57 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
Methods and Approaches for Characterizing Learning Related Changes Observed in functional MRI Data — A Review
Brain imaging data have so far revealed a wealth of information about neuronal circuits involved in higher mental functions like memory, attention, emotion, language etc. Our efforts are toward understanding the learning related effects in brain activity during the acquisition of visuo-motor sequential skills. The aim of this paper is to survey various methods and approaches of analysis that allow the characterization of learning related changes in fMRI data. Traditional imaging analysis using the Statistical Parametric Map (SPM) approach averages out temporal changes and presents overall differences between different stages of learning. We outline other potential approaches for revealing learning effects such as statistical time series analysis, modelling of haemodynamic response function and independent component analysis. We present example case studies from our visuo-motor sequence learning experiments to describe application of SPM and statistical time series analyses. Our review highlights that the problem of characterizing learning induced changes in fMRI data remains an interesting and challenging open research problem
A Multi-disciplinary Approach to the Investigation of Aspects of Serial Order in Cognition
Serial order processing or Sequence processing underlies many human activities such as speech, language, skill learning, planning, problem solving, etc. Investigating the\ud
neural bases of sequence processing enables us to understand serial order in cognition and helps us building intelligent devices. In the current paper, various\ud
cognitive issues related to sequence processing will be discussed with examples. Some of the issues are: distributed versus local representation, pre-wired versus\ud
adaptive origins of representation, implicit versus explicit learning, fixed/flat versus hierarchical organization, timing aspects, order information embedded in sequences, primacy versus recency in list learning and aspects of sequence perception such as recognition, recall and generation. Experimental results that give evidence for the involvement of various brain areas will be described. Finally, theoretical frameworks based on Markov models and Reinforcement Learning paradigm will be presented. These theoretical ideas are useful for studying sequential phenomena in a principled way
Investigation of sequence processing: A cognitive and computational neuroscience perspective
Serial order processing or sequence processing underlies
many human activities such as speech, language, skill
learning, planning, problem-solving, etc. Investigating
the neural bases of sequence processing enables us to
understand serial order in cognition and also helps in
building intelligent devices. In this article, we review
various cognitive issues related to sequence processing
with examples. Experimental results that give evidence
for the involvement of various brain areas will be described.
Finally, a theoretical approach based on statistical
models and reinforcement learning paradigm is
presented. These theoretical ideas are useful for studying
sequence learning in a principled way. This article
also suggests a two-way process diagram integrating
experimentation (cognitive neuroscience) and theory/
computational modelling (computational neuroscience).
This integrated framework is useful not only in the present
study of serial order, but also for understanding
many cognitive processes
Atypical Flexibility in Dynamic Functional Connectivity Quantifies the Severity in Autism Spectrum Disorder
Resting-state functional connectivity (FC) analyses have shown atypical connectivity in autism spectrum disorder (ASD) as compared to typically developing (TD). However, this view emerges from investigating static FC overlooking the whole brain transient connectivity patterns. In our study, we investigated how age and disease influence the dynamic changes in functional connectivity of TD and ASD. We used resting-state functional magnetic resonance imaging (rs-fMRI) data stratified into three cohorts: children (7–11 years), adolescents (12–17 years), and adults (18+ years) for the analysis. The dynamic variability in the connection strength and the modular organization in terms of measures such as flexiblity, cohesion strength, and disjointness were explored for each subject to characterize the differences between ASD and TD. In ASD, we observed significantly higher inter-subject dynamic variability in connection strength as compared to TD. This hyper-variability relates to the symptom severity in ASD. We also found that whole-brain flexibility correlates with static modularity only in TD. Further, we observed a core-periphery organization in the resting-state, with Sensorimotor and Visual regions in the rigid core; and DMN and attention areas in the flexible periphery. TD also develops a more cohesive organization of sensorimotor areas. However, in ASD we found a strong positive correlation of symptom severity with flexibility of rigid areas and with disjointness of sensorimotor areas. The regions of the brain showing high predictive power of symptom severity were distributed across the cortex, with stronger bearings in the frontal, motor, and occipital cortices. Our study demonstrates that the dynamic framework best characterizes the variability in ASD
Enhancing Healthcare with EOG: A Novel Approach to Sleep Stage Classification
We introduce an innovative approach to automated sleep stage classification
using EOG signals, addressing the discomfort and impracticality associated with
EEG data acquisition. In addition, it is important to note that this approach
is untapped in the field, highlighting its potential for novel insights and
contributions. Our proposed SE-Resnet-Transformer model provides an accurate
classification of five distinct sleep stages from raw EOG signal. Extensive
validation on publically available databases (SleepEDF-20, SleepEDF-78, and
SHHS) reveals noteworthy performance, with macro-F1 scores of 74.72, 70.63, and
69.26, respectively. Our model excels in identifying REM sleep, a crucial
aspect of sleep disorder investigations. We also provide insight into the
internal mechanisms of our model using techniques such as 1D-GradCAM and t-SNE
plots. Our method improves the accessibility of sleep stage classification
while decreasing the need for EEG modalities. This development will have
promising implications for healthcare and the incorporation of wearable
technology into sleep studies, thereby advancing the field's potential for
enhanced diagnostics and patient comfort
fMRI Investigation of Cortical and Subcortical Networks in the Learning of Abstract and Effector-Specific Representations of Motor Sequences
A visuomotor sequence can be learned as a series of visuo-spatial cues or as a sequence of effector movements. Earlier imaging studies have revealed that a network of brain areas is activated in the course of motor sequence learning. However these studies do not address the question of the type of representation being established at various stages of visuomotor sequence learning. In an earlier behavioral study, we demonstrated that acquisition of visuo-spatial sequence representation enables rapid learning in the early stage and progressive establishment of somato-motor representation helps speedier execution by the late stage. We conducted functional magnetic resonance imaging (fMRI) experiments wherein subjects learned and practiced the same sequence alternately in normal and rotated settings. In one rotated setting (visual), subjects learned a new motor sequence in response to an identical sequence of visual cues as in normal. In another rotated setting (motor), the display sequence was altered as compared to normal, but the same sequence of effector movements were used to perform the sequence. Comparison of different rotated settings revealed analogous transitions both in the cortical and subcortical sites during visuomotor sequence learning  a transition of activity from parietal to parietal-premotor and then to premotor cortex and a concomitant shift was observed from anterior putamen to a combined activity in both anterior and posterior putamen and finally to posterior putamen. These results suggest a putative role for engagement of different cortical and subcortical networks at various stages of learning in supporting distinct sequence representations
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