neuropsychiatric manifestation among Alzheimer’s disease (AD) patients. It may compromise everyday activities and lead to a faster cognitive decline as well as worse quality of life. The identification of promising biomarkers may therefore help to timely initiate and improve the treatment of preclinical and clinical states of AD, and to improve the long-term functional outcome. In this narrative review, we report studies that investigated biomarkers for AD-related depression. Genetic findings state AD-related depression as a rather complex, multifactorial trait with relevant environmental and inherited contributors. However, one specific set of genes, the brain derived neurotrophic factor (BDNF), specifically the Val66Met polymorphism, may play a crucial role in AD-related depression. Regarding neuroimaging markers, the most promising findings reveal structural impairments in the cortico-subcortical networks that are related to affect regulation and reward / aversion control. Functional imaging studies reveal abnormalities in predominantly frontal and temporal regions. Furthermore, CSF based biomarkers are seen as potentially promising for the diagnostic process showing abnormalities in metabolic pathways that contribute to AD-related depression. However, there is a need for standardization of methodological issues and for replication of current evidence with larger cohorts and prospective studies.

scholarly journals Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks

2021 ◽  
Vol 11 (6) ◽  
pp. 2838
Author(s):  
Nikitha Johnsirani Venkatesan ◽  
Dong Ryeol Shin ◽  
Choon Sung Nam
Keyword(s):  
Neural Network ◽  
Radiation Dose ◽  
Convolutional Neural Network ◽  
Model Performance ◽  
Performance Comparison ◽  
Apache Spark ◽  
Training Time ◽  
Learning Framework ◽  
Proposed Model

In the pharmaceutical field, early detection of lung nodules is indispensable for increasing patient survival. We can enhance the quality of the medical images by intensifying the radiation dose. High radiation dose provokes cancer, which forces experts to use limited radiation. Using abrupt radiation generates noise in CT scans. We propose an optimal Convolutional Neural Network model in which Gaussian noise is removed for better classification and increased training accuracy. Experimental demonstration on the LUNA16 dataset of size 160 GB shows that our proposed method exhibit superior results. Classification accuracy, specificity, sensitivity, Precision, Recall, F1 measurement, and area under the ROC curve (AUC) of the model performance are taken as evaluation metrics. We conducted a performance comparison of our proposed model on numerous platforms, like Apache Spark, GPU, and CPU, to depreciate the training time without compromising the accuracy percentage. Our results show that Apache Spark, integrated with a deep learning framework, is suitable for parallel training computation with high accuracy.

Sign in / Sign up

Export Citation Format

Share Document