scholarly journals A covariate-constraint method to map brain feature space into lower dimensional manifolds

2020 ◽  
pp. 1-30
Author(s):  
Félix Renard ◽  
Christian Heinrich ◽  
Marine Bouthillon ◽  
Maleka Schenck ◽  
Francis Schneider ◽  
...  
Keyword(s):  
Brain Connectivity ◽  
Feature Space ◽  
Graph Metrics ◽  
Brain Connectome ◽  
Biological Phenomena ◽  
Number Of Patients ◽  
Population Comparison ◽  
Constraint Method ◽  
The Brain ◽  
Insight Into

Human brain connectome studies aim at both exploring healthy brains, and extracting and analyzing relevant features associated to pathologies of interest. Usually this consists in modeling the brain connectome as a graph and in using graph metrics as features. A fine brain description requires graph metrics computation at the node level. Given the relatively reduced number of patients in standard cohorts, such data analysis problems fall in the high-dimension low sample size framework. In this context, our goal is to provide a machine learning technique that exhibits flexibility, gives the investigator grip on the features and covariates, allows visualization and exploration, and yields insight into the data and the biological phenomena at stake. The retained approach is dimension reduction in a manifold1 learning methodology, the originality lying in that one (or several) reduced variables be chosen by the investigator. The proposed method is illustrated on two studies, the first one addressing comatose patients, the second one addressing young versus elderly population comparison. The method sheds light on the differences between brain connectivity graphs using graph metrics and potential clinical interpretations of theses differences.

scholarly journals The Modular Organization of Pain Brain Networks: An fMRI Graph Analysis Informed by Intracranial EEG

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Camille Fauchon ◽  
David Meunier ◽  
Isabelle Faillenot ◽  
Florence B Pomares ◽  
Hélène Bastuji ◽  
...  
Keyword(s):  
Information Integration ◽  
Functional Organization ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Modular Organization ◽  
Noxious Heat ◽  
Intracranial Eeg ◽  
Brain Connectome ◽  
Posterior Parietal ◽  
The Brain

Abstract Intracranial EEG (iEEG) studies have suggested that the conscious perception of pain builds up from successive contributions of brain networks in less than 1 s. However, the functional organization of cortico-subcortical connections at the multisecond time scale, and its accordance with iEEG models, remains unknown. Here, we used graph theory with modular analysis of fMRI data from 60 healthy participants experiencing noxious heat stimuli, of whom 36 also received audio stimulation. Brain connectivity during pain was organized in four modules matching those identified through iEEG, namely: 1) sensorimotor (SM), 2) medial fronto-cingulo-parietal (default mode-like), 3) posterior parietal-latero-frontal (central executive-like), and 4) amygdalo-hippocampal (limbic). Intrinsic overlaps existed between the pain and audio conditions in high-order areas, but also pain-specific higher small-worldness and connectivity within the sensorimotor module. Neocortical modules were interrelated via “connector hubs” in dorsolateral frontal, posterior parietal, and anterior insular cortices, the antero-insular connector being most predominant during pain. These findings provide a mechanistic picture of the brain networks architecture and support fractal-like similarities between the micro-and macrotemporal dynamics associated with pain. The anterior insula appears to play an essential role in information integration, possibly by determining priorities for the processing of information and subsequent entrance into other points of the brain connectome.

scholarly journals From static to temporal network theory: Applications to functional brain connectivity

2017 ◽  
Vol 1 (2) ◽  
pp. 69-99 ◽  
Author(s):  
William Hedley Thompson ◽  
Per Brantefors ◽  
Peter Fransson
Keyword(s):  
Network Theory ◽  
Temporal Dynamics ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Network Activity ◽  
Temporal Network ◽  
Functional Brain ◽  
Brain Connectome ◽  
The Brain ◽  
Network Neuroscience

Network neuroscience has become an established paradigm to tackle questions related to the functional and structural connectome of the brain. Recently, interest has been growing in examining the temporal dynamics of the brain’s network activity. Although different approaches to capturing fluctuations in brain connectivity have been proposed, there have been few attempts to quantify these fluctuations using temporal network theory. This theory is an extension of network theory that has been successfully applied to the modeling of dynamic processes in economics, social sciences, and engineering article but it has not been adopted to a great extent within network neuroscience. The objective of this article is twofold: (i) to present a detailed description of the central tenets of temporal network theory and describe its measures, and; (ii) to apply these measures to a resting-state fMRI dataset to illustrate their utility. Furthermore, we discuss the interpretation of temporal network theory in the context of the dynamic functional brain connectome. All the temporal network measures and plotting functions described in this article are freely available as the Python package Teneto.

Assessment of Graph Metrics and Lateralization of Brain Connectivity in Progression of Alzheimer's Disease Using fMRI

2017 ◽  
Vol 9 (4) ◽  
pp. 46-66 ◽  
Author(s):  
Bhuvaneshwari Bhaskaran ◽  
Kavitha Anandan
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Default Mode Network ◽  
Brain Connectivity ◽  
Brain Network ◽  
Brain Disorder ◽  
Default Mode ◽  
Graph Metrics ◽  
Functional Topology ◽  
The Brain

Alzheimer's disease (AD) is a progressive brain disorder which has a long preclinical phase. The beta-amyloid plaques and tangles in the brain are considered as the main pathological causes. Functional connectivity is typically examined in capturing brain network dynamics in AD. A definitive underconnectivity is observed in patients through the progressive stages of AD. Graph theoretic modeling approaches have been effective in understanding the brain dynamics. In this article, the brain connectivity patterns and the functional topology through the progression of Alzheimer's disease are analysed using resting state fMRI. The altered network topology is analysed by graphed theoretical measures and explains cognitive deficits caused by the progression of this disease. Results show that the functional topology is disrupted in the default mode network regions as the disease progresses in patients. Further, it is observed that there is a lack of left lateralization involving default mode network regions as the severity in AD increases.

scholarly journals From static to temporal network theory – applications to functional brain connectivity

2016 ◽  
Author(s):  
William Hedley Thompson ◽  
Per Brantefors ◽  
Peter Fransson
Keyword(s):  
Network Theory ◽  
Temporal Dynamics ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Network Activity ◽  
Temporal Network ◽  
Functional Brain ◽  
Brain Connectome ◽  
The Brain ◽  
Python Package

AbstractNetwork neuroscience has become an established paradigm to tackle questions related to the functional and structural connectome of the brain. Recently, there has been a growing interest to examine the temporal dynamics of the brain's network activity. While different approaches to capture fluctuations in brain connectivity have been proposed, there have been few attempts to quantify these fluctuations using temporal network theory. Temporal network theory is an extension of network theory that has been successfully applied to the modeling of dynamic processes in economics, social sciences and engineering. The objective of this paper is twofold: (i) to present a detailed description of the central tenets and outline measures from temporal network theory; (ii) apply these measures to a resting-state fMRI dataset to illustrate their utility. Further, we discuss the interpretation of temporal network theory in the context of the dynamic functional brain connectome. All the temporal network measures and plotting functions described in this paper are freely available as a python package Teneto.

Evaluación de Terapias Acústicas en Pacientes con Acúfeno: Análisis de Conectividad en Potenciales Relacionados a Eventos

2021 ◽  
Author(s):  
◽  
A. Álvarez-Terríquez
Keyword(s):  
Brain Connectivity ◽  
The Other ◽  
Brain Electrical Activity ◽  
Tinnitus Retraining Therapy ◽  
Control Subjects ◽  
Graph Metrics ◽  
Retraining Therapy ◽  
Feature Values ◽  
Underlying Mechanisms ◽  
The Brain

The application of acoustic therapies in the treatment of patients with tinnitus is increasingly common. The main objective of these therapies is to restore the behavior of the brain pathways and structures involved in the sense of hearing to their normal condition. Brain electrical activity (EEG) is analyzed as it is one of many tools that have been used to study the underlying mechanisms of tinnitus. In this work, some characteristics that allow the detection of changes in brain connectivity 8 weeks after starting therapy in some acoustic therapies are presented. The auditory discrimination therapy or ADT, not only presented changes in the brain connectivity of the subjects who received it, but also the graph metrics used as feature values to measure this connectivity were close to the values ​​presented in the control subjects. In the other hand, the tinnitus retraining therapy TRT, also presented changes in brain connectivity; however, the graph metrics used as feature values to measure this connectivity were not close to the values presented in the control subjects.

scholarly journals Effect of levodopa on human brain connectome in Parkinsons disease

2021 ◽  
Author(s):  
Sajjad Farashi ◽  
Mojtaba Khazaei
Keyword(s):  
Brain Connectivity ◽  
Gamma Band ◽  
Beta Band ◽  
Positive Effects ◽  
Eyes Closed ◽  
Brain Connectome ◽  
Eeg Data ◽  
Eyes Open ◽  
Gamma Band Activity ◽  
The Brain

Levodopa-based drugs are widely used for mitigating the complications induced by PD. Despite the positive effects, several issues regarding the way that levodopa changes brain activities have remained unclear. Methods-A combined strategy using EEG data and graph theory was used for investigating how levodopa changed connectome and processing hubs of the brain during resting-state. Obtained results were subjected to ANOVA test and multiple-comparison post-hoc correction procedure. Results: Results showed that graph topology of PD patients was not significantly different with the healthy group during eyes-closed condition while in eyes-open condition statistical significant differences were found. The main effect of levodopa medication was observed for gamma-band activity of the brain in which levodopa changed the brain connectome toward a star-like topology. Considering the beta subband of EEG data, graph leaf number increased following levodopa medication in PD patients. Enhanced brain connectivity in gamma band and reduced beta band connections in basal ganglia were also observed after levodopa medication. Furthermore, source localization using dipole fitting showed that levodopa prescription suppressed the activity of collateral trigone. Conclusion: Our combined EEG and graph analysis showed that levodopa medication changed the brain connectome, especially in the high-frequency range of EEG (beta and gamma).

scholarly journals Differences in directed functional brain connectivity related to age, sex and mental health

2019 ◽  
Author(s):  
Martina J. Lund ◽  
Dag Alnæs ◽  
Simon Schwab ◽  
Dennis van der Meer ◽  
Ole A. Andreassen ◽  
...  
Keyword(s):  
Mental Health ◽  
Information Flow ◽  
Psychiatric Symptoms ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Uk Biobank ◽  
Functional Brain ◽  
Brain Connectome ◽  
Human Connectome Project ◽  
Insight Into

AbstractObjectiveFunctional interconnections between brain regions define the ‘connectome’ which is of central interest for understanding human brain function. Resting-state functional magnetic resonance (rsfMRI) work has revealed changes in static connectivity related to age, sex, cognitive abilities and psychiatric symptoms, yet little is known how these factors may alter the information flow. The commonly used approach infers functional brain connectivity using stationary coefficients yielding static estimates of the undirected connection strength between brain regions. Dynamic graphical models (DGMs) are a multivariate model with dynamic coefficients reflecting directed temporal associations between nodes, and can yield novel insight into directed functional connectivity. Here, we leveraged this approach to test for associations between edge-wise estimates of direction flow across the brain connectome and age, sex, intellectual abilities and mental health.MethodsWe applied DGM to investigate patterns of information flow in data from 984 individuals from the Human Connectome Project (HCP) and 10,249 individuals from the UK Biobank.ResultsOur analysis yielded patterns of directed connectivity in independent HCP and UK Biobank data similar to those previously reported, including that the cerebellum consistently receives information from other networks. We show robust associations between information flow and age and sex for several connections, with strongest effects of age observed in the sensorimotor network. Visual, auditory and sensorimotor nodes were also linked to mental health.DiscussionOur findings support the use of DGM as a measure of directed connectivity in rsfMRI data and provide new insight into the shaping of the connectome during aging.

A simple treatment planning strategy for patients with multiple metastases treated with Gamma Knife surgery

2006 ◽  
Vol 105 (Supplement) ◽  
pp. 2-4 ◽  
Author(s):  
James G. Douglas ◽  
Robert Goodkin
Keyword(s):  
Mr Imaging ◽  
Treatment Planning ◽  
Gamma Knife ◽  
Gamma Knife Surgery ◽  
Planning Method ◽  
Cns Metastases ◽  
Cell Lung Carcinoma ◽  
Dose Volume Histograms ◽  
Number Of Patients ◽  
The Brain

ObjectIn a substantial number of patients treated at the authors' facility for brain metastases, additional lesions are identified at the time of Gamma Knife surgery (GKS). These lesions are often widely dispersed and may number over 10, which is the maximal number of matrices that can be currently placed for treatment with Leksell Gamma-Plan 4C. The authors describe a simple planning method for GKS in patients with multiple, widely dispersed central nervous system (CNS) metastases.MethodsTwo patients presented with three to five identified recurrent metastases from non–small cell lung carcinoma and breast carcinoma after having received whole-brain radiotherapy. At the time of treatment with GKS in each patient, spoiled-gradient Gd-enhanced magnetic resonance (MR) imaging revealed substantially more metastases than originally thought, which were widely scattered throughout all regions of the brain. The authors simplified the treatment planning approach by dividing the entire CNS contents into six contiguous, nonoverlapping matrices, which allowed for the planning, calculation, and treatment of all lesions.Two patients were successfully treated with GKS for more than 10 CNS metastases by using this simple planning method. Differing peripheral doses to varied-size lesions were delivered by prescribing to different isodose curves within any given matrix when required. Dose–volume histograms showed brain doses as follows: 10% of the total brain volume received 5 to 6.4 Gy; 25% received 3.8 to 4.8 Gy; 50% received 2.7 to 3.1 Gy; and 75% received 2.2 to 2.5 Gy.Conclusions The delineation of more metastases than appreciated on the diagnostic MR imaging is a common occurrence at the time of GKS at the authors' institution. The treatment of multiple (>10), widely dispersed CNS metastases can be simplified by the placement of multiple, contiguous, non-overlapping matrices, which can be employed to treat lesions in all areas of the brain when separate matrices cannot be utilized.

Possibility that the Onset of Autism Spectrum Disorder is Induced by Failure of the Glutamine-Glutamate Cycle

2020 ◽  
Vol 14 (2) ◽  
pp. 170-174
Author(s):  
Koichi Kawada ◽  
Nobuyuki Kuramoto ◽  
Seisuke Mimori
Keyword(s):  
Autism Spectrum Disorder ◽  
Endoplasmic Reticulum ◽  
Environmental Factors ◽  
Endoplasmic Reticulum Stress ◽  
Synapse Formation ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Neurodevelopmental Disease ◽  
Number Of Patients ◽  
The Brain

: Autism spectrum disorder (ASD) is a neurodevelopmental disease, and the number of patients has increased rapidly in recent years. The causes of ASD involve both genetic and environmental factors, but the details of causation have not yet been fully elucidated. Many reports have investigated genetic factors related to synapse formation, and alcohol and tobacco have been reported as environmental factors. This review focuses on endoplasmic reticulum stress and amino acid cycle abnormalities (particularly glutamine and glutamate) induced by many environmental factors. In the ASD model, since endoplasmic reticulum stress is high in the brain from before birth, it is clear that endoplasmic reticulum stress is involved in the development of ASD. On the other hand, one report states that excessive excitation of neurons is caused by the onset of ASD. The glutamine-glutamate cycle is performed between neurons and glial cells and controls the concentration of glutamate and GABA in the brain. These neurotransmitters are also known to control synapse formation and are important in constructing neural circuits. Theanine is a derivative of glutamine and a natural component of green tea. Theanine inhibits glutamine uptake in the glutamine-glutamate cycle via slc38a1 without affecting glutamate; therefore, we believe that theanine may prevent the onset of ASD by changing the balance of glutamine and glutamate in the brain.

scholarly journals Medical Adjuvants in the Treatment of Surgically Refractory Arteriovenous Malformations of the Head and Face: Case Report and Review of Literature

2021 ◽  
pp. 1-7
Author(s):  
Vaidya Govindarajan ◽  
Joshua D. Burks ◽  
Evan M. Luther ◽  
John W. Thompson ◽  
Robert M. Starke
Keyword(s):  
Arteriovenous Malformations ◽  
Mtor Inhibitors ◽  
Molecular Target ◽  
Review Of Literature ◽  
Review Of The Literature ◽  
Early Results ◽  
Number Of Patients ◽  
Initiation Of Therapy ◽  
The Brain ◽  
Radiographic Improvement

<b><i>Background:</i></b> Arteriovenous malformations (AVMs) of the brain and face present unique challenges for clinicians. Cerebral AVMs may induce hemorrhage or form aneurysms, while facial AVMs can cause significant disfigurement and pain. Moreover, facial AVMs often draw blood supply from arteries providing critical blood flow to other important structures of the head which may make them impossible to treat curatively. Medical adjuvants may be an important consideration in the management of these patients. <b><i>Summary:</i></b> We conducted a systematic review of the literature to identify other instances of molecular target of rapamycin (mTOR) inhibitors used as medical adjuvants for the treatment of cranial and facial AVMs. We also present 2 cases from our own institution where patients were treated with partial embolization, followed by adjuvant therapy with rapamycin. After screening a total of 75 articles, 7 were identified which described use of rapamycin in the treatment of inoperable cranial or facial AVM. In total, 21 cases were reviewed. The median treatment duration was 12 months (3–24.5 months), and the highest recorded dose was 3.5 mg/m<sup>2</sup>. 76.2% of patients demonstrated at least a partial response to rapamycin therapy. In 2 patients treated at our institution, symptomatic and radiographic improvement were noted 6 months after initiation of therapy. <b><i>Key Messages:</i></b> Early results have been encouraging in a small number of patients with inoperable AVM of the head and face treated with mTOR inhibitors. Further study of medical adjuvants such as rapamycin may be worthwhile.

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