How Machine Learning is Powering Neuroimaging to Improve Brain Health

Singh, Narendra; Harrod, Jordan B.; Subramanian, Sandya; Robinson, Mitchell B.; Chang, Ken; Cetin-Karayumak, Suheyla; Dalca, Adrian V.; Eickhoff, Simon B.; Fox, Michael; Franke, Loraine; Golland, Polina; Haehn, Daniel; Iglesias, Juan Eugenio; O’Donnell, Lauren J.; Ou, Yangming; Rathi, Yogesh; Siddiqi, Shan; Sun, Haoqi; Westover, M. Brandon; Whitfield-Gabrieli, Susan; Gollub, Randy L.

doi:10.1007/s12021-022-09572-9

Cited by 27 publications

(14 citation statements)

References 166 publications

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“…Further longitudinal studies with longer follow-ups are needed to evaluate clinical consequences (e.g., initial infection vs. reinfection, prevaccination vs. postvaccination COVID infection) and neuroabnormalities [ 139 , 140 ] as well as other regional implications of neurological relevance (e.g., spinal involvement). Studies have reported the increasing adoption of machine learning techniques in the medical field due to their high accuracy [ 141 , 142 ]. Therefore, future work should include machine learning algorithms to predict the impact of COVID-19 on affected brain and spinal regions [ 143 ].…”

Section: Discussionmentioning

confidence: 99%

Topographical Distribution of Neuroanatomical Abnormalities Following COVID-19 Invasion

Kiyak,

Ijezie,

Ackah

et al. 2023

Clin Neuroradiol

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Purpose This systematic review is aimed at synthesising the literature base to date on the frequency and topographical distribution of neuroanatomical changes seen on imaging following COVID-19 invasion with a focus on both the acute and chronic phases of the disease. Methods In this study, 8 databases were systematically searched to identify relevant articles published from December 2019 to March 2022 and supplemented with a manual reference search. Data were extracted from the included studies and narrative synthesis was employed to integrate the findings. Results A total of 110 studies met the inclusion criteria and comprised 119,307 participants (including 31,073 acute and 143 long COVID-19 patients manifesting neurological alterations) and controls. Considerable variability in both the localisation and nature of neuroanatomical abnormalities are noted along the continuum with a wide range of neuropathologies relating to the cerebrovascular/neurovascular system, (sub)cortical structures (including deep grey and white matter structures), brainstem, and predominant regional and/or global alterations in the cerebellum with varying degrees of spinal involvement. Conclusion Structural regional alterations on neuroimaging are frequently demonstrated in both the acute and chronic phases of SARS-CoV‑2 infection, particularly prevalent across subcortical, prefrontal/frontal and cortico-limbic brain areas as well as the cerebrovascular/neurovascular system. These findings contribute to our understanding of the acute and chronic effects of the virus on the nervous system and has the potential to provide information on acute and long-term treatment and neurorehabilitation decisions.

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Section: Discussionmentioning

confidence: 99%

Topographical Distribution of Neuroanatomical Abnormalities Following COVID-19 Invasion

Kiyak,

Ijezie,

Ackah

et al. 2023

Clin Neuroradiol

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“…In recent years, machine-learning (ML) approaches as a branch of artificial intelligence have been used in clinical applications to facilitate predictive diagnoses and thereby help treatment plans ( Ahmed et al, 2020 ). In neuroscience, ML algorithms have shown great promise in combining multimodal neuroimaging data and analyzing brain structural and functional alteration at the individual level, suggesting their high translational potential clinically ( Janssen et al, 2018 ; Senders et al, 2018 ; Singh et al, 2022 ). The support vector machine (SVM), one of the popular supervised ML algorithms, recently has been increasingly used in the classification of neurodegenerative diseases applying to a range of MRI modalities and promising superior classification performance.…”

Section: Introductionmentioning

confidence: 99%

Identification of chronic mild traumatic brain injury using resting state functional MRI and machine learning techniques

et al. 2023

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Mild traumatic brain injury (mTBI) is a major public health concern that can result in a broad spectrum of short-term and long-term symptoms. Recently, machine learning (ML) algorithms have been used in neuroscience research for diagnostics and prognostic assessment of brain disorders. The present study aimed to develop an automatic classifier to distinguish patients suffering from chronic mTBI from healthy controls (HCs) utilizing multilevel metrics of resting-state functional magnetic resonance imaging (rs-fMRI). Sixty mTBI patients and forty HCs were enrolled and allocated to training and testing datasets with a ratio of 80:20. Several rs-fMRI metrics including fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), degree centrality (DC), voxel-mirrored homotopic connectivity (VMHC), functional connectivity strength (FCS), and seed-based FC were generated from two main analytical categories: local measures and network measures. Statistical two-sample t-test was employed comparing between mTBI and HCs groups. Then, for each rs-fMRI metric the features were selected extracting the mean values from the clusters showing significant differences. Finally, the support vector machine (SVM) models based on separate and multilevel metrics were built and the performance of the classifiers were assessed using five-fold cross-validation and via the area under the receiver operating characteristic curve (AUC). Feature importance was estimated using Shapley additive explanation (SHAP) values. Among local measures, the range of AUC was 86.67–100% and the optimal SVM model was obtained based on combined multilevel rs-fMRI metrics and DC as a separate model with AUC of 100%. Among network measures, the range of AUC was 80.42–93.33% and the optimal SVM model was obtained based on the combined multilevel seed-based FC metrics. The SHAP analysis revealed the DC value in the left postcentral and seed-based FC value between the motor ventral network and right superior temporal as the most important local and network features with the greatest contribution to the classification models. Our findings demonstrated that different rs-fMRI metrics can provide complementary information for classifying patients suffering from chronic mTBI. Moreover, we showed that ML approach is a promising tool for detecting patients with mTBI and might serve as potential imaging biomarker to identify patients at individual level.Clinical trial registration[clinicaltrials.gov], identifier [NCT03241732].

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“…In addition to the applications for prediction and diagnostics in healthcare 10 – 14 , ML for brain imaging has application possibilities in the contexts of learning and education 7 , 2 . For decades, scientists have studied the brain processes during cognitive tasks, like mathematics or language.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear and machine learning analyses on high-density EEG data of math experts and novices

Poikonen

Zaluska

Wang

et al. 2023

Sci Rep

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Current trend in neurosciences is to use naturalistic stimuli, such as cinema, class-room biology or video gaming, aiming to understand the brain functions during ecologically valid conditions. Naturalistic stimuli recruit complex and overlapping cognitive, emotional and sensory brain processes. Brain oscillations form underlying mechanisms for such processes, and further, these processes can be modified by expertise. Human cortical functions are often analyzed with linear methods despite brain as a biological system is highly nonlinear. This study applies a relatively robust nonlinear method, Higuchi fractal dimension (HFD), to classify cortical functions of math experts and novices when they solve long and complex math demonstrations in an EEG laboratory. Brain imaging data, which is collected over a long time span during naturalistic stimuli, enables the application of data-driven analyses. Therefore, we also explore the neural signature of math expertise with machine learning algorithms. There is a need for novel methodologies in analyzing naturalistic data because formulation of theories of the brain functions in the real world based on reductionist and simplified study designs is both challenging and questionable. Data-driven intelligent approaches may be helpful in developing and testing new theories on complex brain functions. Our results clarify the different neural signature, analyzed by HFD, of math experts and novices during complex math and suggest machine learning as a promising data-driven approach to understand the brain processes in expertise and mathematical cognition.

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How Machine Learning is Powering Neuroimaging to Improve Brain Health

Cited by 27 publications

References 166 publications

Topographical Distribution of Neuroanatomical Abnormalities Following COVID-19 Invasion

Topographical Distribution of Neuroanatomical Abnormalities Following COVID-19 Invasion

Identification of chronic mild traumatic brain injury using resting state functional MRI and machine learning techniques

Nonlinear and machine learning analyses on high-density EEG data of math experts and novices

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