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Finding new biomarkers to model Parkinson’s Disease (PD) is a challenge not only to help discerning between Healthy Control (HC) subjects and patients with potential PD but also as a way to measure quantitatively the loss of dopaminergic neurons mainly concentrated at substantia nigra. Within this context, this work presented here tries to provide a set of imaging features based on morphological characteristics extracted from I${}^{[123]}$-Ioflupane SPECT scans to discern between HC and PD participants in a balanced set of $386$ scans from Parkinson’s Progression Markers Initiative (PPMI) database. These features, obtained from isosurfaces of each scan at different intensity levels, have been classified through the use of classical Machine Learning classifiers such as Support-Vector-Machines (SVM) or Naïve Bayesian and compared with the results obtained using a Multi-Layer Perceptron (MLP).

The proposed system, based on a Mann–Whitney–Wilcoxon U-Test for feature selection and the SVM approach, yielded a $97.04\%$ balanced accuracy when the performance was evaluated using a $10$-fold cross-validation. This proves the reliability of these biomarkers, especially those related to sphericity, center of mass, number of vertices, 2D-projected perimeter or the 2D-projected eccentricity, among others, but including both internal and external isosurfaces.

Spatial normalization helps us to compare quantitatively two or more input brain scans. Although using an affine normalization approach preserves the anatomical structures, the neuroimaging field is more common to find works that make use of nonlinear transformations. The main reason is that they facilitate a voxel-wise comparison, not only when studying functional images but also when comparing MRI scans given that they fit better to a reference template. However, the amount of bias introduced by the nonlinear transformations can potentially alter the final outcome of a diagnosis especially when studying functional scans for neurological disorders like Parkinson’s Disease. In this context, we have tried to quantify the bias introduced by the affine and the nonlinear spatial registration of FP-CIT SPECT volumes of healthy control subjects and patients with PD. For that purpose, we calculated the deformation fields of each participant and applied these deformation fields to a 3D-grid. As the space between the edges of small cubes comprising the grid change, we can quantify which parts from the brain have been enlarged, compressed or just remain the same. When the nonlinear approach is applied, scans from PD patients show a region near their striatum very similar in shape to that of healthy subjects. This artificially increases the interclass separation between patients with PD and healthy subjects as the local intensity is decreased in the latter region, and leads machine learning systems to biased results due to the artificial information introduced by these deformations.

Parkinson’s Disease (PD) is the second most prevalent neurodegenerative disorder among adults. Although its triggers are still not clear, they may be due to a combination of different types of biomarkers measured through medical imaging, metabolomics, proteomics or genetics, among others. In this context, we have proposed a Computer-Aided Diagnosis (CAD) system that combines structural and functional imaging data from subjects in Parkinson’s Progression Markers Initiative dataset by means of an Ensemble Learning methodology trained to identify and penalize input sources with low classification rates and/ or high-variability. This proposal improves results published in recent years and provides an accurate solution not only from the point of view of image preprocessing (including a comparison between different intensity preservation techniques), but also in terms of dimensionality reduction methods (Isomap). In addition, we have also introduced a bagging classification schema for scenarios with unbalanced data.

As shown by our results, the CAD proposal is able to detect PD with $96.48\%$ of balanced accuracy, and opens up the possibility of combining any number of input data sources relevant for PD.

Neurodegenerative diseases pose a formidable challenge to medical research, demanding a nuanced understanding of their progressive nature. In this regard, latent generative models can effectively be used in a data-driven modeling of different dimensions of neurodegeneration, framed within the context of the manifold hypothesis. This paper proposes a joint framework for a multi-modal, common latent generative model to address the need for a more comprehensive understanding of the neurodegenerative landscape in the context of Parkinson’s disease (PD). The proposed architecture uses coupled variational autoencoders (VAEs) to joint model a common latent space to both neuroimaging and clinical data from the Parkinson’s Progression Markers Initiative (PPMI). Alternative loss functions, different normalization procedures, and the interpretability and explainability of latent generative models are addressed, leading to a model that was able to predict clinical symptomatology in the test set, as measured by the unified Parkinson’s disease rating scale (UPDRS), with R2 up to 0.86 for same-modality and 0.441 cross-modality (using solely neuroimaging). The findings provide a foundation for further advancements in the field of clinical research and practice, with potential applications in decision-making processes for PD. The study also highlights the limitations and capabilities of the proposed model, emphasizing its direct interpretability and potential impact on understanding and interpreting neuroimaging patterns associated with PD symptomatology.

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AUSTRALIA – Tamarind Derivative Repairs Damaged Brain Cells.

AUSTRALIA – High Hopes for Ross River Virus Vaccine.

AUSTRALIA – Genetic Link to Risk of Developing Bone Disease.

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CHINA – China Mulls New Mental Health Law.

CHINA – University of Pennsylvania Pairs up with Chinese Academy of Sciences for Neuroimaging.

INDIA – Vaccine Tests for Brain Virus.

KOREA – New Technique Makes Artificial Bones More Natural.

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SINGAPORE – IE Singapore Forms Biomedical R&D Consortium.

VIETNAM – Fatty Liver Disease on the Rise Among Youth.

OTHER REGIONS — New Clues to How Cancer Spreads.

OTHER REGIONS — Scientists Find Genes Linked to Migraines.

OTHER REGIONS — New Drug Makes Hearts Repair Themselves.

Aims: The purpose of the present case is to describe the functional outcome of a patient with human immunodeficiency virus (HIV) and progressive multifocal leukoencephalopathy (PML) on treatment with antiretroviral therapy using a multidisciplinary approach.

Methods: Neuropsychological tests and diffusion tensor imaging (DTI) were obtained at baseline and after 12 months to define the severity of white matter damage. Neuropsychological and neuroimaging data were compared to an HIV-infected patient without PML and with good immune system health, and to a second HIV-infected patient without PML but with notable immunosuppression.

Results: Review of the HIV/PML patient's cognitive data at both time points revealed significant impairments compared to the control subjects. Similarly, the HIV/PML patient's white matter lesion load and whole brain volume were markedly different from the control subjects at both time points. The tractography-defined metrics suggest significant white matter fiber loss associated with HIV/PML that was not evident in either HIV control patient.

Discussion: Our findings suggest that PML is associated with marked cognitive and neuroimaging abnormalities in the context of antiretroviral therapy.

Integrative Significance: To our knowledge this is the first study to integrate both quantitative DTI and cognitive assessment to define white matter damage associated with HIV and PML. This integrative approach provides a robust methodology to examine the integrity of brain systems mediating cognitive function.

There is little consensus about which objective markers should be used to assess major psychiatric disorders, and predict/evaluate treatment response for these disorders. Clinical practice relies instead on subjective signs and symptoms, such that there is a "translational gap" between research findings and clinical practice. This gap arises from: a) a lack of integrative theoretical models which provide a basis for understanding links between gene-brain-behavior mechanisms and clinical entities; b) the reliance on studying one measure at a time so that linkages between markers are their specificity are not established; and c) the lack of a definitive understanding of what constitutes normative function. Here, we draw on a standardized methodology for acquiring multiple sources of genomic, brain and behavioral data in the same subjects, to propose candidate markers of selected psychiatric disorders: depression, post-traumatic stress disorder, schizophrenia, attention-deficit/hyperactivity disorder and dementia disorders. This methodology has been used to establish a standardized international database which provides a comprehensive framework and the basis for testing hypotheses derived from an integrative theoretical model of the brain. Using this normative base, we present preliminary findings for a number of disorders in relation to the proposed markers. Establishing these objective markers will be the first step towards determining their sensitivity, specificity and treatment prediction in individual patients.

Language perception comprises mechanisms of perception and discrimination of auditory stimuli. An important component of auditory perception and discrimination concerns auditory objects. Many interesting auditory objects in our environment are of relatively long duration; however, the temporal window of integration of auditory cortex neurons processing these objects is very limited. Thus, it is necessary to make active use of short-term memory in order to construct and temporarily store long-duration objects. We sought to understand the mechanisms by which the brain manipulates long-duration tonal patterns, temporarily stores the segments of those patterns, and integrates them into an auditory object. We extended a previously constructed model of auditory recognition of short-duration tonal patterns by expanding the prefrontal cortically-based short-term memory module of the previous model into a memory buffer with multiple short-term memory submodules and by adding a gating module. The gating module distributes the segments of the input pattern to separate locations of the extended prefrontal cortex in an orderly fashion, allowing a subsequent comparison of the stored segments against the segments of a second pattern. In addition to simulating behavioral data and electrical activity of neurons, our model also produces simulations of the blood oxygen level dependent (BOLD) signal as obtained in fMRI studies. The results of these simulations provided us with predictions that we tested in an fMRI experiment with normal volunteers. This fMRI experiment used the same task and similar stimuli to that of the model. We compared simulated data with experimental values. We found that two brain areas, the right precentral gyrus and the left medial frontal gyrus, correlated well with our simulations of the memory gating module. Other fMRI studies of auditory perception and discrimination have also found correlation of fMRI activation of those areas with similar tasks and thus provide further support to our findings.

The diverse nature of cerebral activity, as measured using neuroimaging techniques, has been recognised long ago. It seems obvious that using single modality recordings can be limited when it comes to capturing its complex nature. Thus, it has been argued that moving to a multimodal approach will allow neuroscientists to better understand the dynamics and structure of this activity. This means that integrating information from different techniques, such as electroencephalography (EEG) and the blood oxygenated level dependent (BOLD) signal recorded with functional magnetic resonance imaging (fMRI), represents an important methodological challenge. In this work, we review the work that has been done thus far to derive EEG/fMRI integration approaches. This leads us to inspect the conditions under which such an integration approach could work or fail, and to disclose the types of scientific questions one could (and could not) hope to answer with it.

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) provide complementary noninvasive information of brain activity, and EEG/fMRI fusion can achieve higher spatiotemporal resolution than each modality separately. This focuses on independent component analysis (ICA)-based EEG/fMRI fusion. In order to appreciate the issues, we first describe the potential and limitations of the developed fusion approaches: fMRI-constrained EEG imaging, EEG-informed fMRI analysis, and symmetric fusion. We then outline some newly developed hybrid fusion techniques using ICA and the combination of data-/model-driven methods, with special mention of the spatiotemporal EEG/fMRI fusion (STEFF). Finally, we discuss the current trend in methodological development and the existing limitations for extrapolating neural dynamics.

We demonstrate the use of support vector machine methods to classify autism neuroimaging data collected from multiple sites.

Background: Acupuncture has been evaluated in functional magnetic resonance imaging (fMRI) studies to determine its modulating effect on neuronal activity in the central nervous system. However, the methodology and results vary across reported studies. A review of this research helps illuminate the possible mechanisms of action of acupuncture. Methods: Clinical studies of acupuncture with fMRI technology, published in English language, are reviewed. The data are summarized and discussed. Results: No two studies showed identical results, even when the same acupuncture points were used. One common finding is that acupuncture is associated with functional signal changes in somatosensory areas and the limbic system. How the needle is stimulated also appears important, as it causes varying patterns of activation and deactivation. Direct comparison across studies is difficult due to absence of stable methodology across studies. Conclusion: fMRI technology opens a new field of acupuncture research. Studies conducted to date provide insight into how acupuncture renders its physiologic effects. Better and less variable study design, plus additional data from several study groups, are needed to realize the full potential of this powerful neuroscience research tool in understanding the mechanisms behind acupuncture activity.

Alzheimer’s disease (AD) is a neurodegenerative disorder with few biomarkers even though it impacts a relatively large portion of the population and is predicted to affect significantly more individuals in the future. Neuroimaging has been used in concert with genetic information to improve our understanding in relation to how AD arises and how it can be potentially diagnosed. Additionally, evidence suggests synonymous variants can have a functional impact on gene regulatory mechanisms, including those related to AD. Some synonymous codons are preferred over others leading to a codon bias. The bias can arise with respect to codons that are more or less frequently used in the genome. A bias can also result from optimal and non-optimal codons, which have stronger and weaker codon anti-codon interactions, respectively. Although association tests have been utilized before to identify genes associated with AD, it remains unclear how codon bias plays a role and if it can improve rare variant analysis. In this work, rare variants from whole-genome sequencing from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort were binned into genes using BioBin. An association analysis of the genes with AD-related neuroimaging biomarker was performed using SKAT-O. While using all synonymous variants we did not identify any genomewide significant associations, using only synonymous variants that affected codon frequency we identified several genes as significantly associated with the imaging phenotype. Additionally, significant associations were found using only rare variants that contains an optimal codon in among minor alleles and a non-optimal codon in the major allele. These results suggest that codon bias may play a role in AD and that it can be used to improve detection power in rare variant association analysis.

Autism Spectrum Disorder (ASD) encompasses a range of developmental disabilities marked by differences in social functioning, cognition, and behavior. Both genetic and environmental factors are known to contribute to ASD, yet the exact etiological factors remain unclear. Developing integrative models to explore the effects of gene expression on behavioral and cognitive traits attributed to ASD can uncover environmental and genetic interactions. A notable aspect of ASD research is the sex-wise diagnostic disparity: males are diagnosed more frequently than females, which suggests potential sex-specific biological influences. Investigating neuronal microstructure, particularly axonal conduction velocity offers insights into the neural basis of ASD. Developing robust models that evaluate the vast multidimensional datasets generated from genetic and microstructural processing poses significant challenges. Traditional feature selection techniques have limitations; thus, this research aims to integrate principal component analysis (PCA) with supervised machine learning algorithms to navigate the complex data space. By leveraging various neuroimaging techniques and transcriptomics data analysis methods, this methodology builds on traditional implementations of PCA to better contextualize the complex genetic and phenotypic heterogeneity linked to sex differences in ASD and pave the way for tailored interventions.

Illness related brain effects of neuropsychiatric disorders are not regionally uniform, with some regions showing large pathological effects while others are relatively spared. Presently, Big Data meta-analytic studies tabulate these effects using structural and/or functional brain atlases that are based on the anatomical boundaries, landmarks and connectivity patterns in healthy brains. These patterns are then translated to individual level predictors using approaches such as Regional Vulnerability Index (RVI), which quantifies the agreement between individual brain patterns and the canonical pattern found in the illness. However, the atlases from healthy brains are unlikely to align with deficit pattern expressed in specific disorders such as Major Depressive Disorder (MDD), thus reducing the statistical power for individualized predictions. Here, we evaluated a novel approach, where disorder specific templates are constructed using the Kullback-Leibler (KL) distance to balance granularity, signal-to-noise ratio and the contrast between regional effect sizes to maximize translatability of the population-wide illness pattern at the level of the individual. We used regional homogeneity (ReHo) maps extracted from resting state functional MRI for N = 2, 289 MDD sample (mean age ± s.d.: 63.2 ± 7.2 years) and N = 6104 control subjects (mean age ± s.d.: 62.9 ± 7.2 years) who were free of MDD and any other mental condition. The cortical effects of MDD were analyzed on the 3D spherical surfaces representing cerebral hemispheres. KL-distance was used to organize the cortical surface into 28 regions of interest based on effect sizes, connectivity and signal-to-noise ratio. The RVI values calculated using this novel approach showed significantly higher effect size of the illness than these calculated using standard Desikan brain atlas.

Alzheimer’s disease (AD) is a neurodegenerative disorder that results in progressive cognitive decline but without any clinically validated cures so far. Understanding the progression of AD is critical for early detection and risk assessment for AD in aging individuals, thereby enabling initiation of timely intervention and improved chance of success in AD trials. Recent pseudotime approach turns cross-sectional data into “faux” longitudinal data to understand how a complex process evolves over time. This is critical for Alzheimer, which unfolds over the course of decades, but the collected data offers only a snapshot. In this study, we tested several state-of-the-art pseudotime approaches to model the full spectrum of AD progression. Subsequently, we evaluated and compared the pseudotime progression score derived from individual imaging modalities and multi-modalities in the ADNI cohort. Our results showed that most existing pseudotime analysis tools do not generalize well to the imaging data, with either flipped progression score or poor separation of diagnosis groups. This is likely due to the underlying assumptions that only stand for single cell data. From the only tool with promising results, it was observed that all pseudotime, derived from either single imaging modalities or multi-modalities, captures the progressiveness of diagnosis groups. Pseudotime from multi-modality, but not the single modalities, confirmed the hypothetical temporal order of imaging phenotypes. In addition, we found that multi-modal pseudotime is mostly driven by amyloid and tau imaging, suggesting their continuous changes along the full spectrum of AD progression.