Neuroengineering Models of Brain Disease

Finkel, Leif H.

doi:10.1146/annurev.bioeng.2.1.577

Cited by 27 publications

(29 citation statements)

References 87 publications

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“…AD is a neurodegenerative condition in which nerve tissue in the brain breaks down which gradually reduces the ability to learn, think, and memorize [62]. Recent studies have established the relationship between oxidative stress-generated toxic aldehydes and possible causes of AD [63, 64].…”

Section: Neurodegenerative Diseasesmentioning

confidence: 99%

4-Hydroxynonenal in the Pathogenesis and Progression of Human Diseases

Mohammed

Ansari

Srivastava³

et al. 2013

CMC

155

116

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Metastable aldehydes produced by lipid peroxidation act as 'toxic second messengers' that extend the injurious potential of free radicals. 4-hydroxy 2-nonenal (HNE), a highly toxic and most abundant stable end product of lipid peroxidation, has been implicated in the tissue damage, dysfunction, injury associated with aging and other pathological states such as cancer, Alzheimer, diabetes, cardiovascular and inflammatory complications. Further, HNE has been considered as a oxidative stress marker and it act as a secondary signaling molecule to regulates a number of cell signaling pathways. Biological activity of HNE depends on its intracellular concentration, which can differentially modulate cell death, growth and differentiation. Therefore, the mechanisms responsible for maintaining the intracellular levels of HNE are most important, not only in the defense against oxidative stress but also in the pathophysiology of a number of disease processes. In this review, we discusse the significance of HNE in mediating various disease processes and how regulation of its metabolism could be therapeutically effective.

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Section: Neurodegenerative Diseasesmentioning

confidence: 99%

4-Hydroxynonenal in the Pathogenesis and Progression of Human Diseases

Mohammed

Ansari

Srivastava³

et al. 2013

CMC

155

116

View full text Add to dashboard Cite

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“…3,4,30 While anticonvulsive drug therapies can suppress a seizure through the chemical inhibition of channel transport mechanisms, 7 these drugs are unsuccessful in over 30% of patients. 18 For these medically intractable cases, surgery becomes the only viable alternative available. It requires the removal of a portion of the brain that is thought to be the origin of seizure generation.…”

Section: Introductionmentioning

confidence: 99%

Online Prediction of Onsets of Seizure-like Events in Hippocampal Neural Networks Using Wavelet Artificial Neural Networks

Chiu¹,

Kang

Derchansky

et al. 2006

Ann Biomed Eng

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It has been previously shown that wavelet artificial neural networks (WANNs) are able to classify the different states of epileptiform activity and predict the onsets of seizure-like events (SLEs) by offline processing (Ann. Biomed. Eng. 33(6):798-810, 2005) of the electrical data from the in-vitro hippocampal slice model of recurrent spontaneous SLEs. The WANN design entailed the assumption that time-varying frequency information from the biological recordings can be used to estimate the times at which onsets of SLEs would most likely occur in the future. Progressions of different frequency components were captured by the artificial neural network (ANN) using selective frequency inputs from the initial wavelet transform of the biological data. The training of the WANN had been established using 184 SLE episodes in 34 slices from 21 rats offline. Nine of these rats also exhibited periods of interictal bursts (IBs). These IBs were included as part of the training to help distinguish the difference in dynamics of bursting activities between the preictal- and interictal type. In this paper, we present the results of an online processing using WANN on 23 in-vitro rat hippocampal slices from 9 rats having 93 spontaneous SLE episodes generated under low magnesium conditions. Over the test cases, three of the nine rats exhibited over 30 min of IB activities. We demonstrated that the WANN was able to classify the different states, namely, interictal, preictal, ictal, and IB activities with an accuracy of 86.6, 72.6, 84.5, and 69.1%, respectively. Prediction of state transitions into ictal events was achieved using regression of initial "normalized time-to-onset" estimates. The SLE onsets can be estimated up to 36.4 s ahead of their actual occurrences, with a mean error of 14.3 +/- 27.0 s. The prediction errors decreased progressively as the actual time-to-onset decreased and more initial "normalized time-to-onset" estimates were used for the regression procedure.

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“…5,3,29 While anticonvulsive drug therapies can serve to suppress a seizure through the chemical inhibition of channel transport mechanisms, 7 these drugs presently maintain a failure rate of over 30%. 17 For these medically intractable individuals, surgery becomes the only viable alternative available. It requires the removal of a portion of the brain that is thought to be the origin of seizure generation.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Seizure Onset in an In-Vitro Hippocampal Slice Model of Epilepsy Using Gaussian-Based and Wavelet-Based Artificial Neural Networks

Chiu¹,

Daniel²,

Khosravani

et al. 2005

Ann Biomed Eng

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We propose that artificial neural networks (ANNs) can be used to predict seizure onsets in an in-vitro hippocampal slice model capable of generating spontaneous seizure-like events (SLEs) in their extracellular field recordings. This paper assesses the effectiveness of two ANN prediction schemes: Gaussian-based artificial neural network (GANN) and wavelet-based artificial neural network (WANN). The GANN prediction system consists of a recurrent network having Gaussian radial basis function (RBF) nonlinearities capable of extracting the estimated manifold of the system. It is able to classify the underlying dynamics of spontaneous in-vitro activities into interictal, preictal and ictal modes. It is also able to successfully predict the onsets of SLEs as early as 60 s before. Improvements can be made to the overall seizure predictor design by incorporating time-varying frequency information. Consequently, the idea of WANN is considered. The WANN design entails the assumption that frequency variations in the extracellular field recordings can be used to compute the times at which onsets of SLEs are most likely to occur in the future. Progressions of different frequency components can be captured by the ANN using appropriate frequency band adjustments via pruning, after the initial wavelet transforms. In the off-line processing comprised of 102 spontaneous SLEs generated from 14 in-vitro rat hippocampal slices, with half of them used for training and the other half for testing, the WANN is able to predict the forecoming ictal onsets as early as 2 min prior to SLEs with over 75% accuracy within a 30 s precision window.

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Neuroengineering Models of Brain Disease

Cited by 27 publications

References 87 publications

4-Hydroxynonenal in the Pathogenesis and Progression of Human Diseases

4-Hydroxynonenal in the Pathogenesis and Progression of Human Diseases

Online Prediction of Onsets of Seizure-like Events in Hippocampal Neural Networks Using Wavelet Artificial Neural Networks

Prediction of Seizure Onset in an In-Vitro Hippocampal Slice Model of Epilepsy Using Gaussian-Based and Wavelet-Based Artificial Neural Networks

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