An automated method for identifying an independent component analysis-based language-related resting-state network in brain tumor subjects for surgical planning

As a noninvasive and "task-free" technique, resting-state functional magnetic resonance imaging (rs-fMRI) has been gradually applied to pre-surgical functional mapping. Independent component analysis (ICA)-based mapping has shown advantage, as no a priori information is required. We developed an automated method for identifying language network in brain tumor subjects using ICA on rs-fMRI. In addition to standard processing strategies, we applied a discriminability-index-based component identification algorithm to identify language networks in three different groups. The results from the training group were validated in an independent group of healthy human subjects. For the testing group, ICA and seed-based correlation were separately computed and the detected language networks were assessed by intra-operative stimulation mapping to verify reliability of application in the clinical setting. Individualized language network mapping could be automatically achieved for all subjects from the two healthy groups except one (19/20, success rate = 95.0%). In the testing group (brain tumor patients), the sensitivity of the language mapping result was 60.9%, which increased to 87.0% (superior to that of conventional seed-based correlation [47.8%]) after extending to a radius of 1 cm. We established an automatic and practical component identification method for rs-fMRI-based pre-surgical mapping and successfully applied it to brain tumor patients

Tumor Tissue Detection using Blood-Oxygen-Level-Dependent Functional MRI based on Independent Component Analysis

Accurate delineation of gliomas from the surrounding normal brain areas helps maximize tumor resection and improves outcome. Blood-oxygen-level-dependent (BOLD) functional MRI (fMRI) has been routinely adopted for presurgical mapping of the surrounding functional areas. For completely utilizing such imaging data, here we show the feasibility of using presurgical fMRI for tumor delineation. In particular, we introduce a novel method dedicated to tumor detection based on independent component analysis (ICA) of resting-state fMRI (rs-fMRI) with automatic tumor component identification. Multi-center rs-fMRI data of 32 glioma patients from three centers, plus the additional proof-of-concept data of 28 patients from the fourth center with non-brain musculoskeletal tumors, are fed into individual ICA with different total number of components (TNCs). The best-fitted tumor-related components derived from the optimized TNCs setting are automatically determined based on a new template-matching algorithm. The success rates are 100%, 100% and 93.75% for glioma tissue detection for the three centers, respectively, and 85.19% for musculoskeletal tumor detection. We propose that the high success rate could come from the previously overlooked ability of BOLD rs-fMRI in characterizing the abnormal vascularization, vasomotion and perfusion caused by tumors. Our findings suggest an additional usage of the rs-fMRI for comprehensive presurgical assessment

Disrupted Functional Brain Connectivity in Partial Epilepsy: A Resting-State fMRI Study

Examining the spontaneous activity to understand the neural mechanism of brain disorder is a focus in recent resting-state fMRI. In the current study, to investigate the alteration of brain functional connectivity in partial epilepsy in a systematical way, two levels of analyses (functional connectivity analysis within resting state networks (RSNs) and functional network connectivity (FNC) analysis) were carried out on resting-state fMRI data acquired from the 30 participants including 14 healthy controls(HC) and 16 partial epilepsy patients. According to the etiology, all patients are subdivided into temporal lobe epilepsy group (TLE, included 7 patients) and mixed partial epilepsy group (MPE, 9 patients). Using group independent component analysis, eight RSNs were identified, and selected to evaluate functional connectivity and FNC between groups. Compared with the controls, decreased functional connectivity within all RSNs was found in both TLE and MPE. However, dissociating patterns were observed within the 8 RSNs between two patient groups, i.e, compared with TLE, we found decreased functional connectivity in 5 RSNs increased functional connectivity in 1 RSN, and no difference in the other 2 RSNs in MPE. Furthermore, the hierarchical disconnections of FNC was found in two patient groups, in which the intra-system connections were preserved for all three subsystems while the lost connections were confined to intersystem connections in patients with partial epilepsy. These findings may suggest that decreased resting state functional connectivity and disconnection of FNC are two remarkable characteristics of partial epilepsy. The selective impairment of FNC implicated that it is unsuitable to understand the partial epilepsy only from global or local perspective. We presumed that studying epilepsy in the multi-perspective based on RSNs may be a valuable means to assess the functional changes corresponding to specific RSN and may contribute to the understanding of the neuro-pathophysiological mechanism of epilepsy

Disruption of cortical integration during midazolam-induced light sedation: Effects of Midazolam-Induced Sedation on RSNs

This work examines the effect of midazolam‐induced light sedation on intrinsic functional connectivity of human brain, using a randomized, double‐blind, placebo‐controlled, cross‐over, within‐subject design. Fourteen healthy young subjects were enrolled and midazolam (0.03 mg/kg of the participant's body mass, to a maximum of 2.5 mg) or saline were administrated with an interval of one week. Resting‐state fMRI was conducted before and after administration for each subject. We focus on two types of networks: sensory related lower‐level functional networks and higher‐order functions related ones. Independent component analysis (ICA) was used to identify these resting‐state functional networks. We hypothesize that the sensory (visual, auditory, and sensorimotor) related networks will be intact under midazolam‐induced light sedation while the higher‐order (default mode, executive control, salience networks, etc.) networks will be functionally disconnected. It was found that the functional integrity of the lower‐level networks was maintained, while that of the higher‐level networks was significantly disrupted by light sedation. The within‐network connectivity of the two types of networks was differently affected in terms of direction and extent. These findings provide direct evidence that higher‐order cognitive functions including memory, attention, executive function, and language were impaired prior to lower‐level sensory responses during sedation. Our result also lends support to the information integration model of consciousness. Hum Brain Mapp 36:4247–4261, 2015. © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc

Elokuvan katselun aktivoimien aivoverkostojen ajallispaikallinen erottelu

Niin levon kuin tehtävän suorituksen aikana aktiiviset, toisiinsa toiminnallisesti kytkeytyneet aivoalueet, eli nk. lepoverkostot, ovat yksi nykyaikaisen aivotutkimuksen erityisistä mielenkiinnon kohteista. Ensimmäiseksi havaittiin motorinen verkosto, minkä jälkeen on löydetty monia muita aivoverkostoja. Tässä diplomityössä tutkittiin sensorimotorista ja dorsaalista tarkkaavaisuus verkostoa sekä nk. default mode -verkostoa. Nämä kolme aivoverkostoa erotettiin 15 minuutin pituisen elokuvan ("At Land", Maya Deren) katselun aikana 15 koehenkilöltä kerätystä fMRI-datasta riippumattomien komponenttien analyysillä (ICA). Estimoitujen riippumattomien komponenttien (IC) lukumäärän vaikutusta ryhmä-ICAn tuloksiin tarkasteltiin kolmella eri komponenttimäärällä. ICAlla löydettyjen aivoverkostojen toiminnallinen yhteys todettiin myös lähdekorrelaatiomenetelmällä. Korrelaatioanalyysin (ISC) avulla paikannettiin ärsykkeeseen liittyvät aivoalueet ja ICt järjestettiin ISC-kartan avulla. Näin pystyttiin tunnistamaan ärsykkeeseen reagoivat komponentit, joiden aikasarjoja verrattiin elokuvan tapahtumiin. Pienellä komponenttimäärällä (25) sensorimotorista ja tarkkaavaisuusverkostoa vastaavat komponentit sisälsivät myös näihin verkostoihin kuulumattomia aivoalueita. Kun komponenttimäärää kasvatettiin (40), ylimääräiset alueet erottuivat omiksi verkostoikseen, josta voitiin olettaa, että tämä komponenttimäärä oli lähellä oikeaa. Suurella komponenttimäärällä (70) aivoverkostot jakaantuivat pienempiin osiin. Vaikka spatiaalinen jakaantuminen oli fysiologisesti mielekästä, komponenttien aikasarjat vääristyivät liian suurella komponenttimäärällä. Tämän työn tulokset auttavat ymmärtämään, miten riippumattomien komponenttien lukumäärä vaikuttaa ryhmä-ICAn tuloksiin. Tuloksia voidaan soveltaa oikean komponenttimäärän kokeellisessa etsimisessä ryhmä-fMRI datasta.So called resting state networks (RSNs), i.e. functionally connected brain areas that are active both during rest and task conditions, are receiving growing attention in modern brain research. The first observed RSN was the motor network. Since then, several different cortical networks have been identified. In this thesis the focus was on the sensorimotor, dorsal attention and default-mode networks. Independent component analysis (ICA) was used to segregate the three cortical networks from fMRI data collected from 15 subjects who were watching a 15 minutes long film ("At land" by Maya Deren). ICA was performed at three different dimensionalities and the effect of increasing the number of component estimates was examined. The functional connectivity between brain areas occupied by the three networks was examined also with seed-based correlation. The stimulus-related brain areas were indentified with intersubject correlation (ISC) analysis and the ICs were sorted according to the spatial overlap with the ISC map. The time courses of the most stimulus related ICs were compared with events in the movie. At a low dimensionality of ICA (25), the ICs representing the sensorimotor and dorsal attention networks included brain areas that do not belong to the networks. With an intermediate number of components (40) the additional areas were separated from the networks. This dimensionality was apparently closest to the correct one. When the dimensionality was further increased (70), the networks split into subcomponents. Although the spatial splitting was physiologically sensible, the time courses of the ICs got distorted at a too high dimensionality. The results of this work contribute to understanding how the number of components affects the group-ICA results and how the correct number of ICs could be empirically controlled in group-fMRI data

Using independent components analysis to identify visually driven regions and networks in the human brain, using data collected during movie watching

Traditionally, regions involved in visual processing are mapped in the brain using simple localisers and/or anatomical techniques. As a more efficient (and interesting) alternative, Bartels & Zeki (2004) suggested that independent components analysis (ICA) could be used to segment the brain into functional regions, using data collected during movie watching. The first aim of this thesis was to explore the potential of this technique for reliable identification of visually driven regions and networks. In Chapter 2 I thoroughly and systematically explore the sensitivity of tensor ICA (TICA) to common pre-processing parameters and identify an optimal analysis pipeline. Despite some sensitivity of TICA to the parameters tested, robust components in visually responsive regions could be identified across outputs. Using an optimized pipeline, in Chapter 3 I demonstrate that visually driven components (in particular, peak voxels) are consistent across different samples and movie clips, supporting the use of this technique. In Chapter 4 I show that established resting state networks can be identified in an ICA analysis using movies, and that by increasing dimensionality sub-regions of these networks can be identified. Chapter 5 shows how these reliable components represented visual regions in the motion processing pathway. Based on the success of the technique at the group level, in Chapter 6 I apply the technique to individual observer data. Results show that functional networks and visual regions of interest can be reliably identified, supporting its use in future neuroscientific research. To address the short-comings of BOLD, the second aim of this thesis was to investigate whether MEG frequency data and fMRI bold data could be combined for analysis in a novel technique using TICA. First in Chapter 7 I address some prerequisites for a combined MEG frequency analysis using the technique. On the back of these results, I use the technique to generate interesting cross-frequency components (Chapter 8) and cross modality components using combined MEG and fMRI data (Chapter 9). These results show exciting promise for potential use in future neuroscientific work. In the final chapter, I investigate the potential use of ICA and changing dimensionality for mapping the functional hierarchy of the visual system. With development this could be a useful tool for understanding connectivity between sub-regions of functional networks. These results have important implications for the identification of visually responsive regions and for understanding neural activity during natural viewing

Troubles exécutifs et dysfonctionnement du contrôle inhibiteur dans la maladie de Parkinson

Executive impairments in Parkinson’s disease are debilitating and have no satisfying therapeutic option. This is partly due to the fact that executive functions are difficult to investigate from cognitive, neuro-functional and neurochemical standpoints. Here, we build on recent theoretical and methodological improvements to revisit executive impairments. We are interested in a function that consists in locking in advance movement initiation mechanisms in the face of uncertainty: proactive control of non-selective inhibition of action. Our leading hypothesis is that dysfunction of proactive inhibitory control could generate widespread and heterogeneous executive impairments. We thus: 1) tried to relate clinical markers of the disease to behavioral indexes of proactive control impairment; 2) identified the associated cerebral dysfunctions by means of advanced electroencephalographic methods and manipulation of deep brain stimulation of the subthalamic nucleus; and 3) investigated the neurochemical origin of this function. Our results suggest that impulsivity is not the only outcome of inhibitory impairment. Disorder of proactive inhibitory control may also account for hypo-productive behaviors such as akinesia. Results also indicate that this mechanism is of non-dopaminergic origin and relies on medial frontal and subthalamic activity. This work opens the way for new therapeutic approaches for Parkinson’s disease as well as a better understanding of clinical symptoms observed in others diseasesLes troubles exécutifs de la maladie de Parkinson sont invalidants et sans solution thérapeutique satisfaisante. La raison est liée au fait que les fonctions exécutives sont difficiles à appréhender, tant au niveau de leur modélisation cognitive qu'anatomo-fonctionnelle ou neurochimique. Ici, nous nous appuyons sur des avancées théoriques et méthodologiques récentes pour revisiter ces troubles exécutifs. Nous nous intéressons à une fonction, récemment mise en évidence, destinée à verrouiller par anticipation le déclenchement de toute action en situation d'incertitude : le contrôle proactif de l'inhibition non sélective de l'action. Notre hypothèse directrice est que son dysfonctionnement est susceptible de générer une grande variété de troubles exécutifs. Nous avons : 1) recherché les liens entre marqueurs cliniques et troubles du contrôle proactif ; 2) identifié les dysfonctionnements cérébraux associés au moyen de méthodes électroencéphalographiques innovantes combinées à l'enregistrement des effets de la stimulation du noyau sous-thalamique ; et 3) sondé l'origine neurochimique de cette fonction. Nos résultats suggèrent qu'un dysfonctionnement de l'inhibition proactive n'engendre pas uniquement des troubles impulsifs mais explique également des comportements hypo-productifs comme l'akinésie. Ils indiquent que ces troubles ne sont pas d'origine dopaminergique et qu'ils sont liés au dysfonctionnement de l'activité du cortex frontal médian et du noyau sous-thalamique. Ces travaux ouvrent la voie à de nouvelles perspectives thérapeutiques pour la maladie de Parkinson et à une meilleure appréhension de la clinique d'autres pathologie