Automated diagnosis of HIV-associated neurocognitive disorders using large-scale Granger causality analysis of resting-state functional MRI

Leveraging Brain Modularity Prior for Interpretable Representation Learning of fMRI

Resting-state functional magnetic resonance imaging (rs-fMRI) can reflect spontaneous neural activities in the brain and is widely used for brain disorder analysis. Previous studies focus on extracting fMRI representations using machine/deep learning methods, but these features typically lack biological interpretability. The human brain exhibits a remarkable modular structure in spontaneous brain functional networks, with each module comprised of functionally interconnected brain regions-of-interest (ROIs). However, existing learning-based methods cannot adequately utilize such brain modularity prior. In this paper, we propose a brain modularity-constrained dynamic representation learning framework for interpretable fMRI analysis, consisting of dynamic graph construction, dynamic graph learning via a novel modularity-constrained graph neural network (MGNN), and prediction and biomarker detection. The designed MGNN is constrained by three core neurocognitive modules (i.e., salience network, central executive network, and default mode network), encouraging ROIs within the same module to share similar representations. To further enhance discriminative ability of learned features, we encourage the MGNN to preserve network topology of input graphs via a graph topology reconstruction constraint. Experimental results on 534 subjects with rs-fMRI scans from two datasets validate the effectiveness of the proposed method. The identified discriminative brain ROIs and functional connectivities can be regarded as potential fMRI biomarkers to aid in clinical diagnosis.

Longitudinal prognosis of Parkinson's outcomes using causal connectivity

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-III1* and Montreal Cognitive assessment (MoCA), we achieve new state-of-the-art mean absolute error performance. Additionally, the balanced accuracy we achieve in the diagnosis of PD, MSA, PSP, versus healthy controls surpasses that attained in most clinics, underscoring the relevance of the brain connectivity features. Our models reveal the connectivity between deep nuclei, motor regions, and the thalamus as the most important for prediction. Collectively these results demonstrate the potential of fMRI connectivity as a prognostic biomarker for PD and increase our understanding of this disease.

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 (FC) 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 FC (ML.FC) which efficiently captures linear and nonlinear aspects.Approach.To capture directed information flow between brain regions, effective connectivity (EC) metrics, including dynamic causal modeling and structural equation modeling have been used. However, these methods are impractical to compute across the many regions of the whole brain. Therefore, we propose two new EC measures. The first, a machine learning based measure of effective connectivity (ML.EC), measures nonlinear aspects across the entire brain. The second, Structurally Projected Granger Causality (SP.GC) 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 ofreproducibilityand theability to predict individual traitsin 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 ofML.FCattains high reproducibility (mean intra-subjectR2of 0.44), while the proposed EC measure ofSP.GCattains the highest predictive power (meanR2across prediction tasks of 0.66).Significance.The proposed methods are highly suitable for achieving high reproducibility and predictiveness and demonstrate their strong potential for future neuroimaging studies.

Ladybug Beetle Optimization algorithm: application for real-world problems

In this paper, a novel optimization algorithm is proposed, called the Ladybug Beetle Optimization (LBO) algorithm, which is inspired by the behavior of ladybugs in nature when they search for a warm place in winter. The new proposed algorithm consists of three main parts: (1) determine the heat value in the position of each ladybug, (2) update the position of ladybugs, and (3) ignore the annihilated ladybug(s). The main innovations of LBO are related to both updating the position of the population, which is done in two separate ways, and ignoring the worst members, which leads to an increase in the search speed. Also, LBO algorithm is performed to optimize 78 well-known benchmark functions. The proposed algorithm has reached the optimal values of 73.3% of the benchmark functions and is the only algorithm that achieved the best solution of 20.5% of them. These results prove that LBO is substantially the best algorithm among other well-known optimization methods. In addition, two fundamentally different real-world optimization problems include the Economic-Environmental Dispatch Problem (EEDP) as an engineering problem and the Covid-19 pandemic modeling problem as an estimation and forecasting problem. The EEDP results illustrate that the proposed algorithm has obtained the best values in either the cost of production or the emission or even both, and the use of LBO for Covid-19 pandemic modeling problem leads to the least error compared to others.

Deep-Precognitive Diagnosis: Preventing Future Pandemics by Novel Disease Detection With Biologically-Inspired Conv-Fuzzy Network

Deep learning-based Computer-Aided Diagnosis has gained immense attention in recent years due to its capability to enhance diagnostic performance and elucidate complex clinical tasks. However, conventional supervised deep learning models are incapable of recognizing novel diseases that do not exist in the training dataset. Automated early-stage detection of novel infectious diseases can be vital in controlling their rapid spread. Moreover, the development of a conventional CAD model is only possible after disease outbreaks and datasets become available for training (viz. COVID-19 outbreak). Since novel diseases are unknown and cannot be included in training data, it is challenging to recognize them through existing supervised deep learning models. Even after data becomes available, recognizing new classes with conventional models requires a complete extensive re-training. The present study is the first to report this problem and propose a novel solution to it. In this study, we propose a new class of CAD models, i.e., Deep-Precognitive Diagnosis, wherein artificial agents are enabled to identify unknown diseases that have the potential to cause a pandemic in the future. A de novo biologically-inspired Conv-Fuzzy network is developed. Experimental results show that the model trained to classify Chest X-Ray (CXR) scans into normal and bacterial pneumonia detected a novel disease during testing, unseen by it in the training sample and confirmed to be COVID-19 later. The model is also tested on SARS-CoV-1 and MERS-CoV samples as unseen diseases and achieved state-of-the-art accuracy. The proposed model eliminates the need for model re-training by creating a new class in real-time for the detected novel disease, thus classifying it on all subsequent occurrences. Second, the model addresses the challenge of limited labeled data availability, which renders most supervised learning techniques ineffective and establishes that modified fuzzy classifiers can achieve high accuracy on image classification tasks.

Rates of cognitive impairment in a South African cohort of people with HIV: variation by definitional criteria and lack of association with neuroimaging biomarkers

There is wide variation in the reported prevalence of cognitive impairment in people with HIV (PWH). Part of this variation may be attributable to different studies using different methods of combining neuropsychological test scores to classify participants as either cognitively impaired or unimpaired. Our aim was to determine, in a South African cohort of PWH (N = 148), (a) how much variation in reported rates was due to method used to define cognitive impairment and (b) which method correlated best with MRI biomarkers of HIV-related brain pathology. Participants completed detailed neuropsychological assessment and underwent 3 T structural MRI and diffusion tensor imaging (DTI). We used the neuropsychological data to investigate 20 different methods of determining HIV-associated cognitive impairment. We used the neuroimaging data to obtain volumes for cortical and subcortical grey matter and total white matter and DTI metrics for several white matter tracts. Applying each of the 20 methods to the cognitive dataset resulted in a wide variation (20-97%) in estimated rates of impairment. Logistic regression models showed no method was associated with HIV-related neuroimaging abnormalities as measured by structural volumes or DTI metrics. We conclude that for the population from which this sample was drawn, much of the variation in reported rates of cognitive impairment in PWH is due to the method of classification used, and that none of these methods accurately reflects biological effects of HIV in the brain. We suggest that defining HIV-associated cognitive impairment using neuropsychological test performance only is insufficient; pre-morbid functioning, co-morbidities, cognitive symptoms, and functional impairment should always be considered.

COVID-19 in the Age of Artificial Intelligence: A Comprehensive Review

The recent COVID-19 pandemic, which broke at the end of the year 2019 in Wuhan, China, has infected more than 98.52 million people by today (January 23, 2021) with over 2.11 million deaths across the globe. To combat the growing pandemic on urgent basis, there is need to design effective solutions using new techniques that could exploit recent technology, such as machine learning, deep learning, big data, artificial intelligence, Internet of Things, for identification and tracking of COVID-19 cases in near real time. These technologies have offered inexpensive and rapid solution for proper screening, analyzing, prediction and tracking of COVID-19 positive cases. In this paper, a detailed review of the role of AI as a decisive tool for prognosis, analyze, and tracking the COVID-19 cases is performed. We searched various databases including Google Scholar, IEEE Library, Scopus and Web of Science using a combination of different keywords consisting of COVID-19 and AI. We have identified various applications, where AI can help healthcare practitioners in the process of identification and monitoring of COVID-19 cases. A compact summary of the corona virus cases are first highlighted, followed by the application of AI. Finally, we conclude the paper by highlighting new research directions and discuss the research challenges. Even though scientists and researchers have gathered and exchanged sufficient knowledge over last couple of months, but this structured review also examined technological perspectives while encompassing the medical aspect to help the healthcare practitioners, policymakers, decision makers, policymakers, AI scientists and virologists to quell this infectious COVID-19 pandemic outbreak.

Comparative study of machine learning methods for COVID-19 transmission forecasting

Within the recent pandemic, scientists and clinicians are engaged in seeking new technology to stop or slow down the COVID-19 pandemic. The benefit of machine learning, as an essential aspect of artificial intelligence, on past epidemics offers a new line to tackle the novel Coronavirus outbreak. Accurate short-term forecasting of COVID-19 spread plays an essential role in improving the management of the overcrowding problem in hospitals and enables appropriate optimization of the available resources (i.e., materials and staff).This paper presents a comparative study of machine learning methods for COVID-19 transmission forecasting. We investigated the performances of deep learning methods, including the hybrid convolutional neural networks-Long short-term memory (LSTM-CNN), the hybrid gated recurrent unit-convolutional neural networks (GAN-GRU), GAN, CNN, LSTM, and Restricted Boltzmann Machine (RBM), as well as baseline machine learning methods, namely logistic regression (LR) and support vector regression (SVR). The employment of hybrid models (i.e., LSTM-CNN and GAN-GRU) is expected to eventually improve the forecasting accuracy of COVID-19 future trends. The performance of the investigated deep learning and machine learning models was tested using confirmed and recovered COVID-19 cases time-series data from seven impacted countries: Brazil, France, India, Mexico, Russia, Saudi Arabia, and the US. The results reveal that hybrid deep learning models can efficiently forecast COVID-19 cases. Also, results confirmed the superior performance of deep learning models compared to the two considered baseline machine learning models. Furthermore, results showed that LSTM-CNN achieved improved performances with an averaged mean absolute percentage error of 3.718%, among others.

Functional brain network reorganization in HIV infection

BACKGROUND AND PURPOSE

To investigate the reorganization of the central nervous system provided by resting state-functional MRI (rs-fMRI), graph-theoretical analysis, and a newly developed functional brain network disruption index in patients with human immunodeficiency virus (HIV) infection.

METHODS

Forty HIV-positive patients without neurological impairment and 20 age- and sex-matched healthy controls underwent rs-fMRI at 3T; blood sampling was obtained the same day to evaluate biochemical variables (absolute, relative, and nadir CD4 T-lymphocytes value and plasmatic HIV-RNA). From fMRI data, disruption indices, as well as global and local graph theoretical measures, were estimated and examined for group differences (HIV vs. controls) as well as for associations with biochemical variables (HIV only). Finally, all data (global and local graph-theoretical measures, disruption indices, and biochemical variables) were tested for putative differences across three patient groups based on the duration of combined antiretroviral therapy (cART).

RESULTS

Brain function of HIV patients appeared to be deeply reorganized as compared to normal controls. The disruption index showed significant negative association with relative CD4 values, and a positive significant association between plasmatic HIV-RNA and local graph-theoretical metrics in the left lingual gyrus and the right lobule IV and V of right cerebellar hemisphere was also observed. Finally, a differential distribution of HIV clinical biomarkers and several brain metrics was observed across cART duration groups.

CONCLUSION

Our study demonstrates that rs-fMRI combined with advanced graph theoretical analysis and disruption indices is able to detect early and subtle functional changes of brain networks in HIV patients.

Connectome-based prediction of global cognitive performance in people with HIV

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Networks strengths predicted global cognitive performance in PWH.

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Model generalized to data from an independent PWH sample.

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Network strengths in PWH with HAND were different from either controls or PWH without HAND.

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Network strengths may serve as a potential biomarker to assist HAND diagnosis.

Global cognitive performance plays an important role in the diagnosis of HIV-associated neurocognitive disorders (HAND), yet to date, there is no simple way to measure global cognitive performance in people with HIV (PWH). Here, we performed connectome-based predictive modeling (CPM) to pursue a neural biomarker of global cognitive performance in PWH based on whole-brain resting-state functional connectivity. We built a CPM model that successfully predicted individual differences in global cognitive performance in the training set of 67 PWH by using leave-one-out cross-validation. This model generalized to both 33 novel PWH in the testing set and a subset of 39 PWH who completed a follow-up visit two years later. Furthermore, network strengths identified by the CPM model were significantly different between PWH with HAND and without HAND. Together, these results demonstrate that whole-brain functional network strengths could serve as a potential neural biomarker of global cognitive performance in PWH.

Machine learning prediction of neurocognitive impairment among people with HIV using clinical and multimodal magnetic resonance imaging data

Diagnosis of HIV-associated neurocognitive impairment (NCI) continues to be a clinical challenge. The purpose of this study was to develop a prediction model for NCI among people with HIV using clinical- and magnetic resonance imaging (MRI)-derived features. The sample included 101 adults with chronic HIV disease. NCI was determined using a standardized neuropsychological testing battery comprised of seven domains. MRI features included gray matter volume from high-resolution anatomical scans and white matter integrity from diffusion-weighted imaging. Clinical features included demographics, substance use, and routine laboratory tests. Least Absolute Shrinkage and Selection Operator Logistic regression was used to perform variable selection on MRI features. These features were subsequently used to train a support vector machine (SVM) to predict NCI. Three different classification tasks were performed: one used only clinical features; a second used only selected MRI features; a third used both clinical and selected MRI features. Model performance was evaluated by area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity with a tenfold cross-validation. The SVM classifier that combined selected MRI with clinical features outperformed the model using clinical features or MRI features alone (AUC: 0.83 vs. 0.62 vs. 0.79; accuracy: 0.80 vs. 0.65 vs. 0.72; sensitivity: 0.86 vs. 0.85 vs. 0.86; specificity: 0.71 vs. 0.37 vs. 0.52). Our results provide preliminary evidence that combining clinical and MRI features can increase accuracy in predicting NCI and could be developed as a potential tool for NCI diagnosis in HIV clinical practice.

Synergistic effects of HIV and marijuana use on functional brain network organization

HIV is associated with disruptions in cognition and brain function. Marijuana use is highly prevalent in HIV but its effects on resting brain function in HIV are unknown. Brain function can be characterized by brain activity that is correlated between regions over time, called functional connectivity. Neuropsychiatric disorders are increasingly being characterized by disruptions in such connectivity. We examined the synergistic effects of HIV and marijuana use on functional whole-brain network organization during resting state. Our sample included 78 adults who differed on HIV and marijuana status (19 with co-occurring HIV and marijuana use, 20 HIV-only, 17 marijuana-only, and 22 controls). We examined differences in local and long-range brain network organization using eight graph theoretical metrics: transitivity, local efficiency, within-module degree, modularity, global efficiency, strength, betweenness, and participation coefficient. Local and long-range connectivity were similar between the co-occurring HIV and marijuana use and control groups. In contrast, the HIV-only and marijuana-only groups were both associated with disruptions in brain network organization. These results suggest that marijuana use in HIV may normalize disruptions in brain network organization observed in persons with HIV. However, future work is needed to determine whether this normalization is suggestive of a beneficial or detrimental effect of marijuana on cognitive functioning in HIV.

A concise discussion on the potential spectral tools for the rapid COVID-19 detection

Developing robust methods to detect the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), a causative agent for the current global health pandemic, is an exciting area of research. Nevertheless, the currently used conventional reverse transcription-polymerase chain reaction (RT-PCR) technique in COVID-19 detection endures with some inevitable limitations. Consequently, the establishment of rapid diagnostic tools and quick isolation of infected patients is highly essential. Furthermore, the requirement of point-of-care testing is the need of the hour. Considering this, we have provided a brief review of the use of very recently reported robust spectral tools for rapid COVID-19 detection. The spectral tools include, colorimetric reverse transcription loop-mediated isothermal amplification (RT-LAMP) and matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS), with the admittance of principal component analysis (PCA) and machine learning (ML) for meeting the high-throughput and fool-proof platforms for the detection of SARS-CoV-2, are reviewed. Recently, these techniques have been readily applied to screen a large number of suspected patients within a short period and they demonstrated higher sensitivity for the detection of COVID-19 patients from unaffected human subjects.

Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review

BACKGROUND AND OBJECTIVE

During the recent global urgency, scientists, clinicians, and healthcare experts around the globe keep on searching for a new technology to support in tackling the Covid-19 pandemic. The evidence of Machine Learning (ML) and Artificial Intelligence (AI) application on the previous epidemic encourage researchers by giving a new angle to fight against the novel Coronavirus outbreak. This paper aims to comprehensively review the role of AI and ML as one significant method in the arena of screening, predicting, forecasting, contact tracing, and drug development for SARS-CoV-2 and its related epidemic.

METHOD

A selective assessment of information on the research article was executed on the databases related to the application of ML and AI technology on Covid-19. Rapid and critical analysis of the three crucial parameters, i.e., abstract, methodology, and the conclusion was done to relate to the model's possibilities for tackling the SARS-CoV-2 epidemic.

RESULT

This paper addresses on recent studies that apply ML and AI technology towards augmenting the researchers on multiple angles. It also addresses a few errors and challenges while using such algorithms in real-world problems. The paper also discusses suggestions conveying researchers on model design, medical experts, and policymakers in the current situation while tackling the Covid-19 pandemic and ahead.

CONCLUSION

The ongoing development in AI and ML has significantly improved treatment, medication, screening, prediction, forecasting, contact tracing, and drug/vaccine development process for the Covid-19 pandemic and reduce the human intervention in medical practice. However, most of the models are not deployed enough to show their real-world operation, but they are still up to the mark to tackle the SARS-CoV-2 epidemic.

Neuroinflammation associates with antioxidant heme oxygenase-1 response throughout the brain in persons living with HIV

Previous studies showed that persons living with HIV (PLWH) demonstrate higher brain prefrontal cortex neuroinflammation and immunoproteasome expression compared to HIV-negative individuals; these associate positively with HIV levels. Lower expression of the antioxidant enzyme heme oxygenase 1 (HO-1) was observed in PLWH with HIV-associated neurocognitive impairment (HIV-NCI) compared to neurocognitively normal PLWH. We hypothesized that similar expression patterns occur throughout cortical, subcortical, and brainstem regions in PLWH, and that neuroinflammation and immunoproteasome expression associate with lower expression of neuronal markers. We analyzed autopsied brains (15 regions) from 9 PLWH without HIV-NCI and 7 matched HIV-negative individuals. Using Western blot and RT-qPCR, we quantified synaptic, inflammatory, immunoproteasome, endothelial, and antioxidant biomarkers, including HO-1 and its isoform heme oxygenase 2 (HO-2). In these PLWH without HIV-NCI, we observed higher expression of neuroinflammatory, endothelial, and immunoproteasome markers in multiple cortical and subcortical regions compared to HIV-negative individuals, suggesting a global brain inflammatory response to HIV. Several regions, including posterior cingulate cortex, globus pallidus, and cerebellum, showed a distinct pattern of higher type I interferon (IFN)-stimulated gene and immunoproteasome expression. PLWH without HIV-NCI also had (i) stable or higher HO-1 expression and positive associations between (ii) HO-1 and HIV levels (CSF, plasma) and (iii) HO-1 expression and neuroinflammation, in multiple cortical, subcortical, and brainstem regions. We observed no differences in synaptic marker expression, suggesting little, if any, associated neuronal injury. We speculate that this may reflect a neuroprotective effect of a concurrent HO-1 antioxidant response despite global neuroinflammation, which will require further investigation.

Disruption of brain network organization in patients with human immunodeficiency virus (HIV) infection

In 2019, approximately 38 million people were living with human immunodeficiency virus (HIV). Combined antiretroviral therapy (cART) has determined a change in the course of HIV infection, transforming it into a chronic condition which results in cumulative exposure to antiretroviral drugs, inflammatory effects and aging. Relatedly, at least one quarter of HIV-infected patients suffer from cognitive, motor and behavioral disorder, globally known as HIV-associated neurocognitive disorders (HAND). In this context, objective, neuroimaging-based biomarkers are therefore highly desirable in order to detect, quantify and monitor HAND in all disease stages. In this study, we employed functional MRI in conjunction with graph-theoretical analysis as well as a newly developed functional brain network disruption index to assess a putative functional reorganization in HIV positive patients. We found that brain function of HIV patients is deeply reorganized as compared to normal controls. Interestingly, the regions in which we found reorganized hubs are integrated into neuronal networks involved in working memory, motor and executive functions often altered in patients with HAND. Overall, our study demonstrates that rs-fMRI combined with advanced graph theoretical analysis and disruption indices is able to detect early, subtle functional changes of brain networks in HIV patients before structural changes become evident.