arXiv subjects

Cooper J. Mellema

Publications and source records attributed to Cooper J. Mellema.

3 recordsLinked to original sources

arXiv2023

Novel Machine Learning Approaches for Improving the Reproducibility and Reliability of Functional and Effective Connectivity from Functional MRI

Objective: New measures of human brain connectivity are needed to address gaps in the existing measures and facilitate the study of brain function, cognitive capacity, and identify early markers of human disease. Traditional approaches to measure functional connectivity between pairs of brain regions in functional MRI, such as correlation and partial correlation, fail to capture nonlinear aspects in the regional associations. We propose a new machine learning based measure of functional connectivity which efficiently captures linear and nonlinear aspects. Approach: We propose two new EC measures. The first, a machine learning based measure of effective connectivity, measures nonlinear aspects across the entire brain. The second, Structurally Projected Granger Causality adapts Granger Causal connectivity to efficiently characterize and regularize the whole brain EC connectome to respect underlying biological structural connectivity. The proposed measures are compared to traditional measures in terms of reproducibility and the ability to predict individual traits in order to demonstrate these measures internal validity. We use four repeat scans of the same individuals from the Human Connectome Project and measure the ability of the measures to predict individual subject physiologic and cognitive traits. Main results: The proposed new FC measure of ML.FC attains high reproducibility with an R squared of 0.44, while the proposed EC measure of SP.GC attains the highest predictive power with an R squared of 0.66. Significance: The proposed methods are highly suitable for achieving high reproducibility and predictiveness.

q-bio.NC↗

arXiv2022

Longitudinal Prognosis of Parkinsons Outcomes using Causal Connectivity

Parkinsons disease (PD) is a movement disorder and the second most common neurodengerative disease but despite its relative abundance, there are no clinically accepted neuroimaging biomarkers to make prognostic predictions or differentiate between the similar atypical neurodegenerative diseases Multiple System Atrophy and Progressive Supranuclear Palsy. Abnormal connectivity in circuits including the motor circuit and basal ganglia have been previously shown as early markers of neurodegeneration. Therefore, we postulate the combination patterns of interregional dysconnectivity across the brain can be used to form a patient-specific predictive model of disease state and progression in PD. These models, which employ connectivity calculated from noninvasively measured functional MRI, differentially predict between PD and the atypical lookalikes, predict progression on a disease-specific scale, and predict cognitive decline. Further, we identify the connections most informative for progression and diagnosis. When predicting the one-year progression in the Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) and Montreal Cognitive assessment (MoCA), mean absolute errors of 1.8 and 0.6 basis points in the prediction are achieved respectively. A balanced accuracy of 0.68 is attained when distinguishing idiopathic PD versus the lookalikes and healthy controls. We additionally find network components strongly associated with the prognostic and diagnostic tasks, particularly incorporating connections within deep nuclei, motor regions, and the Thalamus. These predictions, using an MRI modality readily available in most clinical settings, demonstrate the strong potential of fMRI connectivity as a prognostic biomarker in Parkinsons disease.

q-bio.NC↗

arXiv2020

Architectural configurations, atlas granularity and functional connectivity with diagnostic value in Autism Spectrum Disorder

Currently, the diagnosis of Autism Spectrum Disorder (ASD) is dependent upon a subjective, time-consuming evaluation of behavioral tests by an expert clinician. Non-invasive functional MRI (fMRI) characterizes brain connectivity and may be used to inform diagnoses and democratize medicine. However, successful construction of deep learning models from fMRI requires addressing key choices about the model's architecture, including the number of layers and number of neurons per layer. Meanwhile, deriving functional connectivity (FC) features from fMRI requires choosing an atlas with an appropriate level of granularity. Once a model has been built, it is vital to determine which features are predictive of ASD and if similar features are learned across atlas granularity levels. To identify aptly suited architectural configurations, probability distributions of the configurations of high versus low performing models are compared. To determine the effect of atlas granularity, connectivity features are derived from atlases with 3 levels of granularity and important features are ranked with permutation feature importance. Results show the highest performing models use between 2-4 hidden layers and 16-64 neurons per layer, granularity dependent. Connectivity features identified as important across all 3 atlas granularity levels include FC to the supplementary motor gyrus and language association cortex, regions associated with deficits in social and sensory processing in ASD. Importantly, the cerebellum, often not included in functional analyses, is also identified as a region whose abnormal connectivity is highly predictive of ASD. Results of this study identify important regions to include in future studies of ASD, help assist in the selection of network architectures, and help identify appropriate levels of granularity to facilitate the development of accurate diagnostic models of ASD.

cs.LG↗