Abnormal Functional and Structural networks in Rectal Cancer with Depressive risk: A Graph Theory Analysis

2019 ◽  
Author(s):  
Wenwen Zhang ◽  
Ying Zou ◽  
Yuan Li ◽  
Yu Fu ◽  
Jie Shi ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Emotional Disorders ◽  
Parietal Lobe ◽  
Diffusion Tensor ◽  
Brain Network ◽  
Imaging Data ◽  
Network Efficiency ◽  
Limbic Lobe ◽  
Random Node ◽  
Brain Network Reorganization

Abstract Background: Surgery and chemotherapy can cause depressive risk in patients with rectal cancer (RC). However, few comprehensive studies are conducted on RC patients associated alterations induced by emotional disorders in the topological organization of structural and functional networks. Methods: Resting-state functional MRI and Diffusion tensor imaging data were collected from 36 RC patients with surgery and chemotherapy and 32 healthy controls (HC). Functional network (FN) was constructed from extracting average time courses for 246 regions of interest (ROI) and structural network (SN) was established by deterministic tractography. Graph theoretical analysis was used to calculate small-worldness property, clustering coefficients, shortest path length and network efficiency. Additionally, we assess network resilient on FN and SN. Results: Abnormal small-worldness property of FN and SN were found in RC patients. The FN and SN exhibited increased local efficiency and global efficiency respectively in RC patients.The increased nodal efficiency in RC patients were mainly found in the frontal lobe, parietal lobe and limbic lobe for FN and SN, while the decreased nodal efficiency were distributed in subcortical nuclei, parietal lobe and limbic lobe only for SN. In network resilient analysis, the RC patients showed less resilient to targeted or random node deletion in both networks compared with HC. Moreover, FN is more robust than SN for all participants. Conclusions: This study revealed that topological organizations of the FN and SN may be disrupted in RC patients. Brain network reorganization is a compensation mechanism to alleviate the depressive risk in RC patients after surgery and chemotherapy.

scholarly journals Abnormal Functional and Structural networks in Rectal Cancer with Depressive symptoms: A Graph Theory Analysis

2019 ◽  
Author(s):  
Wenwen Zhang ◽  
Ying Zou ◽  
Yuan Li ◽  
Yu Fu ◽  
Jie Shi ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Emotional Disorders ◽  
Parietal Lobe ◽  
Diffusion Tensor ◽  
Brain Network ◽  
Imaging Data ◽  
Network Efficiency ◽  
Limbic Lobe ◽  
Random Node ◽  
Brain Network Reorganization

Abstract Background: Surgery and chemotherapy can cause emotional disorders in patients with rectal cancer (RC). However, few comprehensive studies are conducted on RC patients associated alterations in the topological organization of structural and functional networks. Methods: Resting-state functional MRI and Diffusion tensor imaging data were collected from 36 RC patients with surgery and chemotherapy and 32 healthy controls (HC). Functional network (FN) was constructed from extracting average time courses for 246 regions of interest (ROI) and structural network (SN) was established by deterministic tractography. Graph theoretical analysis was used to calculate small-worldness property, clustering coefficients, shortest path length and network efficiency. Additionally, we assess network resilient on FN and SN. Results: Abnormal small-worldness property of FN and SN were found in RC patients. The FN and SN exhibited increased local efficiency and global efficiency respectively in RC patients.The increased nodal efficiency in RC patients were mainly found in the frontal lobe, parietal lobe and limbic lobe for FN and SN, while the decreased nodal efficiency were distributed in subcortical nuclei, parietal lobe and limbic lobe only for SN. In network resilient analysis, the RC patients showed less resilient to targeted or random node deletion in both networks compared with HC. Moreover, FN is more robust than SN for all participants. Conclusions: This study revealed that topological organizations of the FN and SN may be disrupted in RC patients. Brain network reorganization is a compensation mechanism for brain impairment after surgery and chemotherapy.

scholarly journals A Computational Network Control Theory Analysis of Depression Symptoms

2018 ◽  
Vol 1 ◽  
Author(s):  
Yoed N. Kenett ◽  
Roger E. Beaty ◽  
John D. Medaglia
Keyword(s):  
Control Theory ◽  
Diffusion Tensor ◽  
Brain Regions ◽  
Brain Network ◽  
Network Control ◽  
Depression Symptoms ◽  
Theory Approach ◽  
Imaging Data ◽  
The Brain ◽  
Subclinical Depression

AbstractRumination and impaired inhibition are considered core characteristics of depression. However, the neurocognitive mechanisms that contribute to these atypical cognitive processes remain unclear. To address this question, we apply a computational network control theory approach to structural brain imaging data acquired via diffusion tensor imaging in a large sample of participants, to examine how network control theory relates to individual differences in subclinical depression. Recent application of this theory at the neural level is built on a model of brain dynamics, which mathematically models patterns of inter-region activity propagated along the structure of an underlying network. The strength of this approach is its ability to characterize the potential role of each brain region in regulating whole-brain network function based on its anatomical fingerprint and a simplified model of node dynamics. We find that subclinical depression is negatively related to higher integration abilities in the right anterior insula, replicating and extending previous studies implicating atypical switching between the default mode and Executive Control Networks in depression. We also find that subclinical depression is related to the ability to “drive” the brain system into easy to reach neural states in several brain regions, including the bilateral lingual gyrus and lateral occipital gyrus. These findings highlight brain regions less known in their role in depression, and clarify their roles in driving the brain into different neural states related to depression symptoms.

scholarly journals Differential structure-function network coupling in the inattentive and combined types of attention deficit hyperactivity disorder

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260295
Author(s):  
Dongha Lee ◽  
Elizabeth Quattrocki Knight ◽  
Hyunjoo Song ◽  
Saebyul Lee ◽  
Chongwon Pae ◽  
...  
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Structural Connectivity ◽  
Brain Network ◽  
Functional Network ◽  
Imaging Data ◽  
Network Efficiency ◽  
Significant Group ◽  
Children With Adhd ◽  
Hyperactivity Disorder

The heterogeneous presentation of inattentive and hyperactive-impulsive core symptoms in attention deficit hyperactivity disorder (ADHD) warrants further investigation into brain network connectivity as a basis for subtype divisions in this prevalent disorder. With diffusion and resting-state functional magnetic resonance imaging data from the Healthy Brain Network database, we analyzed both structural and functional network efficiency and structure-functional network (SC-FC) coupling at the default mode (DMN), executive control (ECN), and salience (SAN) intrinsic networks in 201 children diagnosed with the inattentive subtype (ADHD-I), the combined subtype (ADHD-C), and typically developing children (TDC) to characterize ADHD symptoms relative to TDC and to test differences between ADHD subtypes. Relative to TDC, children with ADHD had lower structural connectivity and network efficiency in the DMN, without significant group differences in functional networks. Children with ADHD-C had higher SC-FC coupling, a finding consistent with diminished cognitive flexibility, for all subnetworks compared to TDC. The ADHD-C group also demonstrated increased SC-FC coupling in the DMN compared to the ADHD-I group. The correlation between SC-FC coupling and hyperactivity scores was negative in the ADHD-I, but not in the ADHD-C group. The current study suggests that ADHD-C and ADHD-I may differ with respect to their underlying neuronal connectivity and that the added dimensionality of hyperactivity may not explain this distinction.

STRUCTURAL BRAIN NETWORK ABNORMALITIES IN SUBJECTS WITH INTERNET ADDICTION

2017 ◽  
Vol 17 (07) ◽  
pp. 1740031 ◽  
Author(s):  
MIN-HEE LEE ◽  
AREUM MIN ◽  
YOON HO HWANG ◽  
DONG YOUN KIM ◽  
BONG SOO HAN ◽  
...  
Keyword(s):  
Internet Addiction ◽  
Diffusion Tensor ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Imaging Data ◽  
Structural Brain Network ◽  
Global And Local ◽  
The Impact ◽  
Structural Brain

Although problematic overuse of internet has increased, psychopathological characteristics and neurobiological mechanisms for internet addiction (IA) remain poorly understood. Therefore, it is necessary to investigate the impact of IA on the brain. The present study included 17 subjects with IA and 20 healthy subjects. We constructed the structural brain network from diffusion tensor imaging data and investigated alteration of structural connections in subjects with IA using the network analysis on the global and local levels. The subjects with IA showed increase of regional efficiency (RE) in bilateral orbitofrontal cortex (OFC) and decrease in right middle cingulate and middle temporal gyri ([Formula: see text]), whereas the global properties did not show significant changes. Young’s internet addiction test (IAT) scores and RE in left OFC showed positive correlation, and average time spent on internet per day was positively correlated with the RE in right OFC. This is the first study examining alterations of the structural brain connectivity in IA. We found that subjects with IA showed alterations of RE in some brain regions and RE was positively associated with the severity of IA and average time spent on internet per day. Therefore, RE may be a good property for IA assessment.

scholarly journals Mapping the human praxis network: an investigation of white matter disconnection in limb apraxia

2020 ◽  
Author(s):  
Hannah Rosenzopf ◽  
Daniel Wiesen ◽  
Alexandra Basilakos ◽  
Grigori Yourganov ◽  
Leonardo Bonilha ◽  
...  
Keyword(s):  
White Matter ◽  
Left Hemisphere ◽  
Structural Damage ◽  
Parietal Lobe ◽  
Diffusion Tensor ◽  
Brain Network ◽  
Support Vector ◽  
Precentral Gyrus ◽  
Support Vector Regression Model ◽  
Limb Apraxia

AbstractStroke to the left hemisphere of the brain can cause limb apraxia, a disorder characterised by deficits of higher order motor skills such as the failure to accurately produce meaningful gestures. This disorder provides unique insights into the anatomy of the human praxis system. The present study aimed to identify the structural brain network, that when damaged by stroke, causes limb apraxia. We assessed the ability to perform meaningful gestures with the hand in 101 patients with chronic left hemisphere stroke. Structural damage to white matter fibres was assessed by diffusion tensor imaging. A support vector regression model predicting apraxia based on individual topographies of tract-based fractional anisotropy was utilised to obtain multivariate topographical inference. We found pathological white matter alterations in a densely connected fronto-temporo-parietal network of short and long association fibres to predict limb apraxia deficits. Major disconnection affected temporo-parietal and temporo-temporal connections. Grey matter areas with a high number of disconnections included inferior parietal lobe, middle and superior temporal gyrus, inferior and middle frontal lobe, precentral gyrus, putamen, and caudate nucleus. These results demonstrate the relevance of frontal and inferior parietal regions in praxis, but they also highlight the temporal lobe and its connections to be an important contributor to the human praxis network.

The Role of Neuroimaging in the Diagnosis and Treatment of Depressive Disorder: A Recent Review

2018 ◽  
Vol 24 (22) ◽  
pp. 2515-2523 ◽  
Author(s):  
Tianbin Song ◽  
Xiaowei Han ◽  
Lei Du ◽  
Jing Che ◽  
Jing Liu ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Depressive Disorder ◽  
Diffusion Tensor ◽  
Single Photon ◽  
Rapid Development ◽  
Photon Emission ◽  
Clinical Manifestations ◽  
Brain Network ◽  
Emission Computed Tomography ◽  
Resonance Spectroscopy

Depression is a mental disorder with serious negative health outcomes. Its main clinical manifestations are depressed mood, slow thinking, loss of interest, and lack of energy. The rising incidence of depression has a major impact on patients and their families and imposes a substantial burden on society. With the rapid development of imaging technology in recent years, researchers have studied depression from different perspectives, including molecular, functional, and structural imaging. Many studies have revealed changes in structure, function, and metabolism in various brain regions in patients with depressive disorder. In this review, we summarize relevant studies of depression, including investigations using structural magnetic resonance imaging (MRI), functional MRI (task-state fMRI and resting-state fMRI), diffusion tensor imaging (DTI), magnetic resonance spectroscopy (MRS), brain network and molecular imaging (positron emission tomography [PET] and single photon emission computed tomography [SPECT]), which have contributed to our understanding of the etiology, neuropathology, and pathogenesis of depressive disorder.

scholarly journals Machine Learning for the Classification of Alzheimer’s Disease and Its Prodromal Stage Using Brain Diffusion Tensor Imaging Data: A Systematic Review

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1071
Author(s):  
Lucia Billeci ◽  
Asia Badolato ◽  
Lorenzo Bachi ◽  
Alessandro Tonacci
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Diffusion Tensor Imaging ◽  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Classification Problem ◽  
Computer Algorithms ◽  
Imaging Data

Alzheimer’s disease is notoriously the most common cause of dementia in the elderly, affecting an increasing number of people. Although widespread, its causes and progression modalities are complex and still not fully understood. Through neuroimaging techniques, such as diffusion Magnetic Resonance (MR), more sophisticated and specific studies of the disease can be performed, offering a valuable tool for both its diagnosis and early detection. However, processing large quantities of medical images is not an easy task, and researchers have turned their attention towards machine learning, a set of computer algorithms that automatically adapt their output towards the intended goal. In this paper, a systematic review of recent machine learning applications on diffusion tensor imaging studies of Alzheimer’s disease is presented, highlighting the fundamental aspects of each work and reporting their performance score. A few examined studies also include mild cognitive impairment in the classification problem, while others combine diffusion data with other sources, like structural magnetic resonance imaging (MRI) (multimodal analysis). The findings of the retrieved works suggest a promising role for machine learning in evaluating effective classification features, like fractional anisotropy, and in possibly performing on different image modalities with higher accuracy.

Functional brain network reorganization in HIV infection

2021 ◽  
Author(s):  
Silvia Minosse ◽  
Eliseo Picchi ◽  
Francesca Di Giuliano ◽  
Loredana Sarmati ◽  
Elisabetta Teti ◽  
...  
Keyword(s):  
Hiv Infection ◽  
Brain Network ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Brain Network Reorganization

scholarly journals The unbalanced reorganization of weaker functional connections induces the altered brain network topology in schizophrenia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rossana Mastrandrea ◽  
Fabrizio Piras ◽  
Andrea Gabrielli ◽  
Nerisa Banaj ◽  
Guido Caldarelli ◽  
...  
Keyword(s):  
Resting State ◽  
Functional Organization ◽  
Functional Integration ◽  
Brain Network ◽  
Modular Organization ◽  
Node Degree ◽  
Imaging Data ◽  
Brain Disorder ◽  
Functional Connections ◽  
The Brain

AbstractNetwork neuroscience shed some light on the functional and structural modifications occurring to the brain associated with the phenomenology of schizophrenia. In particular, resting-state functional networks have helped our understanding of the illness by highlighting the global and local alterations within the cerebral organization. We investigated the robustness of the brain functional architecture in 44 medicated schizophrenic patients and 40 healthy comparators through an advanced network analysis of resting-state functional magnetic resonance imaging data. The networks in patients showed more resistance to disconnection than in healthy controls, with an evident discrepancy between the two groups in the node degree distribution computed along a percolation process. Despite a substantial similarity of the basal functional organization between the two groups, the expected hierarchy of healthy brains' modular organization is crumbled in schizophrenia, showing a peculiar arrangement of the functional connections, characterized by several topologically equivalent backbones. Thus, the manifold nature of the functional organization’s basal scheme, together with its altered hierarchical modularity, may be crucial in the pathogenesis of schizophrenia. This result fits the disconnection hypothesis that describes schizophrenia as a brain disorder characterized by an abnormal functional integration among brain regions.

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