22 research outputs found
Assisted Diagnosis of Parkinsonism Based on the Striatal Morphology
Parkinsonism is a clinical syndrome characterized by the progressive loss of striatal dopamine. Its diagnosis
is usually corroborated by neuroimaging data such as DaTSCAN neuroimages that allow visualizing
the possible dopamine deficiency. During the last decade, a number of computer systems have been
proposed to automatically analyze DaTSCAN neuroimages, eliminating the subjectivity inherent to the
visual examination of the data. In this work, we propose a computer system based on machine learning
to separate Parkinsonian patients and control subjects using the size and shape of the striatal region,
modeled from DaTSCAN data. First, an algorithm based on adaptative thresholding is used to parcel
the striatum. This region is then divided into two according to the brain hemisphere division and characterized
with 152 measures, extracted from the volume and its three possible 2-dimensional projections.
Afterwards, the Bhattacharyya distance is used to discard the least discriminative measures and, finally,
the neuroimage category is estimated by means of a Support Vector Machine classifier. This method was
evaluated using a dataset with 189 DaTSCAN neuroimages, obtaining an accuracy rate over 94%. This
rate outperforms those obtained by previous approaches that use the intensity of each striatal voxel as
a feature.This work was supported by the MINECO/
FEDER under the TEC2015-64718-R project, the
Ministry of Economy, Innovation, Science and
Employment of the Junta de Andaluc´ıa under the
P11-TIC-7103 Excellence Project and the Vicerectorate
of Research and Knowledge Transfer of the
University of Granada
Empirical Functional PCA for 3D Image Feature Extraction Through Fractal Sampling.
Medical image classification is currently a challenging task that can be used to aid the diagnosis of different brain diseases. Thus, exploratory and discriminative analysis techniques aiming to obtain representative features from the images play a decisive role in the design of effective Computer Aided Diagnosis (CAD) systems, which is especially important in the early diagnosis of dementia. In this work, we present a technique that allows using specific time series analysis techniques with 3D images. This is achieved by sampling the image using a fractal-based method which preserves the spatial relationship among voxels. In addition, a method called Empirical functional PCA (EfPCA) is presented, which combines Empirical Mode Decomposition (EMD) with functional PCA to express an image in the space spanned by a basis of empirical functions, instead of using components computed by a predefined basis as in Fourier or Wavelet analysis. The devised technique has been used to classify images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Parkinson Progression Markers Initiative (PPMI), achieving accuracies up to 93% and 92% differential diagnosis tasks (AD versus controls and PD versus Controls, respectively). The results obtained validate the method, proving that the information retrieved by our methodology is significantly linked to the diseases.This work was partly supported by the MINECO/
FEDER under TEC2015-64718-R and PSI2015-
65848-R projects and the Consejer´ıa de Innovaci´on,
Ciencia y Empresa (Junta de Andaluc´ıa, Spain)
under the Excellence Project P11-TIC-7103 as well
as the Salvador deMadariaga Mobility Grants 2017.
Data collection and sharing for this project was
funded by the ADNI (National Institutes of Health
Grant U01 AG024904) and DOD ADNI (Depart ment of Defense award number W81XWH-12-2-
0012). ADNI is funded by the National Institute on
Aging, the National Institute of Biomedical Imaging
and Bioengineering, and through generous contribu tions from the following: AbbVie, Alzheimer’s Asso ciation; Alzheimer’s Drug Discovery Foundation;
Araclon Biotech; BioClinica, Inc.; Biogen; Bristol Myer Squibb Company; CereSpir, Inc.; Eisai Inc.;
Elan Pharmaceuticals, Inc.; Eli Lilly and Company;
EuroImmun; F. Ho mann-La Roche Ltd and its ali ated company Genentech, Inc.; Fujirebio; GE Health care; IXICO Ltd.; Janssen Alzheimer Immunother apy Research & Development, LLC.; Johnson &
Johnson Pharmaceutical Research & Development
LLC.; Lumosity; Lundbeck; Merck & Co., Inc.;
Meso Scale Diagnostics, LLC.; NeuroRx Research;
Neurotrack Technologies; Novartis Pharmaceuticals
Corporation; P zer Inc.; Piramal Imaging; Servier;
Takeda Pharmaceutical Company; and Transition
Therapeutics. The Canadian Institutes of Health
Research is providing funds to support ADNI clin ical sites in Canada. Private sector contributions
are facilitated by the Foundation for the National
Institutes of Health (www.fnih.org). The grantee
organization is the Northern California Institute for
Research and Education, and the study is coor dinated by the Alzheimer’s Disease Cooperative
Study at the University of California, San Diego.
ADNI data are disseminated by the Laboratory for
Neuro Imaging at the University of Southern Cali fornia. PPMI a public-private partnership is funded
by the Michael J. Fox Foundation for Parkinson’s
Research and funding partners, including [list the full
names of all of the PPMI funding partners found at
www.ppmi-info.org/fundingpartners]
On the Utility of Representation Learning Algorithms for Myoelectric Interfacing
Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden
Permutation Jaccard Distance-Based Hierarchical Clustering to Estimate EEG Network Density Modifications in MCI Subjects
In this paper, a novel electroencephalographic (EEG)-based method is introduced for the quantification of brain-electrical connectivity changes over a longitudinal evaluation of mild cognitive impaired (MCI) subjects. In the proposed method, a dissimilarity matrix is constructed by estimating the coupling strength between every pair of EEG signals, Hierarchical clustering is then applied to group the related electrodes according to the dissimilarity estimated on pairs of EEG recordings. Subsequently, the connectivity density of the electrodes network is calculated. The technique was tested over two different coupling strength descriptors: wavelet coherence (WC) and permutation Jaccard distance (PJD), a novel metric of coupling strength between time series introduced in this paper. Twenty-five MCI patients were enrolled within a follow-up program that consisted of two successive evaluations, at time T0 and at time T1, three months later. At T1, four subjects were diagnosed to have converted to Alzheimer's Disease (AD). When applying the PJD-based method, the converted patients exhibited a significantly increased PJD (p < 0.05), i.e., a reduced overall coupling strength, specifically in delta and theta bands and in the overall range (0.5-32 Hz). In addition, in contrast to stable MCI patients, converted patients exhibited a network density reduction in every subband (delta, theta, alpha, and beta). When WC was used as coupling strength descriptor, the method resulted in a less sensitive and specific outcome. The proposed method, mixing nonlinear analysis to a machine learning approach, appears to provide an objective evaluation of the connectivity density modifications associated to the MCI-AD conversion, just processing noninvasive EEG signals
Computational investigation of systemic pathway responses in severe pneumonia among the Gambian children and infants
Pneumonia remains the leading cause of infectious mortality in under-five children,
and the burden is highest in sub-Saharan Africa. To mitigate this burden, further
knowledge is required to accelerate the development of innovative and cost-effective
approaches. To gain a deeper insight into the pathogenesis of pneumonia,
I investigated the central hypothesis that systemic pathway (cellular and molecular)
responses underpin the development of severe pneumonia outcomes.
Mainly, I compared whole blood transcriptomes between severe pneumonia cases
(clinically stratified as mild, severe and very severe) and non-pneumonia community
controls (prospectively matched by age and sex). In total, 803 whole blood RNA
samples were collected from Gambian children (aged 2-59 months) between 2007
and 2010, of which, 518 passed laboratory quality control criteria for the microarray
analysis. After data cleaning, the final database reduced to 503 samples including
the training (n=345) and independent validation (n=158) data sets.
To investigate the cellular responses, I applied computational deconvolution
analysis to assess the variations of immune cell type proportions with pneumonia
severity. To further enhance the computational performance, I applied a data fusion
approach on 3,475 immune marker genes from different resources to derive an
optimal and integrated blood marker list (IBML, m=277) for Neutrophils, Monocytes,
NK, Dendritic, B and T cell types; which robustly performed better than the existing
individual resources. Using the IBML resource, pneumonia severity was significantly
associated with the depletion of B, T, Dendritic and NK cell types, and the elevation
of Monocytes and neutrophil proportions (P-value<0.001).
At the molecular level, pneumonia severity was associated (false discovery
rate<0.05) with a battery of systemic pathway (innate, adaptive and metabolic)
responses in a range of biomedical databases. While the up-regulation of
inflammatory innate responses was also observed in mild cases, severe pneumonia
cases were predominantly associated with the co-inhibition of the cells of the
adaptive immune response (B and T) and Natural killer cells, and the up-regulation
of fatty acid and lipid metabolism. While most of these findings were anticipated, the
involvement of NK cells was unexpected, and potentially presents a novel immune-modulation
target for mitigating the burden of pneumonia. Together, the cellular and
molecular pathways responses consistently support the central hypothesis that
systemic pathway responses contribute significantly to the development of severe
pneumonia outcomes.
Clinically, the identification and appropriate treatment of patients at the higher risk of
developing severe pneumonia outcomes remains the major challenge. To address
that, I applied supervised machine-learning approaches on cellular pathway based
transcriptomic features; and derived a 33-gene classifier (representing the NK, T,
and neutrophils cell types), which accurately detected severe pneumonia cases in
both the training (leave-one-out cross-validated accuracy=99%) and independent
validation (accuracy=98%) datasets. Independently, similar performance (98% in
each dataset) was associated with a subset (m=18) of the validated 52-gene
neonatal sepsis classifier. Conversely, at least 75% of the cellular biomarkers were
differentially expressed (false discovery rate<0.05) in bacterial neonatal sepsis.
Further, very severe pneumonia cases were predominantly associated with
antibacterial responses; and mild pneumonia cases with blood-culture-confirmed
positivity were also associated with an increased frequency of differentially
expressed genes. These findings suggest the significant contribution of bacterial
septicaemia in the development of serious pneumonia outcomes. Together, this
study highlights the future potential of host-derived systemic biomarkers for early
identification and novel treatment modalities of high-risk cases presenting at a
resource-constrained clinic with mild pneumonia. However, further validation studies
are required