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  1. Article ; Online: Novel machine learning approaches for improving the reproducibility and reliability of functional and effective connectivity from functional MRI.

    Mellema, Cooper J / Montillo, Albert A

    Journal of neural engineering

    2023  Volume 20, Issue 6

    Abstract: Objective. ...

    Abstract Objective.
    MeSH term(s) Humans ; Reproducibility of Results ; Brain/physiology ; Connectome/methods ; Magnetic Resonance Imaging/methods ; Machine Learning
    Language English
    Publishing date 2023-12-04
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 2170901-4
    ISSN 1741-2552 ; 1741-2560
    ISSN (online) 1741-2552
    ISSN 1741-2560
    DOI 10.1088/1741-2552/ad0c5f
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    In stock of ZB MED Cologne/Königswinter

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    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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  2. Book ; Online: Novel Machine Learning Approaches for Improving the Reproducibility and Reliability of Functional and Effective Connectivity from Functional MRI

    Mellema, Cooper J. / Montillo, Albert

    2022  

    Abstract: 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 ... ...

    Abstract 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.

    Comment: 23 pages, 5 figures, 3 algorithms, 2 equations, 3 tables, 5 pages supplemental
    Keywords Quantitative Biology - Neurons and Cognition ; Quantitative Biology - Quantitative Methods
    Subject code 120
    Publishing date 2022-01-31
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Reproducible neuroimaging features for diagnosis of autism spectrum disorder with machine learning.

    Mellema, Cooper J / Nguyen, Kevin P / Treacher, Alex / Montillo, Albert

    Scientific reports

    2022  Volume 12, Issue 1, Page(s) 3057

    Abstract: Autism spectrum disorder (ASD) is the fourth most common neurodevelopmental disorder, with a prevalence of 1 in 160 children. Accurate diagnosis relies on experts, but such individuals are scarce. This has led to increasing interest in the development of ...

    Abstract Autism spectrum disorder (ASD) is the fourth most common neurodevelopmental disorder, with a prevalence of 1 in 160 children. Accurate diagnosis relies on experts, but such individuals are scarce. This has led to increasing interest in the development of machine learning (ML) models that can integrate neuroimaging features from functional and structural MRI (fMRI and sMRI) to help reveal central nervous system alterations characteristic of ASD. We optimized and compared the performance of 12 of the most popular and powerful ML models. Each was separately trained using 15 different combinations of fMRI and sMRI features and optimized with an unbiased model search. Deep learning models predicted ASD with the highest diagnostic accuracy and generalized well to other MRI datasets. Our model achieves state-of-the-art 80% area under the ROC curve (AUROC) in diagnosis on test data from the IMPAC dataset; and 86% and 79% AUROC on the external ABIDE I and ABIDE II datasets (with further improvement to 93% and 90% after supervised domain adaptation). The highest performing models identified reproducible putative biomarkers for accurate ASD diagnosis in accord with known ASD markers as well as novel cerebellar biomarkers. Such reproducibility lends credence to their tremendous potential for defining and using a set of truly generalizable ASD biomarkers that will advance scientific understanding of neuronal changes in ASD.
    MeSH term(s) Adolescent ; Adult ; Autism Spectrum Disorder/diagnosis ; Autism Spectrum Disorder/diagnostic imaging ; Brain/diagnostic imaging ; Child ; Female ; Humans ; Machine Learning ; Magnetic Resonance Imaging ; Male ; Models, Psychological ; Neuroimaging/methods ; ROC Curve ; Reproducibility of Results ; Young Adult
    Language English
    Publishing date 2022-02-23
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Validation Study
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-022-06459-2
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  4. Article ; Online: Longitudinal prognosis of Parkinson's outcomes using causal connectivity.

    Mellema, Cooper J / Nguyen, Kevin P / Treacher, Alex / Andrade, Aixa X / Pouratian, Nader / Sharma, Vibhash D / O'Suileabhain, Padraig / Montillo, Albert A

    NeuroImage. Clinical

    2024  Volume 42, Page(s) 103571

    Abstract: Despite the prevalence of Parkinson's disease (PD), there are no clinically-accepted neuroimaging biomarkers to predict the trajectory of motor or cognitive decline or differentiate Parkinson's disease from atypical progressive parkinsonian diseases. ... ...

    Abstract Despite the prevalence of Parkinson's disease (PD), there are no clinically-accepted neuroimaging biomarkers to predict the trajectory of motor or cognitive decline or differentiate Parkinson's disease from atypical progressive parkinsonian diseases. Since abnormal connectivity in the motor circuit and basal ganglia have been previously shown as early markers of neurodegeneration, we hypothesize that patterns of interregional connectivity could be useful to form patient-specific predictive models of disease state and of PD progression. We use fMRI data from subjects with Multiple System Atrophy (MSA), Progressive Supranuclear Palsy (PSP), idiopathic PD, and healthy controls to construct predictive models for motor and cognitive decline and differentiate between the four subgroups. Further, we identify the specific connections most informative for progression and diagnosis. When predicting the one-year progression in the MDS-UPDRS-III
    Language English
    Publishing date 2024-02-06
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 2701571-3
    ISSN 2213-1582 ; 2213-1582
    ISSN (online) 2213-1582
    ISSN 2213-1582
    DOI 10.1016/j.nicl.2024.103571
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  5. Article: Architectural configurations, atlas granularity and functional connectivity with diagnostic value in Autism Spectrum Disorder.

    Mellema, Cooper J / Treacher, Alex / Nguyen, Kevin P / Montillo, Albert

    Proceedings. IEEE International Symposium on Biomedical Imaging

    2020  Volume 2020, Page(s) 1022–1025

    Abstract: 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 ...

    Abstract 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 predictive models, such as 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 an accurate diagnostic 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. Identifying new important features extends our understanding of the biological underpinnings of ASD, while identifying features that corroborate past findings and extend across atlas levels instills model confidence. 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 whose abnormal development are associated with deficits in social and sensory processing common 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.
    Language English
    Publishing date 2020-05-22
    Publishing country United States
    Document type Journal Article
    ISSN 1945-7928
    ISSN 1945-7928
    DOI 10.1109/ISBI45749.2020.9098555
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  6. Book ; Online: Longitudinal Prognosis of Parkinsons Outcomes using Causal Connectivity

    Mellema, Cooper J. / Nguyen, Kevin P. / Treacher, Alex / Hernandez, Aixa Andrade / Montillo, Albert A.

    2022  

    Abstract: 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 ... ...

    Abstract 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.
    Keywords Quantitative Biology - Neurons and Cognition
    Subject code 610
    Publishing date 2022-06-21
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: Architectural configurations, atlas granularity and functional connectivity with diagnostic value in Autism Spectrum Disorder

    Mellema, Cooper J. / Treacher, Alex / Nguyen, Kevin P. / Montillo, Albert

    2019  

    Abstract: 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 ...

    Abstract 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.
    Keywords Computer Science - Machine Learning ; Quantitative Biology - Neurons and Cognition ; Statistics - Machine Learning
    Publishing date 2019-11-25
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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