Biomarkers For Brain Disorders Research Articles

Intra-Atlas Node Size Effects on Graph Metrics in fMRI Data: Implications for Alzheimer's Disease and Cognitive Impairment.

Network neuroscience, a multidisciplinary field merging insights from neuroscience and network theory, offers a profound understanding of neural network intricacies. However, the impact of varying node sizes on computed graph metrics in neuroimaging data remains underexplored. This study addresses this gap by adopting a data-driven methodology to delineate functional nodes and assess their influence on graph metrics. Using the Neuromark framework, automated independent component analysis is applied to resting state fMRI data, capturing functional network connectivity (FNC) matrices. Global and local graph metrics reveal intricate connectivity patterns, emphasizing the need for nuanced analysis. Notably, node sizes, computed based on voxel counts, contribute to a novel metric termed 'node-metric coupling' (NMC). Correlations between graph metrics and node dimensions are consistently observed. The study extends its analysis to a dataset comprising Alzheimer's disease, mild cognitive impairment, and control subjects, showcasing the potential of NMC as a biomarker for brain disorders. The two key outcomes underscore the interplay between node sizes and resultant graph metrics within a given atlas, shedding light on an often-overlooked source of variability. Additionally, the study highlights the utility of NMC as a valuable biomarker, emphasizing the necessity of accounting for node sizes in future neuroimaging investigations. This work contributes to refining comparative studies employing diverse atlases and advocates for thoughtful consideration of intra-atlas node size in shaping graph metrics, paving the way for more robust neuroimaging research.

Rational analysis of data from LC-MS/MS: new insights in acylcarnitines as biomarkers for brain disorders or neurotoxicity.

LC-MS/MS-based metabolomics is an important tool for studying disease-related biomarkers. Conventionally, different strategies have been used to screen biomarkers. However, many studies for biomarker screening by different strategies have ignored the dose-response relationship between the biomarker level and exposure level, and no relevant studies have described and compared different strategies in detail. Phenobarbital (PHB) which belongs to the barbiturates, was selected as the typical representative of neurotoxins. Acylcarnitines have been promising candidates for diagnostic biomarkers for several neurological disorders and neurotoxicity. In this work, we aimed to use an acute PHB poisoning animal model to clarify PHB poisoning effects on plasma and brain acylcarnitine changes and how to rationally analyze data from LC-MS/MS. The acylcarnitine profiles in plasma and brain regions in an actuate PHB poisoning animal model were utilized. The dose-response relationship between plasma PHB and carnitine and acylcarnitines (CARs) in plasma and brain were assessed by the variance analysis trend test and Spearman's rank correlation test. In different strategies, principal component analysis (PCA) and partial least squares discriminant analysis (OPLS-DA) screened the differential CARs, variable importance plots (VIPs) were utilized to select putative biomarkers for PHB-induced toxicity, and receiver operating characteristic (ROC) curve analysis then illustrated the reliability of biomarkers. Under the first strategy, 14 potential toxicity biomarkers were obtained including eight downregulated CARs with AUC >0.8. Under the second strategy, 11 potential toxicity biomarkers were obtained containing five downregulated CARs with AUC >0.8. Only when the dose-response relationship was fully considered, different strategies screen for the same biomarkers (plasma acetyl-carnitine (C2) and plasma decanoyl-carnitine (C10)), which indicated plasma acylcarnitines might serve as toxicity biomarkers. In addition, the plasma CAR level changes showed differences from brain CAR level changes, and correlations between plasma CARs and their brain counterparts were weak. We found that plasma C2 and C10 might serve as toxicity biomarkers for PHB poisoning disorders, and PHB poisoning effects on changes in plasma CARs may not be fully representative of changes in brain CARs.

A new diagnostic tool for brain disorders: extracellular vesicles derived from neuron, astrocyte, and oligodendrocyte.

Brain disorders are the leading cause of disability worldwide, affecting people's quality of life and causing economic burdens. The current clinical diagnosis of brain disorders relies solely on individual phenotypes and lacks accurate molecular biomarkers. An emerging field of research centers around extracellular vesicles (EVs), nanoscale membrane vesicles which can easily cross the blood-brain barrier. EVs in the blood are derived from various tissues, including the brain. Therefore, purifying central nervous system (CNS)-derived EVs from the blood and analyzing their contents may be a relatively non-invasive way to analyze brain molecular alterations and identify biomarkers in brain disorders. Recently, methods for capturing neuron-derived EVs (NDEs), astrocyte-derived EVs (ADEs), and oligodendrocyte-derived EVs (ODEs) in peripheral blood were reported. In this article, we provide an overview of the research history of EVs in the blood, specifically focusing on biomarker findings in six major brain disorders (Alzheimer's disease, Parkinson's disease, schizophrenia, bipolar disorder, depression, and autism spectrum disorder). Additionally, we discuss the methodology employed for testing CNS-derived EVs. Among brain disorders, Alzheimer's disease has received the most extensive attention in EV research to date. Most studies focus on specific molecules, candidate proteins, or miRNAs. Notably, the most studied molecules implicated in the pathology of these diseases, such as Aβ, tau, and α-synuclein, exhibit good reproducibility. These findings suggest that CNS-derived EVs can serve as valuable tools for observing brain molecular changes minimally invasively. However, further analysis is necessary to understand the cargo composition of these EVs and improve isolation methods. Therefore, research efforts should prioritize the analysis of CNS-derived EVs' origin and genome-wide biomarker discovery studies.

MiR-218-5p and miR-320a-5p as Biomarkers for Brain Disorders: Focus on the Major Depressive Disorder and Parkinson's Disease.

Depression is one of the early and most persistent non-motor symptoms of Parkinson's disease (PD), which remains ignored, resulting in the underdiagnosis of PD. Unfortunately, scarce studies and the non-availability of diagnostic strategies cause countless complications, highlighting the need for appropriate diagnostic biomarkers. Recently, brain-enriched miRNAs regulating vital neurological functions have been proposed as potent biomarkers for therapeutic strategies. Therefore, the present study is aimed to identify the brain-enriched miR-218-5p and miR-320-5p in the serum of the Chinese depressed PD patients (n = 51) than healthy controls (n = 51) to identify their potency as biomarkers. For this purpose, depressive PD patients were recruited based on HAMA and HAMD scores and miR-218-5p and miR-320-5p and IL-6, and S100B levels were analyzed using real-time PCR (qRT-PCR) and ELISA assay, respectively. In silico analysis was performed to identify key biological pathways and hub genes involved in the psychopathology of depression in PD. Here, we found significantly downregulated miR-218-5p and miR-320-5p following higher levels of IL-6 and S100B in depressed PD patients than in control (p < 0.05). The correlation analysis revealed that both miRNAs were negatively correlated with HAMA and HAMD, and IL-6 scores, along with a positive correlation with PD duration and LEDD medication. ROC analysis showed AUC above 75% in both miRNAs in depressed PD patients, and in silico analysis revealed that both miRNA's targets regulate key neurological pathways such as axon guidance, dopaminergic synapse, and circadian rhythm. Additional analysis revealed PIK3R1, ATRX, BM1, PCDHA10, XRCC5, PPP1CB, MLLT3, CBL, PCDHA4, PLCG1, YWHAZ, CDH2, AGO3, PCDHA3, and PCDHA11 as hub-genes in PPI network. In summary, our findings show that miR-218-5p and miR-320-5p can be utilized as future biomarkers for depression in PD patients, which may aid in the early diagnosis and treatment of Parkinson's disease.

Multitask fMRI Data Classification via Group-Wise Hybrid Temporal and Spatial Sparse Representations.

Task-based functional magnetic resonance imaging (tfMRI) has been widely used to induce functional brain activities corresponding to various cognitive tasks. A relatively under-explored question is whether there exist fundamental differences in fMRI signal composition patterns that can effectively classify the task states of tfMRI data, furthermore, whether there exist key functional components in characterizing the diverse tfMRI signals. Recently, fMRI signal composition patterns of multiple tasks have been investigated via deep learning models, where relatively large populations of fMRI datasets are indispensable and the neurologic meaning of their results is elusive. Thus, the major challenges arise from the high dimensionality, low signal-to-noise ratio, interindividual variability, a small sample size of fMRI data, and the explainability of classification results. To address the above challenges, we proposed a computational framework based on group-wise hybrid temporal and spatial sparse representations (HTSSR) to identify and differentiate multitask fMRI signal composition patterns. Using relatively small cohorts of Human Connectome Project (HCP) tfMRI data as test-bed, the experimental results demonstrated that the multitask of fMRI data can be successfully classified with an average accuracy of 96.67%, where the key components in differentiating the multitask can be characterized, suggesting the effectiveness and explainability of the proposed method. Moreover, both task-related components and resting-state networks (RSNs) can be reliably detected. Therefore, our study proposed a novel framework that identifies the interpretable and discriminative fMRI composition patterns and can be potentially applied for controlling fMRI data quality and inferring biomarkers in brain disorders with small sample neuroimaging datasets.

Visual gamma oscillations predict sensory sensitivity in females as they do in males

Gamma oscillations are driven by local cortical excitatory (E)–inhibitory (I) loops and may help to characterize neural processing involving excitatory-inhibitory interactions. In the visual cortex reliable gamma oscillations can be recorded with magnetoencephalography (MEG) in the majority of individuals, which makes visual gamma an attractive candidate for biomarkers of brain disorders associated with E/I imbalance. Little is known, however, about if/how these oscillations reflect individual differences in neural excitability and associated sensory/perceptual phenomena. The power of visual gamma response (GR) changes nonlinearly with increasing stimulation intensity: it increases with transition from static to slowly drifting high-contrast grating and then attenuates with further increase in the drift rate. In a recent MEG study we found that the GR attenuation predicted sensitivity to sensory stimuli in everyday life in neurotypical adult men and in men with autism spectrum disorders. Here, we replicated these results in neurotypical female participants. The GR enhancement with transition from static to slowly drifting grating did not correlate significantly with the sensory sensitivity measures. These findings suggest that weak velocity-related attenuation of the GR is a reliable neural concomitant of visual hypersensitivity and that the degree of GR attenuation may provide useful information about E/I balance in the visual cortex.

Molecular landscape of long noncoding RNAs in brain disorders.

According to current paradigms, various risk factors, such as genetic mutations, oxidative stress, neural network dysfunction, and abnormal protein degradation, contribute to the progression of brain disorders. Through the cooperation of gene transcripts in biological processes, the study of noncoding RNAs can lead to insights into the cause and treatment of brain disorders. Recently, long noncoding RNAs (lncRNAs) which are longer than 200 nucleotides in length have been suggested as key factors in various brain disorders. Accumulating evidence suggests the potential of lncRNAs as diagnostic or prognostic biomarkers and therapeutic targets. High-throughput screening-based sequencing has been instrumental in identification of lncRNAs that demand new approaches to understanding the progression of brain disorders. In this review, we discuss the recent progress in the study of lncRNAs, and addresses the pathogenesis of brain disorders that involve lncRNAs and describes the associations of lncRNAs with neurodegenerative disorders such as Alzheimer disease (AD), Parkinson disease (PD), and neurodevelopmental disorders. We also discuss potential targets of lncRNAs and their promise as novel therapeutics and biomarkers in brain disorders.

Mining cross-frequency coupling microstates from resting state MEG: An application to mild traumatic brain injury.

Recent studies have investigated the possible role of dynamic functional connectivity and the role of cross-frequency coupling (CFC) to provide the substrate for reliable biomarkers of brain disorders. In this study, we analyzed time-varying CFC profiles from resting state Magnetoencephal-ographic recordings of 30 mild Traumatic Brain Injury (mTBI) patients and 50 normal controls. Interactions among sensors at specific pairs of frequency bands were computed via estimation of phase-to-amplitude couplings. We then computed time-varying functional connectivity graphs that were described in terms of segregation (local efficiency, LE) and integration (global efficiency, GE) and mapped those graphs to time series of GE/LE estimates. The resulting dynamic network revealed transitions between a limited number of microstates for mTBI subjects compared to controls. The significant differences in transition probability between the two groups, along with the limited repertoire of possible states, can form the basis for a robust dynamic connectomic biomarker for the diagnosis of mTBI.

Spontaneous pupil dilations during the resting state are associated with activation of the salience network

Resting state functional magnetic resonance imaging (rs-fMRI) is increasingly applied for the development of functional biomarkers in brain disorders. Recent studies have revealed spontaneous vigilance drifts during the resting state, involving changes in brain activity and connectivity that challenge the validity of uncontrolled rs-fMRI findings. In a combined rs-fMRI/eye tracking study, the pupil size of 32 healthy subjects after 2h of sleep restriction was recorded as an indirect index for activity of the locus coeruleus, the brainstem's noradrenergic arousal center. The spontaneous occurrence of pupil dilations, but not pupil size per se, was associated with increased activity of the salience network, thalamus and frontoparietal regions. In turn, spontaneous constrictions of the pupil were associated with increased activity in visual and sensorimotor regions. These results were largely replicated in a sample of 36 healthy subjects who did not undergo sleep restriction, although in this sample the correlation between thalamus and pupil dilation fell below whole-brain significance. Our data show that spontaneous pupil fluctuations during rest are indeed associated with brain circuitry involved in tonic alertness and vigilance. Pupillometry is an effective method to control for changes in tonic alertness during rs-fMRI.

Single-subject morphological brain networks: connectivity mapping, topological characterization and test-retest reliability.

IntroductionStructural MRI has long been used to characterize local morphological features of the human brain. Coordination patterns of the local morphological features among regions, however, are not well understood. Here, we constructed individual‐level morphological brain networks and systematically examined their topological organization and long‐term test–retest reliability under different analytical schemes of spatial smoothing, brain parcellation, and network type.MethodsThis study included 57 healthy participants and all participants completed two MRI scan sessions. Individual morphological brain networks were constructed by estimating interregional similarity in the distribution of regional gray matter volume in terms of the Kullback–Leibler divergence measure. Graph‐based global and nodal network measures were then calculated, followed by the statistical comparison and intra‐class correlation analysis.ResultsThe morphological brain networks were highly reproducible between sessions with significantly larger similarities for interhemispheric connections linking bilaterally homotopic regions. Further graph‐based analyses revealed that the morphological brain networks exhibited nonrandom topological organization of small‐worldness, high parallel efficiency and modular architecture regardless of the analytical choices of spatial smoothing, brain parcellation and network type. Moreover, several paralimbic and association regions were consistently revealed to be potential hubs. Nonetheless, the three studied factors particularly spatial smoothing significantly affected quantitative characterization of morphological brain networks. Further examination of long‐term reliability revealed that all the examined network topological properties showed fair to excellent reliability irrespective of the analytical strategies, but performing spatial smoothing significantly improved reliability. Interestingly, nodal centralities were positively correlated with their reliabilities, and nodal degree and efficiency outperformed nodal betweenness with respect to reliability.ConclusionsOur findings support single‐subject morphological network analysis as a meaningful and reliable method to characterize structural organization of the human brain; this method thus opens a new avenue toward understanding the substrate of intersubject variability in behavior and function and establishing morphological network biomarkers in brain disorders.

Inflammatory Mediators as Biomarkers in Brain Disorders

Neurodegenerative diseases such as Alzheimer, Parkinson, amyotrophic lateral sclerosis, and Huntington are incurable and debilitating conditions that result in progressive death of the neurons. The definite diagnosis of a neurodegenerative disorder is disadvantaged by the difficulty in obtaining biopsies and thereby to validate the clinical diagnosis with pathological results. Biomarkers are valuable indicators for detecting different phases of a disease such as prevention, early onset, treatment, progression, and monitoring the effect of pharmacological responses to a therapeutic intervention. Inflammation occurs in neurodegenerative diseases, and identification and validation of molecules involved in this process could be a strategy for finding new biomarkers. The ideal inflammatory biomarker needs to be easily measurable, must be reproducible, not subject to wide variation in the population, and unaffected by external factors. Our review summarizes the most important inflammation biomarkers currently available, whose specificity could be utilized for identifying and monitoring distinctive phases of different neurodegenerative diseases.

Assessing biomarkers for brain diseases: progress and gaps

A report on the 2nd Wellcome Trust Scientific Conference on Biomarkers for Brain Disorders: Challenges and Opportunities, held at the Moller Centre, Cambridge, UK, February 3-5, 2013.

Interpreting the effects of altered brain anatomical connectivity on fMRI functional connectivity: a role for computational neural modeling.

Recently, there have been a large number of studies using resting state fMRI to characterize abnormal brain connectivity in patients with a variety of neurological, psychiatric, and developmental disorders. However, interpreting what the differences in resting state fMRI functional connectivity (rsfMRI-FC) actually reflect in terms of the underlying neural pathology has proved to be elusive because of the complexity of brain anatomical connectivity. The same is the case for task-based fMRI studies. In the last few years, several groups have used large-scale neural modeling to help provide some insight into the relationship between brain anatomical connectivity and the corresponding patterns of fMRI-FC. In this paper we review several efforts at using large-scale neural modeling to investigate the relationship between structural connectivity and functional/effective connectivity to determine how alterations in structural connectivity are manifested in altered patterns of functional/effective connectivity. Because the alterations made in the anatomical connectivity between specific brain regions in the model are known in detail, one can use the results of these simulations to determine the corresponding alterations in rsfMRI-FC. Many of these simulation studies found that structural connectivity changes do not necessarily result in matching changes in functional/effective connectivity in the areas of structural modification. Often, it was observed that increases in functional/effective connectivity in the altered brain did not necessarily correspond to increases in the strength of the anatomical connection weights. Note that increases in rsfMRI-FC in patients have been interpreted in some cases as resulting from neural plasticity. These results suggest that this interpretation can be mistaken. The relevance of these simulation findings to the use of functional/effective fMRI connectivity as biomarkers for brain disorders is also discussed.

Biomarkers for brain disorders

Biomarkers, be they genetic traits, biochemical changes or alterations in structural or functional features, are required to help the diagnosis of a variety of neurological disorders and to detect the progression of these diseases. As new medicines and therapeutic strategies are developed, biomarkers will also be required to measure the efficacy of these treatments. The importance of biomarkers in this field should not be underestimated, particularly considering the huge social and economic burden presently attributed to these diseases. This article explains the biomarker development process and aims to describe the current status of biomarker research in association with prevalent neurological disorders such as stroke, motor neuron disease, Alzheimer’s disease, Parkinson’s disease and Huntington’s disease.

Proteomics for Brain Disorders—The Promise for Biomarkers

Biomarkers of brain disorders are urgently needed to aid diagnosis, monitor disease progression, and, as new medicines are introduced, detect the patient's response to treatment. Proteomics provides the opportunity to discover novel biochemical markers based on protein or peptide changes, either in concentration levels or post-translational modification status. There are many challenges associated with proteomics studies, and this article represents a review of the issues discussed during the proteomics breakout sessions held at the Biomarkers for Brain Disorders conference in Oxford in January 2009. Although to date, there are very few qualified biomarkers that have arisen as a result of proteomics efforts, we remain optimistic that proteomics will deliver biomarkers for brain disorders. To be successful, we need to recognize that such endeavors are likely to require multidisciplinary teams and continued collaboration between academia, the biotechnology industry, and the pharmaceutical sector.