Physiological basis and image processing in functional magnetic resonance imaging: Neuronal and motor activity in brain

Functional magnetic resonance imaging (fMRI) is recently developing as imaging modality used for mapping hemodynamics of neuronal and motor event related tissue blood oxygen level dependence (BOLD) in terms of brain activation. Image processing is performed by segmentation and registration methods. Segmentation algorithms provide brain surface-based analysis, automated anatomical labeling of cortical fields in magnetic resonance data sets based on oxygen metabolic state. Registration algorithms provide geometric features using two or more imaging modalities to assure clinically useful neuronal and motor information of brain activation. This review article summarizes the physiological basis of fMRI signal, its origin, contrast enhancement, physical factors, anatomical labeling by segmentation, registration approaches with examples of visual and motor activity in brain. Latest developments are reviewed for clinical applications of fMRI along with other different neurophysiological and imaging modalities

Decoupling function and anatomy in atlases of functional connectivity patterns: Language mapping in tumor patients

In this paper we construct an atlas that summarizes functional connectivity characteristics of a cognitive process from a population of individuals. The atlas encodes functional connectivity structure in a low-dimensional embedding space that is derived from a diffusion process on a graph that represents correlations of fMRI time courses. The functional atlas is decoupled from the anatomical space, and thus can represent functional networks with variable spatial distribution in a population. In practice the atlas is represented by a common prior distribution for the embedded fMRI signals of all subjects. We derive an algorithm for fitting this generative model to the observed data in a population. Our results in a language fMRI study demonstrate that the method identifies coherent and functionally equivalent regions across subjects. The method also successfully maps functional networks from a healthy population used as a training set to individuals whose language networks are affected by tumors.National Science Foundation (U.S.). Division of Information & Intelligent Systems (Collaborative Research in Computational Neuroscience Grant 0904625)National Science Foundation (U.S.) (CAREER Grant 0642971)National Institutes of Health (U.S.) (National Center for Research Resources (U.S.)/Neuroimaging Analysis Center (U.S.) P41-RR13218)National Institutes of Health (U.S.) (National Institute for Biomedical Imaging and Bioengineering (U.S.)/Neuroimaging Analysis Center (U.S.) P41-EB-015902)National Institutes of Health (U.S.) (National Institute for Biomedical Imaging and Bioengineering (U.S.)/National Alliance for Medical Image Computing (U.S.) U54-EB005149)National Institutes of Health (U.S.) (U41RR019703)National Institutes of Health (U.S.) (Eunice Kennedy Shriver National Institute of Child Health and Human Development (U.S.) R01HD067312)National Institutes of Health (U.S.) (P01CA067165)Brain Science FoundationKlarman Family FoundationEuropean Commission (FP7/2007–2013) n°257528 (KHRESMOI))European Commission (330003 (FABRIC))Austrian Science Fund (P 22578-B19 (PULMARCH)

Combining spatial priors and anatomical information for fMRI detection

In this paper, we analyze Markov Random Field (MRF) as a spatial regularizer in fMRI detection. The low signal-to-noise ratio (SNR) in fMRI images presents a serious challenge for detection algorithms, making regularization necessary to achieve good detection accuracy. Gaussian smoothing, traditionally employed to boost SNR, often produces over-smoothed activation maps. Recently, the use of MRF priors has been suggested as an alternative regularization approach. However, solving for an optimal configuration of the MRF is NP-hard in general. In this work, we investigate fast inference algorithms based on the Mean Field approximation in application to MRF priors for fMRI detection. Furthermore, we propose a novel way to incorporate anatomical information into the MRF-based detection framework and into the traditional smoothing methods. Intuitively speaking, the anatomical evidence increases the likelihood of activation in the gray matter and improves spatial coherency of the resulting activation maps within each tissue type. Validation using the receiver operating characteristic (ROC) analysis and the confusion matrix analysis on simulated data illustrates substantial improvement in detection accuracy using the anatomically guided MRF spatial regularizer. We further demonstrate the potential benefits of the proposed method in real fMRI signals of reduced length. The anatomically guided MRF regularizer enables significant reduction of the scan length while maintaining the quality of the resulting activation maps.National Institutes of Health (U.S.) (National Institute for Biomedical Imaging and Bioengineering (U.S.)/National Alliance for Medical Image Computing (U.S.) Grant U54-EB005149)National Science Foundation (U.S.) (Grant IIS 9610249)National Institutes of Health (U.S.) (National Center for Research Resources (U.S.)/Biomedical Informatics Research Network Grant U24-RR021382)National Institutes of Health (U.S.) (National Center for Research Resources (U.S.)/Neuroimaging Analysis Center (U.S.) Grant P41-RR13218)National Institutes of Health (U.S.) (National Institute of Neurological Disorders and Stroke (U.S.) Grant R01-NS051826)National Science Foundation (U.S.) (CAREER Grant 0642971)National Science Foundation (U.S.). Graduate Research FellowshipNational Center for Research Resources (U.S.) (FIRST-BIRN Grant)Neuroimaging Analysis Center (U.S.

Functional hierarchy of oculomotor and visual motion subnetworks within the human cortical optokinetic system

© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. Optokinetic look nystagmus (look OKN) is known to engage cortical visual motion and oculomotor hubs. Their functional network hierarchy, however, and the role of the cingulate eye field (CEF) and the dorsolateral prefrontal cortex (DLPFC) in particular have not been investigated. We used look OKN in fMRI to identify all cortical visual motion and oculomotor hubs involved. Using these activations as seed regions, we employed hierarchical clustering in two differing resting state conditions from a separate public data set. Robust activations in the CEF highlight its functional role in OKN and involvement in higher order oculomotor control. Deactivation patterns indicate a decreased modulatory involvement of the DLPFC. The hierarchical clustering revealed a changeable organization of the eye fields, hMT, V3A, and V6 depending on the resting state condition, segregating executive from higher order visual subnetworks. Overall, hierarchical clustering seems to allow for a robust delineation of physiological cortical networks

Functional and structural MRI image analysis for brain glial tumors treatment

Cotutela con il Dipartimento di Biotecnologie e Scienze della Vita, Universiità degli Studi dell'Insubria.openThis Ph.D Thesis is the outcome of a close collaboration between the Center for Research in Image Analysis and Medical Informatics (CRAIIM) of the Insubria University and the Operative Unit of Neurosurgery, Neuroradiology and Health Physics of the University Hospital ”Circolo Fondazione Macchi”, Varese. The project aim is to investigate new methodologies by means of whose, develop an integrated framework able to enhance the use of Magnetic Resonance Images, in order to support clinical experts in the treatment of patients with brain Glial tumor. Both the most common uses of MRI technology for non-invasive brain inspection were analyzed. From the Functional point of view, the goal has been to provide tools for an objective reliable and non-presumptive assessment of the brain’s areas locations, to preserve them as much as possible at surgery. From the Structural point of view, methodologies for fully automatic brain segmentation and recognition of the tumoral areas, for evaluating the tumor volume, the spatial distribution and to be able to infer correlation with other clinical data or trace growth trend, have been studied. Each of the proposed methods has been thoroughly assessed both qualitatively and quantitatively. All the Medical Imaging and Pattern Recognition algorithmic solutions studied for this Ph.D. Thesis have been integrated in GliCInE: Glioma Computerized Inspection Environment, which is a MATLAB prototype of an integrated analysis environment that offers, in addition to all the functionality specifically described in this Thesis, a set of tools needed to manage Functional and Structural Magnetic Resonance Volumes and ancillary data related to the acquisition and the patient.openInformaticaPedoia, ValentinaPedoia, Valentin

Magnetoenkefalografian ja toiminnallisen magneettikuvauksen vertailu ja yhdistäminen tunto- ja liikejärjestelmän tutkimuksessa

MEG directly measures the neuronal events and has greater temporal resolution than fMRI, which has limited temporal resolution mainly due to the larger timescale of the hemodynamic response. On the other hand fMRI has advantages in spatial resolution, while the localization results with MEG can be ambiguous due to the non-uniqueness of the electromagnetic inverse problem. Thus, these methods could provide complementary information and could be used to create both spatially and temporally accurate models of brain function. We investigated the degree of overlap, revealed by the two imaging methods, in areas involved in sensory or motor processing in healthy subjects and neurosurgical patients. Furthermore, we used the spatial information from fMRI to construct a spatiotemporal model of the MEG data in order to investigate the sensorimotor system and to create a spatiotemporal model of its function. We compared the localization results from the MEG and fMRI with invasive electrophysiological cortical mapping. We used a recently introduced method, contextual clustering, for hypothesis testing of fMRI data and assessed the the effect of neighbourhood information use on the reproducibility of fMRI results. Using MEG, we identified the ipsilateral primary sensorimotor cortex (SMI) as a novel source area contributing to the somatosensory evoked fields (SEF) to median nerve stimulation. Using combined MEG and fMRI measurements we found that two separate areas in the lateral fissure may be the generators for the SEF responses from the secondary somatosensory cortex region. The two imaging methods indicated activation in corresponding locations. By using complementary information from MEG and fMRI we established a spatiotemporal model of somatosensory cortical processing. This spatiotemporal model of cerebral activity was in good agreement with results from several studies using invasive electrophysiological measurements and with anatomical studies in monkey and man concerning the connections between somatosensory areas. In neurosurgical patients, the MEG dipole model turned out to be more reliable than fMRI in the identification of the central sulcus. This was due to prominent activation in non-primary areas in fMRI, which in some cases led to erroneous or ambiguous localization of the central sulcus.Magnetoenkefalografia (MEG) mittaa suoraan aivojen hermosolujen sähköistä toimintaa ja sillä on parempi ajallinen erotuskyky kuin aivojen aktivaation aiheuttamia paikallisen verenkierron muutoksia kuvaava toiminnallinen magneettikuvaus (TMK). TMK:lla on toisaalta etuja paikannuksessa MEG:hen nähden ja MEG:llä saadut paikannustulokset ovat monikäsitteisiä. Nämä menetelmät voivat täydentää toisiaan ja yhdessä niillä voidaan saada tarkempi ajallinen ja paikallinen kuva aivojen toiminnasta. Käytimme näitä kahta menetelmää aivojen tunto- ja liikejärjestelmän toiminnan kuvantamisessa terveillä koehenkilöillä ja neurokirurgisilla potilailla. Tutkimme menetelmillä saatavan paikannustuloksen yhteneväisyyttä ja käytimme TMK:sta saatavaa paikannustietoa MEG:llä mitattujen aivojen magneetisten vasteiden mallinuksessa luoden mallin aivojen tuntojärjestelmän toiminnasta. Neurokirurgisilla potilailla vertasimme kuvantamismenetelmien tuloksia leikkauksenaikaiseen sähköiseen liikeaivokuoren paikannukseen. Tutkimuksessa testattiin ja sovellettin kehittämiämme uusia kuva-analyysimenetelmiä. MEG:llä ja TMK:lla havaitsimme viitteitä aktivaatiosta tuntoärsykkeen kanssa samanpuoleisella primäärillä tuntoaivokuorella. Tuloksemme viittaavat lisäksi siihen että aivojen lateraalisessa fissuurassa on ainakin kaksi erillistä lähdealuetta jotka tuottavat magneettisia tuntoherätevasteita. Mallimme aivojen toiminnasta tuntoarsykkeen käsittelyn aikana vastasi hyvin kirjallisuudessa raportoituja suoraan aivoista mitattuja eri alueiden aktivaatioaikoja. TMK-analyysimenetelmiä vertailtaessa todettiin kuva-alkion naapurustoinformaatiota käyttävien menetelmien tuottavan paremmin toistettavia tuloksia. Kehittämämme menetelmä rajasi tarkemmin aivojen aktivaatioalueen ja oli muita menetelmiä herkempi havaitsemaan heikkoja aktivaatioita. Paikannettaessa aivojen keskusuurretta leikkauksen suunnittelua ja riskien arviointia varten MEG tuotti luotettavamman tuloksen kuin TMK jossa osalla potilaista aktivaatiot muilla kuin primäärillä liikeaivokuorella olivat voimakkaimpia vaikeuttaen tulosten tulkintaa

Functional and structural MRI image analysis for brain glial tumors treatment

This Ph.D Thesis is the outcome of a close collaboration between the Center for Research in Image Analysis and Medical Informatics (CRAIIM) of the Insubria University and the Operative Unit of Neurosurgery, Neuroradiology and Health Physics of the University Hospital ”Circolo Fondazione Macchi”, Varese. The project aim is to investigate new methodologies by means of whose, develop an integrated framework able to enhance the use of Magnetic Resonance Images, in order to support clinical experts in the treatment of patients with brain Glial tumor. Both the most common uses of MRI technology for non-invasive brain inspection were analyzed. From the Functional point of view, the goal has been to provide tools for an objective reliable and non-presumptive assessment of the brain’s areas locations, to preserve them as much as possible at surgery. From the Structural point of view, methodologies for fully automatic brain segmentation and recognition of the tumoral areas, for evaluating the tumor volume, the spatial distribution and to be able to infer correlation with other clinical data or trace growth trend, have been studied. Each of the proposed methods has been thoroughly assessed both qualitatively and quantitatively. All the Medical Imaging and Pattern Recognition algorithmic solutions studied for this Ph.D. Thesis have been integrated in GliCInE: Glioma Computerized Inspection Environment, which is a MATLAB prototype of an integrated analysis environment that offers, in addition to all the functionality specifically described in this Thesis, a set of tools needed to manage Functional and Structural Magnetic Resonance Volumes and ancillary data related to the acquisition and the patient

Investigation of Memory Related Cortical Thalamic Circuitry in the Human Brain

This dissertation examined the role of medial prefrontal cortex (mPFC) and the hippocampus (HC) in episodic memory, and provides a novel approach to identify the midline thalamus mediating mPFC-HC interactions in humans. The mPFC and HC are critical to the temporal organization of episodic memory, and these interactions are disrupted in several mental health and neurological disorders. In the first study, I provide evidence that the mPFC is involved in ordinal retrieval, and the HC is active in temporal context retrieval in remembering the order of when events happen. In the second study, I focus on the anatomical basis of the mPFC-HC interactions which is reliant on the midline thalamus. I review in detail the anatomy of the midline thalamus both in location, and connectivity profile with the rest of the brain comparing the extensive anatomical evidence in rodents with the available evidence in monkeys and humans. This section also elaborates on the role of the midline thalamus in memory, stress regulation, wakefulness, and feeding behavior, and how pathological markers along the midline thalamus are a vanguard of several neurological disorders including Alzheimer’s Disease, schizophrenia, depression, and drug addiction. Lastly, I devised a new approach to identify the midline thalamus in humans in vivo using diffusion weighted imaging, capitalizing on known fiber connections gleaned from non-human animals, focusing on connections between the midline thalamus and the mPFC, medial temporal lobe and the nucleus accumbens. The success of this approach is promising for translational imaging. Overall, this dissertation provides new evidence on 1) complementary functional roles of the mPFC and HC in sequence memory, 2) a cross-species anatomical framework for understanding the midline thalamus in humans and neurological disorders, and 3) a new method for non-invasive identification of the midline thalamus in humans in vivo. Thus, this dissertation provides a new fundamental understanding of mPFC-midline thalamic-HC circuit in humans and tools for its non-invasive study in human disease