Around the world Alzheimer’s Disease (AD) is on the rise. Previous studies have shown the default mode network (DMN) sees changes with AD progression as the disease erodes away cortical areas. Aerobic exercise with significant increases to cardiorespiratory fitness could show neuro-protective changes to delay AD. This study will explore if functional connectivity changes in the DMN can be seen in a young adult sample by using group independent component analysis through FSL MELODIC. The young adult sample of 19 were selected from a larger study at the Brain Plasticity and Neuroimaging Laboratory at Boston University. The participants engaged in a twelve-week exercise intervention in either a strength training or aerobic training group. They also completed pre-intervention and post-intervention resting-state fMRI scans to evaluate change in functional connectivity in the default mode network. Cardiorespiratory fitness was assessed using a modified Balke protocol with pre-intervention and post-intervention VO2 max percentiles being used. Through two repeated-measure ANOVA analyses, this study found no significant increase in mean functional connectivity or cardiorespiratory fitness in the young adult sample. While improvements in mean VO2 max percentile and functional connectivity would have been seen with a larger sample size, this study adds to the literature by suggesting if fitness does not improve significantly, neither will functional connectivity in the default mode network.

Orientador: Prof. Dr. João Ricardo Sato / The brain is a complex system organized in structurally segregated and functionally specialized regions. The brain areas are composed of neuronal networks interconnected by axonal pathways that integrate through correlated neural activity. Recent studies on neural connectivity using graph theoretical analysis have revealed that brain networks interact through densely connected regions with high topological value called hubs. Previous studies of Default Mode Network (DMN), one of the most important resting-state networks, have improved the understanding of the intrinsic neuronal activity and the dynamics of the human brain. Spontaneous brain activity and Resting-State Functional Connectivity (RSFC) patterns of Resting-State Network (RSN) are essential for the comprehension of the brain function. Neuroimaging techniques such as functional Near Infrared Spectroscopy (fNIRS) make these studies possible. Thus, the main objective of this study was to investigate the RSFC using Eigenvector Centrality (EVC) measure of graph theory in fNIRS data. This work has demonstrated the effectiveness of the graph analysis for detection of hubs and mcommunities, and identified brain regions associated with rich-club, that integrates highly interconnected hubs and plays a central role in the flow and integration of Information throughout the brain. One can also conclude from the RSFC analysis the existence of functional hubs associated with DMN. / Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Engenharia da Informação, 2017. / O cérebro é um sistema complexo organizado em regiões segregadas estruturalmente e especializadas funcionalmente que são compostas por redes neuronais interconectadas por vias axonais que se integram através de atividade neural correlacionada. Estudos recentes sobre conectividade neural usando teoria de grafos revelaram que as redes cerebrais interagem através de regiões densamente conectadas e com alto valor topológico denominadas hubs. Dentre as redes existentes destaca-se, por sua contribuição para a melhor compreensão do funcionamento do cérebro humano, a rede de modo padrão (Default Mode Network, DMN). A atividade espontânea do cérebro e os padrões de conectividade funcional (Resting-State Functional Connectivity, RSFC) das redes cerebrais na condição de repouso (Resting-State Network, RSN) também se tornam essenciais nos estudos que visam compreender a função desse órgão, estudos esses possibilitados graças às técnicas de neuroimagem destacando-se a espectroscopia funcional no infravermelho próximo (functional Near Infrared Spectroscopy, fNIRS). Assim, o objetivo principal deste estudo foi investigar a RSFC usando a medida de centralidade do autovetor (Eigenvector Centrality, EVC), técnica pertencente à teoria de grafos, em dados de fNIRS. Este estudo pode demonstrar a eficácia da metodologia empregada para analisar a RSFC além de revelar a existência de um núcleo estrutural, denominado hub complex, densamente conectado (rich-club), que integra hubs altamente interligados e desempenha papel central no fluxo e integração da informação ao longo do cérebro. Pode-se também concluir a partir da análise da RSFC a existência de hubs funcionais associados à DMN.

Abstract The concept of resting state functional magnetic resonance imaging (fMRI) is built onto an original finding in 1995 that brain hemispheres present synchronous signal fluctuations with distinct patterns. fMRI measurements rely on blood oxygenation changes that indirectly mirror neural activity. Therefore, the origin of functional connectivity patterns, resting state networks (RSNs), has been a widely debated research question and numerous contributing factors have been identified. According to current understanding the fluctuations reflect maintenance of the system integrity in addition to spontaneous thought and action processes in the resting state. A popular method to study the functional connectivity in resting state fMRI is spatial independent component analysis (ICA) that decomposes signal sources into statistically independent components. The dichotomy of functional specialization versus functional integration has a correspondence in fMRI studies where RSNs play the integrative viewpoint of brain function. Although canonical large-scale RSNs are broadly distributed they also express modularity that can be accomplished by ICA with a high number of estimated components. The characteristics of high ICA dimensionality are broadly investigated in the thesis. An enduring issue in resting state research has been the confounding noise sources like motion and cardiorespiratory processes which may hamper the analysis. In this thesis the ability of ICA to separate these noise sources from the default mode network, a major RSN, is studied. Additionally, the suitability of ICA for full frequency spectrum analysis, a relatively rare setting in biosignal analysis, is investigated. The results of the thesis support the viewpoint of ICA as a robust analysis method for functional connectivity analysis. Cardiorespiratory and motion induced noise did not confound the functional connectivity analyses with ICA. High dimensional ICA provided better signal source separation, revealed the modular structure of the RSNs and pinpointed the specific aberrations in the autism spectrum disorder population. ICA was also found applicable for fully explorative analysis in both the spatial and temporal domains and indicated functional connectivity changes induced by transcranial bright light stimulation. / Tiivistelmä Konsepti lepotilan tutkimisesta toiminnallisella magneettikuvauksella (engl. functional magnetic resonance imaging, fMRI) on rakentunut vuonna 1995 tehdylle löydökselle aivopuoliskojen välillä synkronisesta signaalivaihtelusta. Mittaukset perustuvat veren hapetuksen muutoksiin, jotka epäsuorasti heijastelevat hermostollista toimintaa. Tämän takia toiminnallisen kytkennällisyyden muodot, lepotilaverkostot, ovat olleet laajasti väitelty tutkimusaihe ja monia verkostoihin vaikuttavia tekijöitä onkin tunnistettu. Nykykäsityksen mukaan signaalivaihtelut lepotilassa heijastelevat järjestelmän yhtenäisyyden ylläpitoa spontaanin ajattelun ja toiminnan lisäksi. Suosittu menetelmä toiminnallisen kytkennällisyyden tutkimiseen lepotilan fMRI:ssä on spatiaalinen itsenäisten komponenttien analyysi (engl. independent component analysis, ICA), joka hajottaa signaalilähteet tilastollisesti itsenäisiin komponentteihin. Aivotoiminnan mallintamisessa kahtiajaolla toiminnalliseen erikoistumiseen ja toiminnalliseen integraatioon on vastaavuus fMRI-tutkimukseen, jossa lepotilaverkostot vastaavat toiminnallisen integraation näkökulmasta. Vaikka kanoniset lepotilaverkostot ovat laaja-alaisia, ne ovat toisaalta modulaarisia, jota voidaan tutkia tutkimalla korkean komponenttimäärän ICA-hajotelmaa. Korkea- dimensioisen ICA-hajotelman ominaisuuksia tutkitaan laajasti tässä väitöskirjassa. Kestoaihe lepotilatutkimuksessa on ollut analyysiä hankaloittavien kohinalähteiden kuten liikkeen ja kardiorespiratoristen prosessien vaikutus. Väitöskirjassa tutkitaan ICA:n kykyä erotella kohinalähteitä ’default mode’ -verkostosta, joka on merkittävin lepotilaverkosto. Lisäksi tutkitaan ICA:n soveltuvuutta täyden taajuuskaistan analysointiin, joka on verrattain harvinaista biosignaalien analyysissä. Väitöskirjan tulokset tukevat näkemystä ICA:n suorituskyvystä toiminnallisen kytkennällisyyden analyysissä. Kardiorespiratorinen ja liikkeestä lähtöisin oleva kohina ei häirinnyt merkittävästi ICA-tuloksia. Korkeadimensioinen ICA tarjosi paremman erottelun signaalilähteille, paljasti lepotilaverkostojen modulaarisen rakenteen ja määritti erityisen poikkeaman autismin kirjon oireyhtymän populaatiossa. ICA:n havaittiin olevan soveltuva täyseksploratiiviselle analyysille ajassa ja avaruudessa; tulos viittaa toiminnallisen kytkennällisyyden muutoksiin kallon läpäisevän kirkasvalostimulaation aikaansaamana.