Frontal Functional Network Disruption Associated with Amyotrophic Lateral Sclerosis: An fNIRS-Based Minimum Spanning Tree Analysis

Recent evidence increasingly associates network disruption in brain organization with multiple neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS), a rare terminal disease. However, the comparability of brain network characteristics across different studies remains a challenge for conventional graph theoretical methods. One suggested method to address this issue is minimum spanning tree (MST) analysis, which provides a less biased comparison. Here, we assessed the novel application of MST network analysis to hemodynamic responses recorded by functional near-infrared spectroscopy (fNIRS) neuroimaging modality, during an activity-based paradigm to investigate hypothetical disruptions in frontal functional brain network topology as a marker of the executive dysfunction, one of the most prevalent cognitive deficit reported across ALS studies. We analyzed data recorded from nine participants with ALS and ten age-matched healthy controls by first estimating functional connectivity, using phase-locking value (PLV) analysis, and then constructing the corresponding individual and group MSTs. Our results showed significant between-group differences in several MST topological properties, including leaf fraction, maximum degree, diameter, eccentricity, and degree divergence. We further observed a global shift toward more centralized frontal network organizations in the ALS group, interpreted as a more random or dysregulated network in this cohort. Moreover, the similarity analysis demonstrated marginally significantly increased overlap in the individual MSTs from the control group, implying a reference network with lower topological variation in the healthy cohort. Our nodal analysis characterized the main local hubs in healthy controls as distributed more evenly over the frontal cortex, with slightly higher occurrence in the left prefrontal cortex (PFC), while in the ALS group, the most frequent hubs were asymmetrical, observed primarily in the right prefrontal cortex. Furthermore, it was demonstrated that the global PLV (gPLV) synchronization metric is associated with disease progression, and a few topological properties, including leaf fraction and tree hierarchy, are linked to disease duration. These results suggest that dysregulation, centralization, and asymmetry of the hemodynamic-based frontal functional network during activity are potential neuro-topological markers of ALS pathogenesis. Our findings can possibly support new bedside assessments of the functional status of ALS’ brain network and could hypothetically extend to applications in other neurodegenerative diseases

Resting-State Quantitative Electroencephalography Reveals Increased Neurophysiologic Connectivity in Depression

Symptoms of Major Depressive Disorder (MDD) are hypothesized to arise from dysfunction in brain networks linking the limbic system and cortical regions. Alterations in brain functional cortical connectivity in resting-state networks have been detected with functional imaging techniques, but neurophysiologic connectivity measures have not been systematically examined. We used weighted network analysis to examine resting state functional connectivity as measured by quantitative electroencephalographic (qEEG) coherence in 121 unmedicated subjects with MDD and 37 healthy controls. Subjects with MDD had significantly higher overall coherence as compared to controls in the delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), and beta (12–20 Hz) frequency bands. The frontopolar region contained the greatest number of “hub nodes” (surface recording locations) with high connectivity. MDD subjects expressed higher theta and alpha coherence primarily in longer distance connections between frontopolar and temporal or parietooccipital regions, and higher beta coherence primarily in connections within and between electrodes overlying the dorsolateral prefrontal cortical (DLPFC) or temporal regions. Nearest centroid analysis indicated that MDD subjects were best characterized by six alpha band connections primarily involving the prefrontal region. The present findings indicate a loss of selectivity in resting functional connectivity in MDD. The overall greater coherence observed in depressed subjects establishes a new context for the interpretation of previous studies showing differences in frontal alpha power and synchrony between subjects with MDD and normal controls. These results can inform the development of qEEG state and trait biomarkers for MDD

A deep phenotyping approach to understand major depressive disorder and responses to antidepressant pharmacotherapy

Major depressive disorder (MDD) is a debilitating psychiatric disorder characterised by a complex underlying biology and poor response to pharmacological antidepressant strategies. Given the heterogeneity of MDD and the diverse range of available treatment options, there is an increasing desire to develop and implement precision medicine approaches to tailor existing treatment strategies to the biological system of the individual. In this thesis, high-resolution omics data (connectomics [fMRI], metabolomics [1H NMR] and immunomics [inflammatory cytokines]) collected from the Canadian Biomarker Integration Network in Depression (CAN-BIND) study has been integrated to facilitate the deep phenotyping of MDD. In addition, this approach has been used to predict the treatment response to two common antidepressant drugs, monotherapy with the selective serotonin reuptake inhibitor (SSRI) escitalopram (10-20 mg) or combination therapy with escitalopram and the dopaminergic antipsychotic aripiprazole (2-10 mg). This approach identified a multi-modal panel of sex-specific biomarkers of MDD and treatment response, highlighting a strong immunometabolic component in depressed males, but not females. Unsupervised clustering methods indicated the superiority of biological (neuroimaging) over symptom-based (clinical questionnaires) data for the stratification of patients into MDD subtypes with differential response to treatment. More importantly, a set of multi-modal, sex-specific biomarkers were identified that predicted treatment response with escitalopram monotherapy (84.7% accuracy) or aripiprazole augmentation (88.5% accuracy). In addition to highlighting potential new aspects of the biology of MDD (e.g. relevance of lipoprotein size and density for their relation to depression), this work is one of the first attempts to apply systems biology approaches to high-resolution biological data from a large clinical trial to predict later treatment outcome. With the validation of the findings presented in this thesis in independent cohorts, and with further development of omics technologies, leading to cheaper and high-throughput screening of the patient population, pre-dose biomarkers have the potential to achieve personalised treatment. Each year, escitalopram and aripiprazole are prescribed to an estimated 26 million and 7 million individuals respectively, and over one third of them do not respond. Thus, being able to predict response to antidepressant medication from baseline biomarkers has enormous clinical and socioeconomic benefits.Open Acces

Seçilmiş ülkelerin nüfus, gsyih, mal ve hizmetlerin ihracatı değişkenlerine göre hiyerarşik kümelenmesi

The changes in the population, gross domestic product (GDP) and exports of goods and services of the 32 countries that we have selected for 25 years have annually been examined. In this study, we use hierarchical structure methods such as minimum spanning tree (MST) and hierarchical tree (HT) to explain the relationship between countries' GDP, population, and annual changes in exports of goods and services from 1991 to 2016. Using MST and HT, we obtained the countries with the same economic development level as some variables. At the same time, we obtained the opportunity to see which country's population, GDP, and annual changes in exports of goods and services are at a similar change. Finally, for observing the cluster structure, we use a clustering linkage procedure. This topology will provide us with a useful guide to understanding their behavior and identifying the dominant associated countries in the network as part of a complicated network.Bu çalışmada, 32 ülkenin 25 yıllık; nüfus, gayri safi yurtiçi hasıla (GSYİH) ve mal ve hizmet ihracatındaki değişimleri arasındaki ilişkiler yıllık olarak incelenmektedir. 1991-2016 yılları arasında ülkelerin GSYİH, nüfus ve mal ve hizmet ihracatındaki yıllık değişimler arasındaki ilişkiyi açıklamak için minimum örten ağaç (MST) ve hiyerarşik ağaç (HT) gibi hiyerarşik yapı yöntemleri kullanılmıştır. MST ve HT'yi kullanarak, bazı değişkenlerle aynı ekonomik kalkınma düzeyine sahip ülkeleri elde ettik. Aynı zamanda, hangi ülkenin nüfusunun, GSYİH'sının ve mal ve hizmet ihracatındaki yıllık değişikliklerin benzer değişime sahip olduğunu görme fırsatı da elde ettik. Son olarak, küme yapısını gözlemlemek için kümeleme yöntemlerini kullandık. Bu topoloji, bize değişkenlerin davranışlarını anlamak ve karmaşık bir ağın parçası olarak ağdaki baskın ilişkili ülkeleri tanımlamak için yararlı bir kılavuz sağlamaktadı

Discriminative power of EEG-based biomarkers in major depressive disorder: A systematic review

Currently, the diagnosis of major depressive disorder (MDD) and its subtypes is mainly based on subjective assessments and self-reported measures. However, objective criteria as Electroencephalography (EEG) features would be helpful in detecting depressive states at early stages to prevent the worsening of the symptoms. Scientific community has widely investigated the effectiveness of EEG-based measures to discriminate between depressed and healthy subjects, with the aim to better understand the mechanisms behind the disorder and find biomarkers useful for diagnosis. This work offers a comprehensive review of the extant literature concerning the EEG-based biomarkers for MDD and its subtypes, and identify possible future directions for this line of research. Scopus, PubMed and Web of Science databases were researched following PRISMA’s guidelines. The initial papers’ screening was based on titles and abstracts; then full texts of the identified articles were examined, and a synthesis of findings was developed using tables and thematic analysis. After screening 1871 articles, 76 studies were identified as relevant and included in the systematic review. Reviewed markers include EEG frequency bands power, EEG asymmetry, ERP components, non-linear and functional connectivity measures. Results were discussed in relations to the different EEG measures assessed in the studies. Findings confirmed the effectiveness of those measures in discriminating between healthy and depressed subjects. However, the review highlights that the causal link between EEG measures and depressive subtypes needs to be further investigated and points out that some methodological issues need to be solved to enhance future research in this field

Fluctuations of attention network activation during the resting state reflect fluctuations in subjective attentional state.

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