11,999 research outputs found
Detection of Cognitive States from fMRI data using Machine Learning Techniques
Over the past decade functional Magnetic Resonance
Imaging (fMRI) has emerged as a powerful
technique to locate activity of human brain while
engaged in a particular task or cognitive state. We
consider the inverse problem of detecting the cognitive
state of a human subject based on the fMRI
data. We have explored classification techniques
such as Gaussian Naive Bayes, k-Nearest
Neighbour and Support Vector Machines. In order
to reduce the very high dimensional fMRI data, we
have used three feature selection strategies. Discriminating
features and activity based features
were used to select features for the problem of
identifying the instantaneous cognitive state given
a single fMRI scan and correlation based features
were used when fMRI data from a single time interval
was given. A case study of visuo-motor sequence
learning is presented. The set of cognitive
states we are interested in detecting are whether the
subject has learnt a sequence, and if the subject is
paying attention only towards the position or towards
both the color and position of the visual
stimuli. We have successfully used correlation
based features to detect position-color related cognitive
states with 80% accuracy and the cognitive
states related to learning with 62.5% accuracy
A reversal coarse-grained analysis with application to an altered functional circuit in depression
Introduction:
When studying brain function using functional magnetic resonance imaging (fMRI) data containing tens of thousands of voxels, a coarse-grained approach – dividing the whole brain into regions of interest – is applied frequently to investigate the organization of the functional network on a relatively coarse scale. However, a coarse-grained scheme may average out the fine details over small spatial scales, thus rendering it difficult to identify the exact locations of functional abnormalities.
Methods:
A novel and general approach to reverse the coarse-grained approach by locating the exact sources of the functional abnormalities is proposed.
Results:
Thirty-nine patients with major depressive disorder (MDD) and 37 matched healthy controls are studied. A circuit comprising the left superior frontal gyrus (SFGdor), right insula (INS), and right putamen (PUT) exhibit the greatest changes between the patients with MDD and controls. A reversal coarse-grained analysis is applied to this circuit to determine the exact location of functional abnormalities.
Conclusions:
The voxel-wise time series extracted from the reversal coarse-grained analysis (source) had several advantages over the original coarse-grained approach: (1) presence of a larger and detectable amplitude of fluctuations, which indicates that neuronal activities in the source are more synchronized; (2) identification of more significant differences between patients and controls in terms of the functional connectivity associated with the sources; and (3) marked improvement in performing discrimination tasks. A software package for pattern classification between controls and patients is available in Supporting Information
Brain-mediated Transfer Learning of Convolutional Neural Networks
The human brain can effectively learn a new task from a small number of
samples, which indicate that the brain can transfer its prior knowledge to
solve tasks in different domains. This function is analogous to transfer
learning (TL) in the field of machine learning. TL uses a well-trained feature
space in a specific task domain to improve performance in new tasks with
insufficient training data. TL with rich feature representations, such as
features of convolutional neural networks (CNNs), shows high generalization
ability across different task domains. However, such TL is still insufficient
in making machine learning attain generalization ability comparable to that of
the human brain. To examine if the internal representation of the brain could
be used to achieve more efficient TL, we introduce a method for TL mediated by
human brains. Our method transforms feature representations of audiovisual
inputs in CNNs into those in activation patterns of individual brains via their
association learned ahead using measured brain responses. Then, to estimate
labels reflecting human cognition and behavior induced by the audiovisual
inputs, the transformed representations are used for TL. We demonstrate that
our brain-mediated TL (BTL) shows higher performance in the label estimation
than the standard TL. In addition, we illustrate that the estimations mediated
by different brains vary from brain to brain, and the variability reflects the
individual variability in perception. Thus, our BTL provides a framework to
improve the generalization ability of machine-learning feature representations
and enable machine learning to estimate human-like cognition and behavior,
including individual variability
Evaluation of atlas-based segmentation of hippocampi in healthy humans
Introduction and aim: Region of interest (ROI)-based functional magnetic resonance imaging (fMRI) data analysis relies on extracting signals from a specific area which is presumed to be involved in the brain activity being studied. The hippocampus is of interest in many functional connectivity studies for example in epilepsy as it plays an important role in epileptogenesis. In this context, ROI may be defined using different techniques. Our study aims at evaluating the spatial correspondence of hippocampal ROIs obtained using three brain atlases with hippocampal ROI obtained using an automatic segmentation algorithm dedicated to the hippocampus.
Material and methods: High-resolution volumetric T1-weighted MR images of 18 healthy volunteers (five females) were acquired on a 3T scanner. Individual ROIs for both hippocampi of each subject were segmented from the MR images using an automatic hippocampus and amygdala segmentation software called SACHA providing the gold standard ROI for comparison with the atlas-derived results. For each subject, hippocampal ROIs were also obtained using three brain atlases: PickAtlas available as a commonly used software toolbox; automated anatomical labeling (AAL) atlas included as a subset of ROI into PickAtlas toolbox and a frequency-based brain atlas by Hammers et al. The levels of agreement between the SACHA results and those obtained using the atlases were assessed based on quantitative indices measuring volume differences and spatial overlap. The comparison was performed in standard Montreal Neurological Institute space, the registration being obtained with SPM5 (http://www.fil.ion.ucl.ac.uk/spm/).
Results: The mean volumetric error across all subjects was 73% for hippocampal ROIs derived from AAL atlas; 20% in case of ROIs derived from the Hammers atlas and 107% for ROIs derived from PickAtlas. The mean false-positive and false-negative classification rates were 60% and 10% respectively for the AAL atlas; 16% and 32% for the Hammers atlas and 6% and 72% for the PickAtlas.
Conclusion: Though atlas-based ROI definition may be convenient, the resulting ROIs may be poor representations of the hippocampus in some studies critical to under- or oversampling. Performance of the AAL atlas was inferior to that of the Hammers atlas. Hippocampal ROIs derived from PickAtlas are highly significantly smaller, and this results in the worst performance out of three atlases. It is advisable that the defined ROIs should be verified with knowledge of neuroanatomy before using it for further data analysis
An overview of the first 5 years of the ENIGMA obsessive–compulsive disorder working group: The power of worldwide collaboration
Abstract Neuroimaging has played an important part in advancing our understanding of the neurobiology of obsessive?compulsive disorder (OCD). At the same time, neuroimaging studies of OCD have had notable limitations, including reliance on relatively small samples. International collaborative efforts to increase statistical power by combining samples from across sites have been bolstered by the ENIGMA consortium; this provides specific technical expertise for conducting multi-site analyses, as well as access to a collaborative community of neuroimaging scientists. In this article, we outline the background to, development of, and initial findings from ENIGMA's OCD working group, which currently consists of 47 samples from 34 institutes in 15 countries on 5 continents, with a total sample of 2,323 OCD patients and 2,325 healthy controls. Initial work has focused on studies of cortical thickness and subcortical volumes, structural connectivity, and brain lateralization in children, adolescents and adults with OCD, also including the study on the commonalities and distinctions across different neurodevelopment disorders. Additional work is ongoing, employing machine learning techniques. Findings to date have contributed to the development of neurobiological models of OCD, have provided an important model of global scientific collaboration, and have had a number of clinical implications. Importantly, our work has shed new light on questions about whether structural and functional alterations found in OCD reflect neurodevelopmental changes, effects of the disease process, or medication impacts. We conclude with a summary of ongoing work by ENIGMA-OCD, and a consideration of future directions for neuroimaging research on OCD within and beyond ENIGMA
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