Biology subjects

Leow, A. D.

Publications and source records attributed to Leow, A. D..

3 recordsLinked to original sources

bioRxiv2024

Instantaneous Frequency: A New Functional Biomarker for Dynamic Brain Causal Networks

This study introduces instantaneous frequency (IF) analysis as a novel method for characterizing dynamic brain causal networks from fMRI blood-oxygen-level-dependent (BOLD) signals. Effective connectivity, estimated using dynamic causal modeling (DCM), is analyzed to derive IF sequences, with the average IF across brain regions serving as a potential biomarker for global network oscillatory behavior. Analysis of data from the Alzheimers Disease Neuroimaging Initiative (ADNI), Open Access Series of Imaging Studies (OASIS), and Human Connectome Project (HCP) demonstrates the methods efficacy in distinguishing between clinical and demographic groups, such as cognitive decline stages, sex differences, and sleep quality levels. Statistical analyses reveal significant group differences in IF metrics, highlighting its potential as a sensitive indicator for early diagnosis and monitoring of neurodegenerative and cognitive conditions. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/628965v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@10da58org.highwire.dtl.DTLVardef@6eee6borg.highwire.dtl.DTLVardef@e6e756org.highwire.dtl.DTLVardef@15dff2a_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIThe study introduces instantaneous frequency (IF) as a novel biomarker derived from dynamic brain effective connectivity, capturing temporal fluctuations in brain networks. C_LIO_LIThe proposed IF biomarker effectively differentiates between various clinical stages, such as Mild Cognitive Impairment (MCI) and Alzheimers Disease (AD), and demographic factors, including sex and sleep quality. C_LIO_LIThe robustness and clinical relevance of the IF biomarker are validated using three independent datasets: ADNI, OASIS, and HCP, demonstrating its potential in cognitive and neurological research. C_LI

neuroscience↗

bioRxiv2020

Everyone Can Learn to Meditate: Characterizing a Personalized Connectomic State Space among Meditation Groups

Alterations in brain connectivity has been shown for many disease states and groups of people from different levels of cognitive training. To study dynamic functional connectivity, we propose a method for a personalized connectomic state space called Thought Chart. Experienced meditators are an interesting group of healthy subjects for brain connectivity analyses due to their demonstrated differences in resting state dynamics, and altered brain connectivity has been implicated as a potential factor in several psychiatric disorders. Three distinct techniques of meditation are explored: Isha Yoga, Himalayan Yoga, and Vipassana, as well as a meditation-naive group of individuals. All individuals participated in a breath awareness task, an autobiographical thinking task, and one of three different meditation practices according to their expertise, while being recorded by a 64-electrode electroencephalogram (EEG). The functional brain connectivity was estimated using weighted phase lag index (WPLI) and the connectivity dynamics were investigated using a within-individual formulation of Thought Chart, a previously proposed dimensionality reduction method which utilizes manifold learning to map out a state space of functional connectivity. Results showed that the two meditation tasks (breath awareness task and own form of meditation) in all groups were found to have consistently different functional connectivity patterns relative to those of the instructed mind-wandering (IMW) tasks in each individual, as measured using the Hausdorff distance in the state space. The specific meditation state was found to be most similar to the breath awareness state in all groups, as expected in these meditation traditions which all incorporate breath awareness training in their practice trajectory. The difference in connectivity was found to not be solely driven by specific frequency bands. These results demonstrate that the within-individual form of Thought Chart consistently and reliably separates similar tasks among healthy meditators and non-meditators during resting state-like EEG recordings. Unexpectedly, we found the dissimilarity between breath awareness/meditation and IMW, measured via Hausdorff distance, regardless of meditation experience or tradition, with no significant group differences.

neuroscience↗

bioRxiv2020

rest2vec: Vectorizing the resting-state functional connectome using graph embedding

Resting-state functional magnetic resonance imaging (O_SCPLOWRSC_SCPLOWO_SCPCAP-C_SCPCAPO_SCPLOWFMRIC_SCPLOW) is widely used in connectomics for studying the functional relationships between regions of the human brain. O_SCPLOWRSC_SCPLOWO_SCPCAP-C_SCPCAPO_SCPLOWFMRIC_SCPLOW connectomics, however, has inherent analytical challenges, such as accounting for negative correlations. In addition, functional relationships between brain regions do not necessarily correspond to their anatomical distance, making the intrinsic geometry of the functional connectome less well understood. Recent techniques in natural language processing and machine learning, such as word2vec, have used embedding methods to map high-dimensional data into meaningful vector spaces. Inspired by this approach, we have developed a graph embedding pipeline, rest2vec, for studying the intrinsic geometry of functional connectomes. We demonstrate how rest2vec uses the phase angle spatial embedding (O_SCPLOWPHASEC_SCPLOW) method with dimensionality reduction techniques to embed the functional connectome into lower dimensions. Rest2vec can also be linked to the maximum mean discrepancy (O_SCPLOWMMDC_SCPLOW) metric to assign functional modules of the connectome in a continuous manner, improving upon traditional binary classification methods. Together, this allows for studying the functional connectome such that the full range of correlative information is preserved and gives a more informed understanding of the functional organization of the brain.

neuroscience↗