A Two Consequent Multi-layers Deep Discriminative Approach for Classifying fMRI Images

2020 ◽  
Vol 29 (06) ◽  
pp. 2030001
Author(s):  
Abeer M. Mahmoud ◽  
Hanen Karamti ◽  
Fadwa Alrowais
Keyword(s):  
Feature Space ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Brain Disorder ◽  
Medical Problems ◽  
Discriminative Approach ◽  
Large Brain ◽  
Data Driven Approach

Functional Magnetic Resonance Imaging (fMRI), for many decades acts as a potential aiding method for diagnosing medical problems. Several successful machine learning algorithms have been proposed in literature to extract valuable knowledge from fMRI. One of these algorithms is the convolutional neural network (CNN) that competent with high capabilities for learning optimal abstractions of fMRI. This is because the CNN learns features similarly to human brain where it preserves local structure and avoids distortion of the global feature space. Focusing on the achievements of using the CNN for the fMRI, and accordingly, the Deep Convolutional Auto-Encoder (DCAE) benefits from the data-driven approach with CNN’s optimal features to strengthen the fMRI classification. In this paper, a new two consequent multi-layers DCAE deep discriminative approach for classifying fMRI Images is proposed. The first DCAE is unsupervised sub-model that is composed of four CNN. It focuses on learning weights to utilize discriminative characteristics of the extracted features for robust reconstruction of fMRI with lower dimensional considering tiny details and refining by its deep multiple layers. Then the second DCAE is a supervised sub-model that focuses on training labels to reach an outperformed results. The proposed approach proved its effectiveness and improved literately reported results on a large brain disorder fMRI dataset.

scholarly journals A Novel Data-Driven Approach to Preoperative Mapping of Functional Cortex Using Resting-State Functional Magnetic Resonance Imaging

Neurosurgery ◽  
2013 ◽  
Vol 73 (6) ◽  
pp. 969-983 ◽  
Author(s):  
Timothy J. Mitchell ◽  
Carl D. Hacker ◽  
Jonathan D. Breshears ◽  
Nick P. Szrama ◽  
Mohit Sharma ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Resting State ◽  
Tumor Resection ◽  
Data Driven ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Preoperative Mapping ◽  
Data Driven Approach

Abstract BACKGROUND: Recent findings associated with resting-state cortical networks have provided insight into the brain's organizational structure. In addition to their neuroscientific implications, the networks identified by resting-state functional magnetic resonance imaging (rs-fMRI) may prove useful for clinical brain mapping. OBJECTIVE: To demonstrate that a data-driven approach to analyze resting-state networks (RSNs) is useful in identifying regions classically understood to be eloquent cortex as well as other functional networks. METHODS: This study included 6 patients undergoing surgical treatment for intractable epilepsy and 7 patients undergoing tumor resection. rs-fMRI data were obtained before surgery and 7 canonical RSNs were identified by an artificial neural network algorithm. Of these 7, the motor and language networks were then compared with electrocortical stimulation (ECS) as the gold standard in the epilepsy patients. The sensitivity and specificity for identifying these eloquent sites were calculated at varying thresholds, which yielded receiver-operating characteristic (ROC) curves and their associated area under the curve (AUC). RSNs were plotted in the tumor patients to observe RSN distortions in altered anatomy. RESULTS: The algorithm robustly identified all networks in all patients, including those with distorted anatomy. When all ECS-positive sites were considered for motor and language, rs-fMRI had AUCs of 0.80 and 0.64, respectively. When the ECS-positive sites were analyzed pairwise, rs-fMRI had AUCs of 0.89 and 0.76 for motor and language, respectively. CONCLUSION: A data-driven approach to rs-fMRI may be a new and efficient method for preoperative localization of numerous functional brain regions.

scholarly journals A Transdiagnostic Data-driven Study of Children’s Behaviour and the Functional Connectome

2021 ◽  
Author(s):  
J. S. Jones ◽  
D. E. Astle ◽  
Keyword(s):  
Executive Function ◽  
Data Driven ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Functional Connectome ◽  
Behavioural Difficulties ◽  
Intrinsic Connectivity ◽  
Intrinsic Connectivity Networks ◽  
Children’S Behaviour ◽  
The Brain

AbstractBehavioural difficulties are seen as hallmarks of many neurodevelopmental conditions. Differences in functional brain organisation have been observed in these conditions, but little is known about how they are related to a child’s profile of behavioural difficulties. We investigated whether behavioural difficulties are associated with how the brain is functionally organised in an intentionally heterogeneous and transdiagnostic sample of 957 children aged 5-15. We used consensus community detection to derive data-driven profiles of behavioural difficulties and constructed functional connectomes from a subset of 238 children with resting-state functional Magnetic Resonance Imaging (fMRI) data. We identified three distinct profiles of behaviour that were characterised by principal difficulties with hot executive function, cool executive function, and learning. Global organisation of the functional connectome did not differ between the groups, but multivariate patterns of connectivity at the level of Intrinsic Connectivity Networks (ICNs), nodes, and hubs significantly predicted group membership in held-out data. Fronto-parietal connector hubs were under-connected in all groups relative to a comparison sample, and children with hot vs cool executive function difficulties were distinguished by connectivity in ICNs associated with cognitive control, emotion processing, and social cognition. This demonstrates both general and specific neurodevelopmental risk factors in the functional connectome.

scholarly journals Clustering for Data-driven Unraveling Artificial Neural Networks

2020 ◽  
Author(s):  
Felipe Farias ◽  
Teresa Ludermir ◽  
Carmelo Bastos-Filho
Keyword(s):  
Neural Networks ◽  
Statistical Significance ◽  
Feature Space ◽  
Data Driven ◽  
P Value ◽  
Proposed Model ◽  
Data Driven Approach ◽  
Benchmark Datasets ◽  
Hidden Layer ◽  
Fold Cross Validation

This work presents an investigation on how to define Neural Networks (NN) architectures adopting a data-driven approach using clustering to create sub-labels to facilitate the learning process and to discover the number of neurons needed to compose the layers. We also increase the depth of the model aiming to represent the samples better, the more in-depth it flows into the model. We hypothesize that the clustering process identifies sub-regions in the feature space in which the samples belonging to the same cluster have strong similarities. We used seven benchmark datasets to validate our hypothesis using 10-fold cross validation 3 times. The proposed model increased the performance, while never decreased it, with statistical significance considering the p-value $< 0.05$ in comparison with a Multi-Layer Perceptron with a single hidden layer with approximately the same number of parameters of the architectures found by our approach.

scholarly journals A triple-network organization for the mouse brain

2021 ◽  
Author(s):  
Francesca Mandino ◽  
Roël M. Vrooman ◽  
Heidi E. Foo ◽  
Ling Yun Yeow ◽  
Thomas A. W. Bolton ◽  
...  
Keyword(s):  
Mouse Brain ◽  
Data Driven ◽  
Network Organization ◽  
Brain Disorder ◽  
Default Mode ◽  
Temporal Properties ◽  
Disease Condition ◽  
Data Driven Approach ◽  
Species Comparisons ◽  
Spatio Temporal

AbstractThe triple-network model of psychopathology is a framework to explain the functional and structural neuroimaging phenotypes of psychiatric and neurological disorders. It describes the interactions within and between three distributed networks: the salience, default-mode, and central executive networks. These have been associated with brain disorder traits in patients. Homologous networks have been proposed in animal models, but their integration into a triple-network organization has not yet been determined. Using resting-state datasets, we demonstrate conserved spatio-temporal properties between triple-network elements in human, macaque, and mouse. The model predictions were also shown to apply in a mouse model for depression. To validate spatial homologies, we developed a data-driven approach to convert mouse brain maps into human standard coordinates. Finally, using high-resolution viral tracers in the mouse, we refined an anatomical model for these networks and validated this using optogenetics in mice and tractography in humans. Unexpectedly, we find serotonin involvement within the salience rather than the default-mode network. Our results support the existence of a triple-network system in the mouse that shares properties with that of humans along several dimensions, including a disease condition. Finally, we demonstrate a method to humanize mouse brain networks that opens doors to fully data-driven trans-species comparisons.

scholarly journals A naturalistic neuroimaging database for understanding the brain using ecological stimuli

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Sarah Aliko ◽  
Jiawen Huang ◽  
Florin Gheorghiu ◽  
Stefanie Meliss ◽  
Jeremy I. Skipper
Keyword(s):  
Resting State ◽  
Signal To Noise Ratio ◽  
Data Driven ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Complete Understanding ◽  
Movement Data ◽  
Good Signal ◽  
Functional Analyses ◽  
The Brain

Abstract Neuroimaging has advanced our understanding of human psychology using reductionist stimuli that often do not resemble information the brain naturally encounters. It has improved our understanding of the network organization of the brain mostly through analyses of ‘resting-state’ data for which the functions of networks cannot be verifiably labelled. We make a ‘Naturalistic Neuroimaging Database’ (NNDb v1.0) publically available to allow for a more complete understanding of the brain under more ecological conditions during which networks can be labelled. Eighty-six participants underwent behavioural testing and watched one of 10 full-length movies while functional magnetic resonance imaging was acquired. Resulting timeseries data are shown to be of high quality, with good signal-to-noise ratio, few outliers and low movement. Data-driven functional analyses provide further evidence of data quality. They also demonstrate accurate timeseries/movie alignment and how movie annotations might be used to label networks. The NNDb can be used to answer questions previously unaddressed with standard neuroimaging approaches, progressing our knowledge of how the brain works in the real world.

scholarly journals Schizophrenia Exhibits Bi-Directional Brain-Wide Alterations in Cortico-Striato-Cerebellar Circuits

2017 ◽  
Author(s):  
Jie Lisa Ji ◽  
Caroline Diehl ◽  
Charles Schleifer ◽  
Carol A. Tamminga ◽  
Matcheri S. Keshavan ◽  
...  
Keyword(s):  
Resting State ◽  
Data Driven ◽  
Association Cortex ◽  
Support Vector ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Chronic Patients ◽  
Functional Pathways ◽  
Using Data ◽  
Hallmark Feature

ABSTRACTDistributed neural dysconnectivity is considered a hallmark feature of schizophrenia, yet a tension exists between studies pinpointing focal disruptions versus those implicating brain-wide disturbances. The cerebellum and the striatum communicate reciprocally with the thalamus and cortex through monosynaptic and polysynaptic connections, forming cortico-striatal-thalamic-cerebellar (CSTC) functional pathways that may be sensitive to brain-wide dysconnectivity in schizophrenia. It remains unknown if the same pattern of alterations persists across CSTC systems, or if specific alterations exist along key functional elements of these networks. We characterized connectivity along major functional CSTC subdivisions using resting-state functional magnetic resonance imaging in 159 chronic patients and 162 matched controls. Associative CSTC subdivisions revealed consistent brain-wide bi-directional alterations in patients, marked by hyper-connectivity with sensory-motor cortices and hypo-connectivity with association cortex. Focusing on the cerebellar and striatal components, we validate the effects using data-driven k-means clustering of voxel-wise dysconnectivity and support vector machine classifiers. We replicate these results in an independent sample of 202 controls and 145 patients, additionally demonstrating that these neural effects relate to cognitive performance across subjects. Taken together, these results from complementary approaches implicate a consistent motif of brain-wide alterations in CSTC systems in schizophrenia, calling into question accounts of exclusively focal functional disturbances.

scholarly journals Functional topography of the human entorhinal cortex

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Tobias Navarro Schröder ◽  
Koen V Haak ◽  
Nestor I Zaragoza Jimenez ◽  
Christian F Beckmann ◽  
Christian F Doeller
Keyword(s):  
Entorhinal Cortex ◽  
Spatial Information ◽  
Spatial Navigation ◽  
Data Driven ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Human Homologue ◽  
Related Disease ◽  
Functional Topography

Despite extensive research on the role of the rodent medial and lateral entorhinal cortex (MEC/LEC) in spatial navigation, memory and related disease, their human homologues remain elusive. Here, we combine high-field functional magnetic resonance imaging at 7 T with novel data-driven and model-based analyses to identify corresponding subregions in humans based on the well-known global connectivity fingerprints in rodents and sensitivity to spatial and non-spatial information. We provide evidence for a functional division primarily along the anteroposterior axis. Localising the human homologue of the rodent MEC and LEC has important implications for translating studies on the hippocampo-entorhinal memory system from rodents to humans.

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