Dissertations / Theses on the topic 'Resting state in fMRI'

Diffusion weighted imaging (DWI) and dynamic susceptibility contrast-enhanced (DSC) perfusion-weighted imaging (PWI) are commonly employed in clinical practice and in research to give pathophysiological information for patients with acute ischemic stroke. DWI is thought to roughly reflect the severely damaged infarct core, while DSC-PWI reflects the area of hypoperfusion. The volumetric difference between DWI and DSC-PWI is termed the PWI/DWI-mismatch, and has been suggested as an MRI surrogate of the ischemic penumbra. However, due to the application of a contrast agent, which has potentially severe side-effects (e.g., nephrogenic systemic fibrosis), the DSC-PWI precludes repetitive examinations for monitoring purposes. New approaches are being sought to overcome this shortcoming. BOLD (blood oxygen-level dependent) signal can reflect the metabolism of blood oxygen in the brain and hemodynamics can be assessed with resting-state fMRI. The aim of this thesis was to use resting-state fMRI as a new approach to give similar information as DSC-PWI. This thesis comprises two studies: In the first study (see Chapter 2), two resting-state fMRI methods, local methods which compare low frequency amplitudes between two hemispheres and a k-means clustering approach, were applied to investigate the functional damage of patients with acute ischemic stroke both in the time domain and frequency domain. We found that the lesion areas had lower amplitudes than contralateral homotopic healthy tissues. We also differentiated the lesion areas from healthy tissues using a k-means clustering approach. In the second study (see Chapter 3), time-shift analysis (TSA), which assesses time delays of the spontaneous low frequency fluctuations of the resting-state BOLD signal, was applied to give similar pathophysiological information as DSC-PWI in the acute phase of stroke. We found that areas which showed a pronounced time delay to the respective mean time course were very similar to the hypoperfusion area. In summary, we suggest that the resting-state fMRI methods, especially the time-shift analysis (TSA), may provide comparable information to DSC-PWI and thus serve as a useful diagnostic tool for stroke MRI without the need for the application of a contrast agent.

Negli ultimi anni la teoria dei network è stata applicata agli ambiti più diversi, mostrando proprietà caratterizzanti tutti i network reali. In questo lavoro abbiamo applicato gli strumenti della teoria dei network a dati cerebrali ottenuti tramite MRI funzionale “resting”, provenienti da due esperimenti. I dati di fMRI sono particolarmente adatti ad essere studiati tramite reti complesse, poiché in un esperimento si ottengono tipicamente più di centomila serie temporali per ogni individuo, da più di 100 valori ciascuna. I dati cerebrali negli umani sono molto variabili e ogni operazione di acquisizione dati, così come ogni passo della costruzione del network, richiede particolare attenzione. Per ottenere un network dai dati grezzi, ogni passo nel preprocessamento è stato effettuato tramite software appositi, e anche con nuovi metodi da noi implementati. Il primo set di dati analizzati è stato usato come riferimento per la caratterizzazione delle proprietà del network, in particolare delle misure di centralità, dal momento che pochi studi a riguardo sono stati condotti finora. Alcune delle misure usate indicano valori di centralità significativi, quando confrontati con un modello nullo. Questo comportamento `e stato investigato anche a istanti di tempo diversi, usando un approccio sliding window, applicando un test statistico basato su un modello nullo pi`u complesso. Il secondo set di dati analizzato riguarda individui in quattro diversi stati di riposo, da un livello di completa coscienza a uno di profonda incoscienza. E' stato quindi investigato il potere che queste misure di centralità hanno nel discriminare tra diversi stati, risultando essere dei potenziali bio-marcatori di stati di coscienza. E’ stato riscontrato inoltre che non tutte le misure hanno lo stesso potere discriminante. Secondo i lavori a noi noti, questo `e il primo studio che caratterizza differenze tra stati di coscienza nel cervello di individui sani per mezzo della teoria dei network.

Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2010.
Committee Chair: Hu, Xiaoping; Committee Co-Chair: Vachtsevanos, George; Committee Member: Butera, Robert; Committee Member: Gurbaxani, Brian; Committee Member: Mayberg, Helen; Committee Member: Yezzi, Anthony. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Major advances in resting-state functional magnetic resonance imaging (fMRI) techniques in the last two decades have provided a tool to better understand the functional organization of the brain both in health and illness. Despite such developments, characterizing regulation and cerebral representation of mind wandering, which occurs unavoidably during resting-state fMRI scans and may induce variability of the acquired data, remains a work in progress. Here, we demonstrate that a decrease or decoupling in functional connectivity involving the caudate nucleus, insula, medial prefrontal cortex and other domain-specific regions was associated with more sustained mind wandering in particular thought domains during resting-state fMRI. Importantly, our findings suggest that temporal and between-subject variations in functional connectivity of above-mentioned regions might be linked with the continuity of mind wandering. Our study not only provides a preliminary framework for characterizing the maintenance and cerebral representation of different types of mind wandering, but also highlights the importance of taking mind wandering into consideration when studying brain organization with resting-state fMRI in the future.

Functional magnetic resonance imaging (fMRI) has shown great potential in studying the underlying neural systems. Functional connectivity measured by fMRI provides an efficient approach to study the interactions and relationships between different brain regions. However, functional connectivity studies require accurate definition of brain regions, which is often difficult and may not be achieved through anatomical landmarks. In this thesis, we present a novel framework for parcellation of a brain region into functional subunits based on their connectivity patterns with other reference brain regions. The proposed method takes the prior neurological information into consideration and aims at finding spatially continuous and functionally consistent sub-regions in a given brain region. The proposed framework relies on a sparse spatially regularized fused lasso regression model for feature extraction. The usual lasso model is a linear regression model commonly applied in high dimensional data such as fMRI signals. Compared with lasso, the proposed model further considers the spatial order of each voxel and thus encourages spatially and functionally adjacent voxels to share similar regression coefficients despite of the possible spatial noise. In order to achieve the accurate parcellation results, we propose a process by iteratively merging voxels (groups) and tuning the parameters adaptively. In addition, a Graph-Cut optimization algorithm is adopted for assigning the overlapped voxels into separate sub-regions. With spatial information incorporated, spatially continuous and functionally consistent subunits can be obtained which are desired for subsequent brain connectivity analysis. The simulation results demonstrate that the proposed method could reliably yield spatially continuous and functionally consistent subunits. When applied to real resting state fMRI datasets, two consistent functional subunits could be obtained in the putamen region for all normal subjects. Comparisons between the results of the Parkinson’s disease group and the normal group suggest that the obtained results are in accordance with our medical assumption. The extracted functional subunits themselves are of great interest in studying the influence of aging and a certain disease, and they may provide us deeper insights and serve as a biomarker in our future Parkinson’s disease study.
Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate

The sensorimotor cortex is a brain region comprising the primary motor cortex (MI) and the primary somatosensory (SI) cortex. In humans, investigation into these regions suggests that MI and SI are involved in the modulation and control of motor and somatosensory processing, and are somatotopically organized according to a body plan (Penfield & Boldrey, 1937). Additional investigations into somatotopic mapping in relation to the limbs in the peripheral nervous system and SI in central nervous system have further born out the importance of this body-based organization (Wall & Dubner, 1972). Understanding the nature of the sensorimotor cortex‟s structure and function has broad implications not only for human development, but also motor learning (Taubert et al., 2011) and clinical applications in structural plasticity in Parkinson‟s disease (Sehm et al., 2014), among others. The aim of the present thesis is to identify functionally meaningful subregions within the sensorimotor cortex via parcellation analysis. Previously, cerebral subregions were identified in postmortem brains by invasive procedures based on histological features (Brodmann, 1909; Vogt. & Vogt., 1919; Economo, 1926; Sanides, 1970). One widely used atlas is based on Brodmann areas (BA). Brodmann divided human brains into several areas based on the visually inspected cytoarchitecture of the cortex as seen under a microscope (Brodmann, 1909). In this atlas, BA 4, BA 3, BA 1 and BA 2 together constitute the sensorimotor cortex (Vogt. & Vogt., 1919; Geyer et al., 1999; Geyer et al., 2000). However, BAs are incapable of delineating the somatotopic detail reflected in other research (Blankenburg et al., 2003). And, although invasive approaches have proven reliable in the discovery of functional parcellation in the past, such approaches are marked by their irreversibility which, according to ethical standards, makes them unsuitable for scientific inquiry. Therefore, it is necessary to develop non-invasive approaches to parcellate functional brain regions. In the present study, a non-invasive and task-free approach to parcellate the sensorimotor cortex with resting-state fMRI was developed. This approach used functional connectivity patterns of brain areas in order to delineate functional subregions as connectivity-based parcellations (Wig et al., 2014). We selected two adjacent BAs (BA 3 and BA 4) from a standard template to cover the area along the central sulcus (Eickhoff et al., 2005). Then subregions within this area were generated using resting-state fMRI data. These subregions were organized somatotopically from medial-dorsal to ventral-lateral (corresponding roughly to the face, hand and foot regions, respectively) by comparing them with the activity maps obtained by using independent motor tasks. Interestingly, resting-state parcellation map demonstrated higher correspondence to the task-based divisions after individuals had performed motor tasks. We also observed higher functional correlations between the hand area and the foot and tongue area, respectively, than between the foot and tongue regions. The functional relevance of those subregions indicates the feasibility of a wide range of potential applications to brain mapping (Nebel et al., 2014). In sum, the present thesis provides an investigation of functional network, functional structure, and properties of the sensorimotor cortex by state-of-art neuroimaging technology. The methodology and the results of the thesis hope to carry on the future research of the sensorimotor system.

Functional MRI is a method of imaging changes in blood oxygenation that accompany neural activity in the brain. A specific area within fMRI studies investigates what the brain is doing when it is not being stimulated. It is postulated that there are distinctly separate regions of the brain that are connected based upon functional relations and that these connected regions synchronously communicate even during rest. Resting state connectivity has become a tool to investigate neurological disorders in humans without specific knowledge of the mechanisms that correlate neural activity with brain metabolism and blood flow. This work attempts to characterize resting state connectivity in the rat brain to establish a model that will help elucidate the relationship between functional connectivity, as measured with fMRI, and brain function. Four analysis techniques, power spectrum estimation, cross correlation analysis, principle component analysis, and independent component analysis, are employed to examine data acquired during a non-stimulation, single-slice, gradient echo EPI sequence in search of functionally connected, spatially distant regions of the rat brain.

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

Spontaneous brain activity, measured under the absence of any overt task, has been investigated under the label of “resting state” for about 20 years with rising interest. While it was known since the beginnings of modern electrophysiology that the brain exhibits spontaneous fluctuations also during rest, the discovery, in 1995, that these fluctuations possess a robust spatio-temporal structure had a profound impact on how we understand and investigate brain activity. In this dissertation, we characterize the spatio-temporal dynamics of resting state on a macroscopic level using fMRI recordings from humans and combining novel data analysis tools with theoretical models on the level of the whole brain. We demonstrate the presence of common patterns of functional connectivity, known as resting state networks (RSNs), that evolve in time in both empirical and model data. We show that spontaneous fluctuations and their statistics are determined by the structure of the brain network and its dynamics.
La actividad cerebral espontánea, o actividad de reposo, es aquella que uno puede registrar cuando el cerebro no está involucrado en ninguna tarea impuesta del exterior (tal como sería la presentación de un estímulo). El estudio de la actividad de reposo ha conocido un interés creciente durante los últimos 20 años. Si bien las fluctuaciones en la actividad de reposo eran conocidas desde los inicios de la electrofisiología moderna, el descubrimiento, en 1995, de que estas fluctuaciones muestran patrones espaciotemporales robustos ha tenido un impacto profundo en la manera de entender e investigar la actividad del cerebro. En esta disertación caracterizamos la dinámica espaciotemporal de la actividad de reposo a nivel macroscópico usando registros de fMRI en humanos y combinando nuevas herramientas de análisis y modelos teóricos del cerebro a gran escala. Observamos patrones comunes de conectividad funcional evolviendo en el tiempo tanto en los datos empíricos como en las simulaciones. Demostramos que las fluctuaciones de reposo y su estadística son determinadas por la estructura de la red cerebral y su dinámica.

Obesity is associated with structural and functional alterations in brain areas that are often functionally distinct and anatomically distant. This suggests that obesity is associated with differences in functional connectivity of regions distributed across the brain. However, studies addressing whole brain functional connectivity in obesity remain scarce. Here, we compared voxel-wise degree centrality and eigenvector centrality between participants with obesity (n=20) and normal-weight controls (n=21). We analyzed resting state and task-related fMRI data acquired from the same individuals. Relative to normal-weight controls, participants with obesity exhibited reduced degree centrality in the right middle frontal gyrus in the resting-state condition. During the task fMRI condition, obese participants exhibited less degree centrality in the left middle frontal gyrus and the lateral occipital cortex along with reduced eigenvector centrality in the lateral occipital cortex and occipital pole. Our results highlight the central role of the middle frontal gyrus in the pathophysiology of obesity, a structure involved in several brain circuits signaling attention, executive functions and motor functions. Additionally, our analysis suggests the existence of task-dependent reduced centrality in occipital areas; regions with a role in perceptual processes and that are profoundly modulated by attention.

Parcellation of brain imaging data is desired for proper neurological interpretation in resting-state functional magnetic resonance imaging (rs-fMRI) data. Some methods require specifying a number of parcels and using model selection to determine the number of parcels with rs-fMRI data. However, this generalization does not fit with all subjects in a given dataset. A method has been proposed using parametric formulas for the distribution of modularity in random networks to determine the statistical significance between parcels. In this thesis, we propose an agglomerative clustering algorithm using parametric formulas for the distribution of modularity in random networks, coupled with a false discovery rate (FDR) controller to parcellate rsfMRI data. The proposed method controls the FDR to reduce the number of false positives and incorporates spatial information to ensure the regions are spatially contiguous. Simulations demonstrate that our proposed FDRcontrolled agglomerative clustering algorithm yields more accurate results when compared with existing methods. We applied our proposed method to a rs-fMRI dataset and found that it obtained higher reproducibility compared to the Ward hierarchical clustering method. Lastly, we compared the normalized total connectivity degree of each region within the motor network between normal subjects and Parkinson’s disease (PD) subjects using sub-regions defined by our proposed method and the entire region. We found that PD subjects without medication had a significant increase in functional connectivity compared to normal subjects in the right primary motor cortex using our sub-regions within the right primary motor cortex, whereas this significant increase was not found using the entire right primary motor cortex. These sub-regions are of great interest in studying the differences in functional connectivity between different neurological diseases, which can be used as biomarkers and may provide insight in severity of the disease.
Applied Science, Faculty of
Graduate

Clinical care for major depressive disorder (MDD) would be greatly improved if we had reliable clinical predictors of individual antidepressant treatment outcome. While, at the present time, no biomarkers have sufficiently proven utility to be ready for clinical application, several neuroimaging modalities have shown promise for such development. Attempts to combine the recently developed modality of resting-state functional Magnetic Resonance Imaging (rs-fMRI) with pharmacological challenges to explore the impact of antidepressants on resting-state brain connectivity have just begun (McCabe et al., 2011a, McCabe et al., 2011b). The aim of the current study was to investigate the effects of a single dose of the SSRI (selective serotonin reuptake inhibitor) escitalopram on resting-state functional connectivity in health.

The aim of this thesis is to analyse and describe the neurobiological basis of the systems derived from Reinforcement Sensitivity Theory (RST). To do so, we assessed the reactivity of these systems (the Behavioural Activation and Inhibition Systems and the Fight-Flight-Freeze System) by means of the Sensitivity to Punishment and Sensitivity to Reward Questionnaire (SPSRQ), as well as its brain correspondence through fMRI, both structurally and through resting state connectivity. Results confirm the frontostriatal and limbic involvement in approach (SR) and inhibition (SP) behaviours, respectively. We also observed differences linked to demographic (sex) and methodological (eyes open/closed at rest) aspects. In conclusion, results show stable brain patterns associated with the personality dimensions derived from RST. These data extend the knowledge about RST and its applicability in humans as a framework to study the predisposition toward different psychopathological disorders.
El objetivo de esta tesis es analizar y describir las bases neurobiológicas de los sistemas derivados de la Teoría de la Sensibilidad al Refuerzo (TSR). Para ello, evaluamos la reactividad de estos sistemas (los Sistemas de Activación e Inhibición Conductual y el Sistema de Lucha-Huida-Congelamiento) mediante el cuestionario de Sensibilidad al Castigo y a la Recompensa (SCSR), así como su correspondencia cerebral a través de RMf tanto a nivel estructural como de conectividad en estado de reposo. Los resultados confirman la implicación frontoestriatal y límbica en conductas de aproximación (SR) e inhibición (SC), respectivamente. También observamos diferencias ligadas a aspectos demográficos (sexo) y metodológicos (ojos abiertos/cerrados en reposo). En conclusión, los resultados muestran rasgos cerebrales estables asociados a las dimensiones de personalidad derivadas de la TSR. Estos datos amplían el conocimiento y aplicabilidad de la TSR en humanos como marco para estudiar la predisposición a diferentes trastornos psicopatológicos.

Functional Magnetic Resonance Imaging, or fMRI, is today a well established tool used to assess both resting state connectivity and task activation in neuroscience. It has also been used for the study of brain development in neonates and there are a small number of pilot studies that seek to use fMRI in utero. However, there are formidable challenges in this application as the fetus lies within the mother and is moved by her respiration as well as performing its own sporadic and unpredictable motion. Thus motion is a core issue for any fetal fMRI study. The first chapter of the thesis discusses a pipeline that was developed to analyse fetal fMRI data acquired with standard sequences. The approach addresses motion correction as a primary requirement, both to stabilise anatomical content for each voxel in a fMRI time series, but also to correct the data from other sources of image artefacts that can be modulated by movement, such as bias eld, spin history and distortions. From the results of this study, it emerges that functional MRI is feasible in the developing fetus. The magnetic properties of fetal and infant brain tissue are very different from adults, leading to a longer T2* relaxation time. This would suggest the use of longer echo times to optimise the BOLD eect, with the downside of decreasing imaging speed. Therefore, the second chapter explores the use of an echo shifted EPI (es-EPI) sequence that achieves an improved signal sensitivity while maintaining ecient sampling. The sequence has been extensively tested on phantom experiments and an improved signal detection is demonstrated on a series of fMRI experiments run on preterm and term-equivalent babies. The long T2* and the lack of air-tissue boundaries between the fetal head and the womb encourages the use of EVI as favourable tool for fetal fMRI. A main benefit of an EVI sequence could indeed be imaging speed and robustness to motion. In a third chapter, EVI fetal imaging is explored and the methods developed allowed the fetal brain to be imaged in full 3D. Despite the challenges of making this work robustly, we speculate that further refinements of the sequence could constitute the ground work with which to perform fetal fMRI in the near future.

The human brain is an ongoing dynamic system not activated by experience but nudged from intrinsic activity into new network configurations during perception and learning. Ongoing neural activity during rest is assumed to reflect these intrinsic dynamics in a relatively closed system state. Traditionally, inter-regional connectivity in this system is measured by obtaining time-locked correlations in BOLD activity using fMRI. It is well documented, however, that neural activity unfolds across time and is not isolatent to some reference point.

This exploratory study is a theoretical analysis of how a lagged analysis of resting state dynamics in fMRI could represent persistent representations of knowledge in the neocortex. A novel procedure using both surface based maps and independent component analysis (ICA) is applied to a small group of 54 adolescents. The ICA methods appear to reveal lagged structures with different information than traditional resting state analysis. The group level results are symmetrical between hemispheres and may represent high level perceptual systems. 

The components obtained from this exploration are then used to attempt understand how these knowledge systems in neocortex frame mind-wandering frequency when reading aloud in a subset of 38 individuals. The results did not correlate with any known neural systems related to mind wandering, but the methods here are unique. One of the identified components shows significant difference in the lag structure of the occipital cortex as a function of mind wandering frequency during oral reading. This demonstrates that it may be worth exploring the timing in visual system to understand why individuals mind wander when reading aloud. Reverse inference is used to interpret results and suggest future approaches.

Schizophrenia (SCZ) is a complex psychiatric disorder including various symptoms. Resting-state fMRI investigations mostly focused on functional connectivity alterations in SCZ reflecting the spontaneous activity’s spatial structure. Complementing its spatial structure, the brain’s spontaneous activity can be characterized by a complex temporal structure such as scale-free dynamics or long- range temporal correlations (LRTCs). However, it remains an open question whether the temporal structure of spontaneous brain activity, as indexed by the power-law exponent (PLE), can provide biomarkers specific to SCZ as distinguished from other psychiatric disorders like major depressive disorder (MDD) and bipolar disorder (BP). Here, we studied a large-scale cohort (n = 244) of two independent schizophrenic data sets (n = 45), MDD (n = 28), and BP patients (n = 73, in manic, depressed, and euthymic phases) and 98 healthy controls. We found significant PLE reduction in specifically the medial prefrontal cortex (mPFC) in SCZ. This was replicated in an independent sample and was shown to be specific when compared to MDD and different phases of BP. Due to its disease-specific nature, the mPFC PLE reduction may eventually serve as a biomarker for SCZ.

Major depressive disorder (MDD) is one of the most common psychiatric illnesses and causes significant disturbances in daily functioning. Research on heart-rate variability (HRV) biofeedback training suggests that HRV is an efficacious adjunct to psychotherapy in reducing depressive symptoms. The purpose of this study was to examine neurological changes in depressed individuals who were randomized to either a psychotherapy plus HRV biofeedback training or to a treatment as usual group. A control group with no history of depression was also studied. We collected psychological, physiological, and imaging data from 30 participants (10 in an experimental group, 10 in a treatment as usual group, and the other 10 in a healthy control group) at baseline and follow-up. Regions of interest (ROIs) included anterior cingulate cortex, hippocampus, and amygdala. Participants from the experimental group went through 5 weekly HRV trainings in conjunction with traditional psychotherapy approaches. The treatment as usual group only received psychotherapy. The healthy controls did not receive any HRV training or therapy services. Overall, we found significant improvements in the experimental group's depression score, overall distress level, and HRV measurements relative to the TAU and control groups. However, we did not find significant HRV and resting-state connectivity group differences among experimental group relative to healthy controls. Together, results suggest that HRV training helps to reduce depressed participants' overall distress level and depressive symptoms. However, findings do not show any changes in participants' imaging data. These findings serve as pilot data on literature related to HRV biofeedback training in a depressed population.

Emotional intelligence (EI) is defined as an individual's capacity to accurately perceive, understand, reason about, and regulate emotions, and to apply that information to facilitate thought and achieve goals. Although EI plays an important role in mental health and success in academic, professional and social realms, the neurocircuitry underlying this capacity remains poorly characterized, and no study to date has yet examined the relationship between EI and intrinsic neural network function. Here, in a sample of 54 healthy individuals (28 women, 26 men), we apply independent components analysis (ICA) with dual regression to functional magnetic resonance imaging (fMRI) data acquired while subjects were resting in the scanner to investigate brain circuits (intrinsic resting state networks) whose activity is associated with greater self-reported (i.e. Trait) and objectively measured (i.e. Ability) EI. We show that higher Ability EI, but not Trait EI, is associated with stronger negatively correlated spontaneous fMRI signals between the basal ganglia/limbic network (BGN) and posterior default mode network (DMN), and regions involved in emotional processing and regulation. Importantly, these findings suggest that the functional connectivity within and between intrinsic networks associated with mentation, affective regulation, emotion processing, and reward are strongly related to ability EI.

This thesis gives an overview on how to estimate changes in functional brain networks using graph theoretical measures. It explains the assessment and definition of functional brain networks derived from fMRI data. More explicitly, this thesis provides examples and newly developed methods on the measurement and visualization of changes due to pathology, external electrical stimulation or ongoing internal thought processes. These changes can occur on long as well as on short time scales and might be a key to understanding brain pathologies and their development. Furthermore, this thesis describes new methods to investigate and visualize these changes on both time scales and provides a more complete picture of the brain as a dynamic and constantly changing network.

In this work we introduce the Wishart Distributed Matrices Multiple Order Classification (WISDoM) method. The WISDoM Classification method consists of a pipeline for single feature analysis, supervised learning,cross validation and classification for any problems whose elements can be tied to a symmetric positive-definite matrix representation. The general idea is for informations about properties of a certain system contained in a symmetric positive-definite matrix representation (i.e covariance and correlation matrices) to be extracted by modelling an estimated distribution for the expected classes of a given problem. The application to fMRI data classification and clustering processing follows naturally: the WISDoM classification method has been tested on the ADNI2 (Alzheimer's Disease Neuroimaging Initiative) database. The goal was to achieve good classification performances between Alzheimer's Disease diagnosed patients (AD) and Normal Control (NC) subjects, while retaining informations on which features were the most informative decision-wise. In our work, the informations about topological properties contained in ADNI2 functional correlation matrices are extracted by modelling an estimated Wishart distribution for the expected diagnostical groups AD and NC, and allowed a complete separation between the two groups.

Il lavoro che ho sviluppato presso l'unità di RM funzionale del Policlinico S.Orsola-Malpighi, DIBINEM, è incentrato sull'analisi dati di resting state - functional Magnetic Resonance Imaging (rs-fMRI) mediante l'utilizzo della graph theory, con lo scopo di valutare eventuali differenze in termini di connettività cerebrale funzionale tra un campione di pazienti affetti da Nocturnal Frontal Lobe Epilepsy (NFLE) ed uno di controlli sani. L'epilessia frontale notturna è una peculiare forma di epilessia caratterizzata da crisi che si verificano quasi esclusivamente durante il sonno notturno. Queste sono contraddistinte da comportamenti motori, prevalentemente distonici, spesso complessi, e talora a semiologia bizzarra. L'fMRI è una metodica di neuroimaging avanzata che permette di misurare indirettamente l'attività neuronale. Tutti i soggetti sono stati studiati in condizioni di resting-state, ossia di veglia rilassata. In particolare mi sono occupato di analizzare i dati fMRI con un approccio innovativo in campo clinico-neurologico, rappresentato dalla graph theory. I grafi sono definiti come strutture matematiche costituite da nodi e links, che trovano applicazione in molti campi di studio per la modellizzazione di strutture di diverso tipo. La costruzione di un grafo cerebrale per ogni partecipante allo studio ha rappresentato la parte centrale di questo lavoro. L'obiettivo è stato quello di definire le connessioni funzionali tra le diverse aree del cervello mediante l'utilizzo di un network. Il processo di modellizzazione ha permesso di valutare i grafi neurali mediante il calcolo di parametri topologici che ne caratterizzano struttura ed organizzazione. Le misure calcolate in questa analisi preliminare non hanno evidenziato differenze nelle proprietà globali tra i grafi dei pazienti e quelli dei controlli. Alterazioni locali sono state invece riscontrate nei pazienti, rispetto ai controlli, in aree della sostanza grigia profonda, del sistema limbico e delle regioni frontali, le quali rientrano tra quelle ipotizzate essere coinvolte nella fisiopatologia di questa peculiare forma di epilessia.

It is commonly accepted that accurate early diagnosis and monitoring of neurodegenerative conditions is essential for effective disease management and delivery of medication and treatment. This research develops automatic methods for detecting brain imaging preclinical biomarkers for Parkinson’s disease (PD) by considering the novel application of evolutionary algorithms. An additional novel element of this work is the use of evolutionary algorithms to both map and predict the functional connectivity in patients using rs-fMRI data. Specifically, Cartesian Genetic Programming was used to classify dynamic causal modelling data as well as timeseries data. The findings were validated using two other commonly used classification methods (Artificial Neural Networks and Support Vector Machines) and by employing k-fold cross-validation. Across dynamic causal modelling and timeseries analyses, findings revealed maximum accuracies of 75.21% for early stage (prodromal) PD patients in which patients reveal no motor symptoms versus healthy controls, 85.87% for PD patients versus prodromal PD patients, and 92.09% for PD patients versus healthy controls. Prodromal PD patients were classified from healthy controls with high accuracy – this is notable and represents the key finding since current methods of diagnosing prodromal PD have low reliability and low accuracy. Furthermore, Cartesian Genetic Programming provided comparable performance accuracy relative to Artificial Neural Networks and Support Vector Machines. Nevertheless, evolutionary algorithms enable us to decode the classifier in terms of understanding the data inputs that are used, more easily than in Artificial Neural Networks and Support Vector Machines. Hence, these findings underscore the relevance of both dynamic causal modelling analyses for classification and Cartesian Genetic Programming as a novel classification tool for brain imaging data with medical implications for disease diagnosis, particularly in early stages 5-20 years prior to motor symptoms.

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 91-93).
The main focus of the thesis is the resting state fMRI data analysis, with much emphasis on the anesthesia fMRI experiments. Under this central topic, three separate themes are developed: resting state fMRI data analysis overview, improved denoising techniques, and application to the Dexmedetomidine experiment data. In the first part, important and confusing resting state data analysis steps are explored indepth, focusing on how and why the pipeline is different from that of the task-based fMRI. In the second part, the Principal Component Analysis (PCA) based denoising technique is introduced and compared against the conventional fMRI denoising techniques. Finally, with the PCA denoising technique, the functional connectivity of the brainstem with the brain is assessed for the Dexmedetomidine-induced unconscious subjects. We found that the functional connectivity between the Locus Ceruleus (LC) in the brainstem and the Thalamus & Posterior Cingulate Cortex (PCC) is the neural correlates of the Dexmedetomidine-induced unconsciousness.
by Andrew Hyungsuk Song.
M. Eng.

Att undersöka områden i hjärnan som är aktiva utan att någon stimulans sker kan ge information om en individs standardnätverks basnivå. Denna basnivå kan användas för att identifiera avvikande spatiala mönster i hjärnan som associeras med sjukdomar och funktionsnedsättningar. Denna uppsats syftar till att undersöka hur skillnaderna ser ut för individspecifika nätverkskartor genom att jämföra tre olika anpassningar av Dual Regression, en frekventistisk och två Bayesianska modeller. Datamaterialet som analyseras i uppsatsen är från Cambridge-Buckner, en del av 1000 Functional Connectomes Project som innehåller fMRI-data. Från datamaterialet har även tillhörande förhandsskattade gruppvisa oberoende komponenter erhållits från 20 utvalda individer vilket sedan används i uppsatsen för att skatta individspecifika nätverkskartor i hjärnan för tre individer från studien. Det anpassas tre olika Dual Regressions-modeller: En frekventistisk modell med homoskedastisk varians, en Bayesiansk modell med heteroskedastisk varians med okorrelade feltermer samt en Bayesiansk modell med heteroskedastisk varians och korrelerade feltermer. För de två Bayesianska modellerna används icke-informativa priorfördelningar. Dessa olika modeller skiljer sig åt då de kan ta hänsyn till olika mängder av information genom att ha olika komplexa kovariansstrukturer. Det observeras att den frekventistiska modellen och den Bayesianska modellen med heteroskedastisk varians och okorrelerade feltermer skattar nätverk som är i stor utsträckning lika varandra. Den Bayesianska modellen med heteroskedastisk varians och korrelerade feltermer tenderar att skatta nätverk som är skild från de andra modellerna, där det ofta förekom skillnader i nätverkens former samt en del amplitudskillnader. I kovariansmatrisen för den Bayesianska modellen med heteroskedatisk varians och korrelerade feltermer observeras ett flertal höga korrelationer mellan feltermerna vilket indikerar på att det bör tas hänsyn till korrelerade feltermer. Det diskuteras även om problem som förekommer hos respektive tillvägagångssätt för att skatta modellen, där frekventistiska tillvägagångssättet inte tar hänsyn till all information i data men är enkel att anpassa. Den Bayesianska modellen med heteroskedastisk varians och okorrelerade feltermer ger liknande resultat som det frekventistiska tillvägagångssättet. Den Bayesianska modellen med heteroskedastisk varians och korrelerade feltermer ger resultat som anpassar data bättre än de andra två modellerna men är mer komplex att beräkna.
Examining regions in the brain that are active without any stimuli gives information about an individual's default brain networks. These default mode networks can be analyzed to identify deviating spatial patterns in the brain that are associated with diseases and disabilities. This thesis aims to analyze the difference in how frequentist and Bayesian Dual Regression estimates subject specific spatial-maps. We received pre-estimated groupwise independent components from 20 individuals based off of fMRI-data from the Cambridge-Buckner dataset which is part of the 1000 Functional Connectomes Project. These are later used to create subject specific spatial-maps for 3 individuals in the study. In this thesis 3 different types of Dual Regression models will be fitted: A frequentist Dual Regression, A Bayesian model with heteroscedastic variance and uncorrelated error terms and a Bayesian model with heteroscedastic variance and correlated error terms. Non-informative prior distributions are used for both Bayesian models. As these 3 models can account for varying amounts of information in the data due to varying complexity of the covariance structure some difference were observed in the subject specific maps. The frequentist Dual Regression and the Bayesian model with heteroscedastic variance and uncorrelated error terms often showed similar results, however the resulting networks from the Bayesian model with heteroscedastic variance and correlated error terms often differed from the other two models. The difference was observed both in network shapes and in activation amplitude. The covariance matrix for the Bayesian model with heteroscedastic variance and correlated error terms contained a number of high correlations between the error terms, indicating that correlation among error terms should be taken into account. Some arguments are made for respective way of fitting the model as each model has its unique advantage and disadvantage; where the frequentist model does not take into account all information from the data it is easy to fit. The Bayesian model with heteroscedastic variance and uncorrelated error terms is also relatively easy to fit and provides similar results to the frequentist model. The Bayesian model with heteroscedastic variance and correlated error terms however does account for more information and yields better results but is more computationally expensive.

Psychology
Ph.D.
Neurobiological theories of bipolar spectrum disorder (BSD) propose that the emotional dysregulation characteristic of BSD stems from disrupted prefrontal control over subcortical limbic structures (Strakowski et al., 2012; Depue & Iacono, 1989). However, existing neuroimaging research on functional connectivity between frontal and limbic brain regions remains inconclusive, and is unable to adequately characterize global functional network dynamics. Graph theoretical analysis provides a framework for understanding the local and global connections of the brain and comparing these connections between groups (Sporns et al., 2004). The purpose of this study was to investigate resting state functional connectivity in individuals at low and high risk for BSD based on moderate versus high reward sensitivity, both with and without a BSD diagnosis, using graph theoretical network analysis. Results demonstrated decreased connectivity in a cognitive control region (dorsolateral prefrontal cortex), but increased connectivity of a brain region involved in the detection and processing of reward (bilateral orbitofrontal cortex), among participants at high risk for BSD. Participants with BSD showed increased inter-module connectivity of the dorsal anterior cingulate cortex (ACC). Reward sensitivity was associated with decreased global and local efficiency, and interacted with BSD risk group status to predict inter-module connectivity. Findings are discussed in relation to neurobiological theories of BSD.
Temple University--Theses

Brain activity, on every scale, spontaneously fluctuates, thereby exhibiting complex, dynamic interactions that manifest rich synchronization patterns. The past ten years have been dominated by studies intended to further our understanding of the mecha-nisms behind the dynamic interactions within the brain through the basis of its structural and functional connectivity structures. Moreover, there is a tremendous effort to unveil the role that these interactions play in psychiatric disorders. This thesis addresses these questions from novel perspectives. The first pillar of this thesis is the time-varying na-ture of the dynamic interactions between brain regions. The second pillar is the role that FC dynamics play in clinical populations. The third pillar uncovers the connectivity structure that links the observed anatomical and functional connectivity patterns through computational modeling. The final pillar of the thesis proposes a mechanistic explana-tion for brain disorders.
L'activitat del cervell fluctua espontàniament a diferents escales i per tant exhibeix in-teraccions dinàmiques i complexes que manifesten patrons de sincronització rics. Du-rant els darrers deu anys han abundat els estudis orientats a comprendre els mecanismes que hi ha darrere les interaccions cerebrals basant-se en les seves estructures funcionals i estructurals. A més, existeix un esforç ingent per desvetllar el paper que aquestes in-teraccions juguen en els trastorns psiquiàtrics. Aquesta tesi aborda les qüestions esmen-tades des de noves perspectives. El primer pilar d'aquesta tesi és la naturalesa variable en el temps de la interacció dinàmica entre diferents regions del cervell. El segon pilar és el paper que aquesta dinàmica de connectivitat funcional juga en diferents poblacions clíniques. El tercer pilar es centra en l'ús de models computacionals per determinar l'es-tructura de connectivitat que relaciona els patrons de connectivitat funcional i anatòmics observats. El quart pilar de la tesi proposa una explicació del mecanisme dels trastorns cerebrals.

Le concept de réseaux - l'idée que deux ou plusieurs nœuds distribués peuvent interagir pour produire un phénomène - a longtemps été utilisé dans la recherche et le traitement de l'épilepsie. En effet, même dans les épilepsies considérées comme «focales», les perspectives cliniques et théoriques soulignent l'importance des questions suivantes, à savoir : 1) comment pouvons-nous localiser, partitionner et caractériser les réseaux impliqués dans l'épilepsie et 2) dans quelle mesure ces réseaux interagissent avec le réseau cérébral à grande échelle? Récemment, la notion de réseaux pathologiques dans l'épilepsie a été renforcée par l’apport de la neuroimagerie, avec en particulier le paradigme 'd'état de repos' qui reconnaît l'information inhérente à l'activité spontanée du cerveau, en plus de celle liée aux événements transitoires exogènes et paroxystiques endogènes.En tirant partie de ces techniques, ce travail fournit de nouveaux concepts sur 1) les relations multimodales et le couplage entre l’hémodynamique et la connectivité fonctionnelle électro physiologique aussi bien dans les cortex épileptiques que non affectés, 2) les processus pathologiques affectant l’homéostasie ionique et les dysfonctionnements neuronaux dans les réseaux épileptiques, 3) les interactions au niveau de groupe entre les réseaux épileptiques et les propriétés topologiques du cerveau, et 4) comment les interactions entre la pathologie épileptique et des propriétés uniques du réseau cérébral peuvent contribuer à produire des effets cliniques au niveau du réseau
The concept of networks – the idea that two or more distributed nodes may interact to produce a phenomenon – has long been of utility in research into and the treatment of epilepsy. Indeed, even in epilepsies deemed ‘focal’, clinical and theoretical insights underline the importance of the questions 1) how can we localize, partition and characterize networks involved in epilepsy, and 2) to what extent do such networks interact with the brain network at large? Recently, the notion of pathological network effects in epilepsy has been reinvigorated with input from neuroimaging, especially a ‘resting-state’ paradigm that recognizes the systemic information inherent in the ongoing activity of the brain in addition to that provided when it is disturbed by transient exogenous events and endogenous paroxysms. By leveraging these techniques, this work provides novel insights into 1) the multimodal relationships and coupling between haemodynamic- and electrophysiologically-defined functional connectivity, both in epileptic and unaffected cortices 2) pathological processes affecting ionic homeostasis and neural dysfunction in epileptic networks 3) group-level interactions between epileptic networks and brain network topological properties and 4) how interactions between epileptic pathology and unique brain network properties may contribute to produce to clinical effects at the network level. This work opens up new perspectives on the understanding of network effects in epilepsy, sources of variance in their analysis, the biological processes occurring in parallel and contributing to them and their role in an individualized understanding of pathology

Resting state functional magnetic resonance imaging (fMRI) is currently at the forefront of research on cognition and the brain’s large-scale organization. Patterns of hemodynamic activity that it records have been strongly linked to certain behaviors and cognitive pathologies. These signals are widely assumed to reflect local neuronal activity but our understanding of the exact relationship between them remains incomplete. Researchers often address this using multimodal approaches, pairing fMRI signals with known measures of neuronal activity such as electroencephalography (EEG). It has long been thought that infraslow (< 0.1 Hz) fMRI signals, which have become so important to the study of brain function, might have a direct electrophysiological counterpart. If true, EEG could be positioned as a low-cost alternative to fMRI when fMRI is impractical and therefore could also become much more influential in the study of functional brain networks. Previous works have produced indirect support for the fMRI-EEG relationship, but until recently the hypothesized link between them had not been tested in resting humans. The objective of this study was to investigate and characterize their relationship by simultaneously recording infraslow fMRI and EEG signals in resting human adults. We present evidence strongly supporting their link by demonstrating significant stationary and dynamic correlations between the two signal types. Moreover, functional brain networks appear to be a fundamental unit of this coupling. We conclude that infraslow electrophysiology is likely playing an important role in the dynamic configuration of the resting state brain networks that are well-known to fMRI research. Our results provide new insights into the neuronal underpinnings of hemodynamic activity and a foundational point on which the use of infraslow EEG in functional connectivity studies can be based.

Early life stress (ELS) affects the developing brain and impacts capacity for self-regulation and risk for psychopathology. The high spatial resolution of functional magnetic resonance imaging (fMRI) confers an advantage for studying specific neural regions posited to link ELS with subsequent functioning. The first chapter in this dissertation reviews the literature establishing the feasibility and utility of fMRI research with infants and young children. This chapter examines methodological issues and outlines the potential for this technique to make unique contributions to understanding how ELS influences brain development. The next two chapters present results from a study that employed a functional activation paradigm and resting state functional connectivity MRI (rs-fcMRI) to examine associations between a common source of ELS, non-physical interparental conflict, and neural functioning during infancy. The functional activation paradigm focused on emotional tone of voice as a stimulus relevant to interparental conflict, which is likely salient to infants. Higher levels of interparental conflict (as reported by mothers) were associated with infants (6 to 12 months of age) showing greater reactivity to very angry versus neutral tone of voice in neural regions associated with processing and regulation of stress and emotion (hypothalamus and rostral anterior cingulate cortex). The rs-fcMRI analysis examined coordinated neural functioning in the absence of stimuli, focusing on the amygdala as a key region for understanding the impact of ELS and the posterior cingulate cortex as part of a group of regions that show higher levels of activity in the absence of stimuli (the default network). The results replicate previous work characterizing the default network in infants and provide novel evidence for the functional connectivity of the amydgala and amygdala subregions during infancy. Interparental conflict was associated with variation in the connectivity of both regions. Thus levels of interparental conflict were associated with neural reactivity to a stressor-relevant stimulus and with patterns of coordinated neural functioning in the absence of such stimuli. These results provide support for the utility of using fMRI with infants to examine early emerging associations between common forms of ELS and brain functioning. This dissertation includes previously published and co-authored material.
2016-10-17

Social Anxiety Disorder (SAD) is characterized by excessive concern or fear of negative evaluation in one or more social situations and ranks as one of the most common psychiatric disorders. SAD has also been characterized by significant deficits in social motivation and a lack of reactivity to pleasurable stimuli (i.e., positive affect; [PA]), particularly within social contexts. Recent neuroimaging work has shifted towards examining positively-valenced motivational systems in SAD focused on reward responses to social and nonsocial stimuli. These studies have revealed aberrant reward processing during social reward tasks in individuals with SAD. However, not all individuals with SAD exhibit reward circuitry dysfunction. Therefore, the current study aimed to examine if functional patterns of connectivity in the brain underlie heterogeneity in PA differences in individuals with SAD. Results revealed several functional connectivity strength differences between SAD and control groups within reward regions. Additionally, associations between regions of interest (ROIs)-couplings (i.e., OFC and insula, OFC and subgenual cingulate, insula and cingulate, and cingulate and subgenual cingulate) and diminished PA were present in individuals with SAD, but not controls. Lastly, results demonstrated that individuals with SAD had higher variability in their reward connectivity strength presentations and reports of PA as compared to controls. These results hold significance for the development of interventions for SAD that focus on the enhancement of PA to bolster social reward responsivity.
M.S.
Social Anxiety Disorder (SAD) is a common disorder where individuals experience persistent excessive fear of one or more social situations. Individuals with SAD also tend to show lower social motivation and a lack of reactivity to pleasurable activities/events (referred to broadly as positive affect; [PA]), particularly within social situations. Current work has focused on areas within the brain that are responsible for reward responses, and have indicated that individuals with SAD show different types of reward processing during social reward situations. However, not all individuals with SAD show these same patterns. Therefore, the current study aimed to examine if connections between reward regions in the brain underlie differences in PA differences in individuals with SAD. Results showed several differences between SAD and control groups within reward regions of the brain. Additionally, specific associations between brain regions of interest and low PA were present in individuals with SAD, but not controls. Lastly, results demonstrated that individuals with SAD had higher variability in their connections between reward regions and reports of PA as compared to controls. These results can help inform the development of treatments for SAD that focus on the improving PA in an attempt to increase responsiveness to social rewards.

Human infants have been shown to have an innate capacity to acquire their mother tongue. In recent decades, the advent of the functional magnetic resonance imaging (fMRI) technique has made it feasible to explore the neural basis underlying language acquisition and processing in children, even in newborn infants (for reviews, see Kuhl & Rivera-Gaxiola, 2008; Kuhl, 2010) . Spontaneous low-frequency (< 0.1 Hz) fluctuations (LFFs) in the resting brain have been shown to be physiologically meaningful in the seminal study (Biswal et al., 1995) . Compared to task-based fMRI, resting-state fMRI (rs-fMRI) has some unique advantages in neuroimaging research, especially in obtaining data from pediatric and clinical populations. Moreover, it enables us to characterize the functional organization of the brain in a systematic manner in the absence of explicit tasks. Among brain systems, the language network has been well investigated by analyzing LFFs in the resting brain. This thesis attempts to investigate the functional connectivity within the language network in typically developing preschool children and the covariation of this connectivity with children’s language development by using the rs-fMRI technique. The first study (see Chapter 2.1; Xiao et al., 2016a) revealed connectivity differences in language-related regions between 5-year-olds and adults, and demonstrated distinct correlation patterns between functional connections within the language network and sentence comprehension performance in children. The results showed a left fronto-temporal connection for processing syntactically more complex sentences, suggesting that this connection is already in place at age 5 when it is needed for complex sentence comprehension, even though the whole functional network is still immature. In the second study (see Chapter 2.2; Xiao et al., 2016b), sentence comprehension performance and rs-fMRI data were obtained from a cohort of children at age 5 and a one-year follow-up. This study examined the changes in functional connectivity in the developing brain and their relation to the development of language abilities. The findings showed that the development of intrinsic functional connectivity in preschool children over the course of one year is clearly observable and individual differences in this development are related to the advancement in sentence comprehension ability with age. In summary, the present thesis provides new insights into the relationship between intrinsic functional connectivity in the brain and language processing, as well as between the changes in intrinsic functional connectivity and concurrent language development in preschool children. Moreover, it allows for a better understanding of the neural mechanisms underlying language processing and the advancement of language abilities in the developing brain.

Convergent findings have elucidated the regions involved in semantic cognition. The anterior temporal lobes (ATL) act as a hub for multimodal semantic processing alongside modality-specific ‘spoke’ regions. In addition, areas of inferior parietal, posterior temporal and frontal cortex are necessary for semantic cognition. However, many questions remain. Little is known about the timing of the ATL or how distributed regions interact in order to perform semantic processing. In order to gain knowledge of the precise spatial and temporal dynamics of the ATL and semantic cognition network, a series of studies was performed. Chapter 3 investigated the time at which the ATL is necessary for a semantic judgement using chronometric TMS. The ATL was found to be necessary for semantic cognition from 400ms post-stimuli presentation. This is known to be a critical time for semantic processing. Processing of items presented in different modalities converges around this time. This supports the role of the ATL in multimodal semantic cognition. Chapter 4 used offline repetitive TMS to investigate the role of ATL subregions and posterior temporal cortex in semantic and phonological processing. However, no significant TMS effects were demonstrated. Chapter 5 employed dual echo fMRI to assess how different types of semantic relationships are instantiated within the brain. Association (spatially and temporally co-occurring concepts) and conceptual similarity (concepts sharing features) were shown to rely on the same cortical regions. This provides evidence against theories suggesting separate representational hubs for these different relationship types. Instead it supports the reliance of both relationship types on the ATL hub. These two kinds of relationship may be more similar than previously thought, with the hub-and-spoke model able to explain both. The semantic network identified here included ATL, posterior temporal, frontal and ventral parietal cortex. This network of semantic regions was shown to be interconnected in Chapter 6 during a semantic task (using a psychophysiological interaction analysis) and during rest (using a seed-based functional connectivity analysis). Differential connectivity was identified between the ventral ATL (to multimodal semantic regions) and the aSTG (to language-related regions). The semantic network overlapped with the default mode network (DMN) and involved regions previously found to constitute the frontoparietal network (FPN).Emergent questions related to the overlap between previously identified network and the semantic network were addressed with preliminary independent component analyses in Chapter 7. This showed the dynamic connectivity of the ATL in task and rest. The semantic network was found to be distinct from but overlapping with the DMN and FPN. The role of this network in semantic cognition was confirmed, whereas the DMN was not found to relate to semantic processing. The anterior DMN component appeared semantic based on activity alone, suggesting prior results relating the DMN to semantic cognition fail to take the dynamic connectivity of the regions in to account. The left FPN overlapped with semantic control regions but appeared to relate to more general control processes. When assessed with dual echo fMRI, the ATL appears to be highly connected in a dynamic fashion and may be an important region currently under-represented within studies of the connectome. Overall, these studies add to the hub-and-spoke model of semantic cognition, elucidating the types of relationship involved, how regions interact and the precise temporal and spatial dynamics of these areas.

Introduction: The aim of the project was to investigate the effects of antidepressants on brain networks whilst at rest. My hypothesis was that antidepressants work by reversing persistent activity in the brain’s default mode network (DMN). The DMN is implicated in self-reflection and rumination in MDD. The methodologies and results of studies of resting state networks in MDD and the effects of antidepressants are reviewed in the thesis. Increasing evidence implicates glutamate in the action of antidepressant drugs. Whether there are illness related changes in glutamate function is unresolved, largely because of the lack of techniques for assessing it. Ketamine and other NMDA antagonists have improved MDD symptoms within 24 hours though the effects are short lasting. The molecular neural networks involved in ketamine’s putative antidepressant effects are unclear. The thesis reviews the evidence. Much evidence implicates ACC as a site of action of antidepressant effects but whether this is through its regulation of the DMN or other networks is not known. This thesis compares the effect of ketamine and citalopram on ACC-related systems. Method: The thesis combines two systematic reviews of the effects of MDD and antidepressant drugs on i) resting state networks (53 studies) and ii) glutamate neurotransmission (45 studies of clinical efficacy of ketamine). There are two experimental chapters. The first describes investigation into two rapid acting antidepressant drugs acting via glutamate mechanisms. 54 unmedicated cMDD were scanned across two centres on 3T MRI scanners while being infused with placebo (0.5% saline), 0.5mg/kg ketamine or 100mg AZD6765 over 1 hour. fMRI resting state data between drug treatments was compared for the final 25 minutes of the drug infusion and for a 25 minute resting state scan a day later. The second experimental chapter examines whether these effects were shared by citalopram, a standard antidepressant. 67 unmedicated cMDD, rMDD and HC were administered citalopram 7.5mg i.v. and scanned on a 1.5T MRI scanner. In a second study 63 cMDD and HC were administered i.v. citalopram 7.5mg or placebo (0.5% saline). fMRI resting state data for the final 12 ½ minutes following drug infusion was compared. Independent Component Analysis was performed using the Group ICA for fMRI toolbox. The resting component with the highest spatial correlation to the ACC was used. Brain maps of the intensity of the selected component were constructed for each individual. Group averages were calculated and compared using SPM. Regional analysis was performed using Marseille Boite a Regions d'interet. Results: On day 1 AZD6765 significantly increased mean intensity of ACC resting component in the right insula, right IPL and left cingulate gyrus greater than ketamine or placebo. Ketamine increased mean intensity of ACC resting component greater than placebo in the right lentiform nucleus and left mFG. Significantly decreased mean intensity of ACC resting component in the left insula in the AZD6765 group compared to placebo was noted. On day 2 AZD6765 increased mean intensity of ACC resting component greater than ketamine and placebo in the left and right lentiform nuclei. AZD6765 reduced mean intensity of the ACC resting component in the left and right MFG. The first citalopram study revealed reduced mean intensity of ACC resting component in cMDD compared to rMDD and HC in PCC. rMDD had reduced mean intensity of ACC resting component in the precuneus compared to HC. In the second study, citalopram had no effect in HC but normalised precuneus activity in cMDD producing a significant drug x group interaction. Conclusions: The acute antidepressant effects of citalopram are modulated by changes in the bilateral precuneus. The precuneus is central to connectivity with other regions in MDD. It has a prominent role in the DMN and is linked to rumination. The mechanism of the antidepressant effects of AZD6765 is different from those of ketamine and citalopram. The insula, IPL, MFG, cingulate gyrus and lentiform nuclei are all regions implicated in MDD suggesting antidepressant effects. The rapid antidepressant effects of AZD6765 are possibly due to a resetting of the interface between DMN and salience networks.

Abstract Neuropsychiatric illnesses usually become overtly manifest in adolescence and early adulthood. A critical long-term aim is to be able to prevent the development of such illnesses, which requires instruments to identify subjects at high risk of illness and to offer them effective interventions. There is an indisputable need for more sophisticated methods to enable more precise detection of adolescents and young adults who are at high risk of developing psychosis. Abnormal function in brain networks has been reported in people with schizophrenia and other psychotic disorders. Similar abnormalities have been found also in people at risk for developing psychosis, but it is not known whether this applies also to spontaneous resting state activity in young people with a familial risk for psychosis. We conducted resting-state functional MRI (R-fMRI) in 72 (29 male) young adults with a history of psychosis in one or both parents (FR) but without psychosis themselves, and 72 (29 male) similarly healthy control subjects without familial risk for psychosis. Both groups in the Oulu Brain and Mind study were drawn from the Northern Finland Birth Cohort 1986. All volunteers were 20–25 years old. Parental psychosis was established using the Care Register for Health Care. R-fMRI data was pre-processed using independent component analysis (ICA). A dual regression technique was used to detect between-group differences with p < 0.05 threshold corrected for multiple comparisons at voxel level. FR subjects demonstrated significantly decreased activity compared to control subjects in the default mode network and in the central executive network and increased activity in the cerebellum. The findings clarify previously controversial literature on the subject. The finding suggests that abnormal activity in these brain networks in rest may be associated with increased vulnerability to psychosis. The findings maybe helpful in developing more precise methods for detecting young people at highest risk for developing psychosis
Tiivistelmä Psykoottisiin häiriöihin sairastutaan yleensä nuoruudessa tai varhaisaikuisuudessa. Psykoositutkimuksen tavoitteena on löytää uusia menetelmiä, joiden avulla kyettäisiin tunnistamaan suurimmassa psykoosiriskissä olevat nuoret, jotta heille voitaisiin tarjota sairautta ennaltaehkäiseviä hoitokeinoja. Skitsofreniaan ja muihin psykoottisiin häiriöihin sairastuneilla on havaittu aivotoiminnan poikkeavuuksia. Samankaltaisia aivotoiminnan poikkeavuuksia on havaittu myös nuorilla, jotka ovat vaarassa sairastua psykoosiin. Toistaiseksi on ollut epäselvää, onko psykoosiin sairastuneiden henkilöiden lapsilla aivohermoverkkojen toiminnan poikkeavuuksia lepotilassa. Suoritimme aivojen lepotilan MRI-tutkimuksen (R-fMRI) 72:lle (29 miestä) nuorelle aikuiselle, joiden jompikumpi vanhempi oli sairastunut psykoosin sekä 72:lle (29 miestä) nuorelle aikuiselle, joiden vanhemmat eivät olleet sairastaneet psykoosia. Molemmat tutkimusryhmät tässä Oulu Brain and Mind -tutkimuksessa olivat Pohjois-Suomen 1986 syntymäkohortin jäseniä. Tutkittavat olivat 20–25 vuoden iässä. Lepotilan toiminnallinen magneettikuvaus suoritettiin 1.5 Teslan Siemensin magneettikuvantamislaitteella. Tutkimuskohteiksi valittiin lepotilan toiminnallinen aivohermoverkko, toiminnan ohjauksesta vastaava aivohermoverkko ja pikkuaivot. Kuvantamisdataan sovellettiin itsenäisten komponenttien analyysia aivohermoverkkojen määrittämistä varten. Ryhmien välisen eron havaitsemiseen käytettiin ei-parametristä permutaatiotestiä, joka kynnystettiin tilastollisesti merkitsevään tasoon (p < 0.05). Lepotilan oletushermoverkossa ja toiminnanohjauksesta vastaavassa aivohermoverkoissa havaittiin vähäisempää aktiivisuutta ja pikkuaivoissa kohonnutta aktiivisuutta perinnöllisessä psykoosiriskissä olevilla nuorilla aikuisilla verrattuna verrokkeihin. Tutkimustulokset selkeyttivät aiempaa ristiriitaista kirjallisuutta tutkimusaiheesta. Tutkimuksessa havaittujen aivoalueiden poikkeava toiminta lepotilassa voi liittyä kohonneeseen psykoosin puhkeamisriskiin. Tutkimuslöydösten avulla voidaan todennäköisesti edesauttaa parempien kuvantamismenetelmien kehittämistä suurimmassa psykoosiriskissä olevien nuorten tunnistamiseen

Older bilingual adults typically perform better than monolinguals in tasks of executive control, and are diagnosed later with dementia. Studies have also shown structural and functional brain differences between bilinguals and monolinguals. However, it remains poorly understood how language history influences the functional organization of the aging brain. The current study investigated; 1) differences in resting-state functional connectivity between monolinguals and bilinguals in the Default Mode Network (DMN), Frontoparietal Network (FPN), Executive Control Network (ECN), Language Network (LANG), and a network consisting of structures associated with tasks of executive control coined the Bilingual Control Network (BCN); 2) the relationship of cognitive performance with functional connectivity of the BCN; and 3) whether proficiency, age of second language acquisition, degree of second language exposure, and frequency of language use predicts the network’s functional connectivity. Healthy older bilinguals (N=10) were matched pairwise for age, sex and education to healthy older monolinguals (N=10). All participants completed a battery of cognitive tests, a language history questionnaire, and a 6-minute functional scan during rest. Results showed that groups did not differ in cognitive performance, or in the functional connectivity of the FPN, ECN, LANG, or BCN. However, monolinguals had significantly stronger functional connectivity in the DMN compared to bilinguals. Later age of second language acquisition and lower proficiency were also associated with greater DMN functional connectivity. None of these variables predicted BCN’s functional connectivity. However, bilinguals showed stronger functional connectivity with other structures outside of the canonical networks compared to monolinguals. Finally, vocabulary scores, local switch cost accuracy and reaction time were negatively correlated with BCN’s functional connectivity. Overall, these findings illustrate differences in functional brain organization associated with language experience in the DMN, while challenging the “bilingual advantage” hypothesis. The results also suggest a possible neural mechanism by which bilingualism might mediate cognitive reserve.

Abstract Resting-state (RS) measurements during functional magnetic resonance imaging (fMRI) have become an established approach for studying spontaneous brain activity. RS-fMRI results are often obtained using explorative approaches like spatial independent component analysis (sICA). These approaches and their software implementations are rarely evaluated extensively or specifically concerning RS-fMRI. Trust is placed in the software that they will work according to the published method descriptions. Many methods and parameters are used despite the lack of test data, and the validity of the underlying models remains an open question. A substantially greater number of evaluations would be needed to ensure the quality of exploratory RS-fMRI analyses. This thesis investigates the applicability of sICA methodology and software in the RS-fMRI context. The experiences were used to formulate general guidelines to facilitate future method evaluations. Additionally, a novel multiple comparison correction (MCC) method, Maxmad, was devised for adjusting evaluation results statistically. With regard to software considerations, the source code of FSL Melodic, popular sICA software, was analyzed against its published method descriptions. Unreported and unevaluated details were found, which implies that one should not automatically assume a correspondence between the literature and the software implementations. The method implementations should rather be subjected to independent reviews. An experimental contribution of this thesis is that the credibility of the emerging sliding window sICAs has been improved by the validation of sICA related preprocessing procedures. In addition to that, the estimation accuracy regarding the results in existing RS-fMRI sICA literature was also shown not to suffer even though repeatability tools like Icasso have not been used in their computation. Furthermore, the evidence against conventional sICA model suggests the consideration of different approaches to analysis of RS-fMRI. The guidelines developed for facilitation of evaluations include adoption of 1) open software development (improved error detection), 2) modular software designs (easier evaluations), 3) data specific evaluations (increased validity), and 4) extensive coverage of parameter space (improved credibility). The proposed Maxmad MCC addresses a statistical problem arising from broad evaluations. Large scale cooperation efforts are proposed concerning evaluations in order to improve the credibility of exploratory RS-fMRI methods
Tiivistelmä Aivoista toiminnallisella magneettikuvantamisella (engl. functional magnetic resonance imaging, fMRI) lepotilassa tehdyt mittaukset ovat saaneet vakiintuneen aseman spontaanin aivotoiminnan tutkimuksessa. Lepotilan fMRI:n tulokset saadaan usein käyttämällä exploratiivisia menetelmiä, kuten spatiaalista itsenäisten komponenttien analyysia (engl. spatial independent component analysis, sICA). Näitä menetelmiä ja niiden ohjelmistototeutuksia evaluoidaan harvoin kattavasti tai erityisesti lepotilan fMRI:n kannalta. Ohjelmistojen luotetaan toimivan menetelmäkuvausten mukaisesti. Monia menetelmiä ja parametreja käytetään testidatan puuttumisesta huolimatta, ja myös menetelmien taustalla olevien mallien pätevyys on edelleen epäselvä asia. Eksploratiivisten lepotilan fMRI-datan analyysien laadun varmistamiseksi tarvittaisiin huomattavasti nykyistä suurempi määrä evaluaatioita. Tämä väitöskirja tutki sICA-menetelmien ja -ohjelmistojen soveltuvuutta lepotilan fMRI-tutkimuksiin. Kokemuksien perusteella luotiin yleisiä ohjenuoria helpottamaan tulevaisuuden menetelmäevaluaatioita. Lisäksi väitöskirjassa kehitettiin uusi monivertailukorjausmenetelmä, Maxmad, evaluaatiotulosten tilastolliseen korjaukseen. Tunnetun sICA-ohjelmiston, FSL Melodicin, lähdekoodi analysoitiin suhteessa julkaistuihin menetelmäkuvauksiin. Analyysissa ilmeni aiemmin raportoimattomia ja evaluoimattomia menetelmäyksityiskohtia, mikä tarkoittaa, ettei kirjallisuudessa olevien menetelmäkuvausten ja niiden ohjelmistototeutusten välille pitäisi automaattisesti olettaa vastaavuutta. Menetelmätoteutukset pitäisi katselmoida riippumattomasti. Väitöskirjan kokeellisena panoksena parannettiin liukuvassa ikkunassa suoritettavan sICA:n uskottavuutta varmistamalla sICA:n esikäsittelyjen oikeellisuus. Lisäksi väitöskirjassa näytettiin, että aiempien sICA-tulosten tarkkuus ei ole kärsinyt, vaikka niiden estimoinnissa ei ole käytetty toistettavuustyökaluja, kuten Icasso-ohjelmistoa. Väitöskirjan tulokset kyseenalaistavat myös perinteisen sICA-mallin, minkä vuoksi tulisi harkita siitä poikkeavia lähtökohtia lepotilan fMRI-datan analyysiin. Evaluaatioiden helpottamiseksi kehitetyt ohjeet sisältävät seuraavat periaatteet: 1) avoin ohjelmistokehitys (parantunut virheiden havaitseminen), 2) modulaarinen ohjelmistosuunnittelu (nykyistä helpommin toteutettavat evaluaatiot), 3) datatyyppikohtaiset evaluaatiot (parantunut validiteetti) ja 4) parametriavaruuden laaja kattavuus evaluaatioissa (parantunut uskottavuus). Ehdotettu Maxmad-monivertailukorjaus tarjoaa ratkaisuvaihtoehdon laajojen evaluaatioiden tilastollisiin haasteisiin. Jotta lepotilan fMRI:ssä käytettävien exploratiivisten menetelmien uskottavuus paranisi, väitöskirjassa ehdotetaan laaja-alaista yhteistyötä menetelmien evaluoimiseksi

Human infants have been shown to have an innate capacity to acquire their mother tongue. In recent decades, the advent of the functional magnetic resonance imaging (fMRI) technique has made it feasible to explore the neural basis underlying language acquisition and processing in children, even in newborn infants (for reviews, see Kuhl & Rivera-Gaxiola, 2008; Kuhl, 2010) . Spontaneous low-frequency (< 0.1 Hz) fluctuations (LFFs) in the resting brain have been shown to be physiologically meaningful in the seminal study (Biswal et al., 1995) . Compared to task-based fMRI, resting-state fMRI (rs-fMRI) has some unique advantages in neuroimaging research, especially in obtaining data from pediatric and clinical populations. Moreover, it enables us to characterize the functional organization of the brain in a systematic manner in the absence of explicit tasks. Among brain systems, the language network has been well investigated by analyzing LFFs in the resting brain. This thesis attempts to investigate the functional connectivity within the language network in typically developing preschool children and the covariation of this connectivity with children’s language development by using the rs-fMRI technique. The first study (see Chapter 2.1; Xiao et al., 2016a) revealed connectivity differences in language-related regions between 5-year-olds and adults, and demonstrated distinct correlation patterns between functional connections within the language network and sentence comprehension performance in children. The results showed a left fronto-temporal connection for processing syntactically more complex sentences, suggesting that this connection is already in place at age 5 when it is needed for complex sentence comprehension, even though the whole functional network is still immature. In the second study (see Chapter 2.2; Xiao et al., 2016b), sentence comprehension performance and rs-fMRI data were obtained from a cohort of children at age 5 and a one-year follow-up. This study examined the changes in functional connectivity in the developing brain and their relation to the development of language abilities. The findings showed that the development of intrinsic functional connectivity in preschool children over the course of one year is clearly observable and individual differences in this development are related to the advancement in sentence comprehension ability with age. In summary, the present thesis provides new insights into the relationship between intrinsic functional connectivity in the brain and language processing, as well as between the changes in intrinsic functional connectivity and concurrent language development in preschool children. Moreover, it allows for a better understanding of the neural mechanisms underlying language processing and the advancement of language abilities in the developing brain.

The field of neuroimaging has truly become data rich, and as such, novel analytical methods capable of gleaning meaningful information from large stores of imaging data are in high demand. Those methods that might also be applicable on the level of individual subjects, and thus potentially useful clinically, are of special interest. In this dissertation we introduce just such a method, called nonlinear functional mapping (NFM), and demonstrate its application in the analysis of resting state fMRI (functional Magnetic Resonance Imaging) from a 242-subject subset of the IMAGEN project, a European study of risk-taking behavior in adolescents that includes longitudinal phenotypic, behavioral, genetic, and neuroimaging data. Functional mapping employs a computational technique inspired by biological evolution to discover and mathematically characterize interactions among ROI (regions of interest), without making linear or univariate assumptions. Statistics of the resulting interaction relationships comport with recent independent work, constituting a preliminary cross-validation. Furthermore, nonlinear terms are ubiquitous in the models generated by NFM, suggesting that some of the interactions characterized here are not discoverable by standard linear methods of analysis. One such nonlinear interaction is discussed in the context of a direct comparison with a procedure involving pairwise correlation, designed to be an analogous linear version of functional mapping. Another such interaction suggests a novel distinction in brain function between drinking and non-drinking adolescents: a tighter coupling of ROI associated with emotion, reward, and interceptive processes such as thirst, among drinkers. Finally, we outline many improvements and extensions of the methodology to reduce computational expense, complement other analytical tools like graph-theoretic analysis, and possibly allow for voxel level functional mapping to eliminate the necessity of ROI selection.

El balance del cerebro se altera a nivel estructural y funcional por el consumo de alcohol y puede causar trastornos por consumo de alcohol (TCA). El objetivo de esta Tesis Doctoral fue investigar los efectos del consumo crónico y excesivo de alcohol en el cerebro desde una perspectiva funcional y estructural, mediante análisis de imágenes multimodales de resonancia magnética (RM). Realizamos tres estudios con objetivos específicos: i) Para entender cómo las neuroadaptaciones desencadenadas por el consumo de alcohol se ven reflejadas en la conectividad cerebral funcional entre redes cerebrales, así como en la actividad cerebral, realizamos estudios en ratas msP en condiciones de control y tras un mes con acceso a alcohol. Para cada sujeto se obtuvieron las señales específicas de sus redes cerebrales tras aplicar análisis probabilístico de componentes independientes y regresión espacial a las imágenes funcionales de RM en estado de reposo (RMf-er). Después, estimamos la conectividad cerebral en estado de reposo mediante correlación parcial regularizada. Para una lectura de la actividad neuronal realizamos un experimento con imágenes de RM realzadas con manganeso. En la condición de alcohol encontramos hipoconectividades entre la red visual y las redes estriatal y sensorial; todas con incrementos en actividad. Por el contrario, hubo hiperconectividades entre tres pares de redes cerebrales: 1) red prefrontal cingulada media y red estriatal, 2) red sensorial y red parietal de asociación y 3) red motora-retroesplenial y red sensorial, siendo la red parietal de asociación la única red sin incremento de actividad. Estos resultados indican que las redes cerebrales ya se alteran desde una fase temprana de consumo continuo y prolongado de alcohol, disminuyendo el control ejecutivo y la flexibilidad comportamental. ii) Para comparar el volumen de materia gris (MG) cortical entre 34 controles sanos y 35 pacientes con dependencia al alcohol, desintoxicados y en abstinencia de 1 a 5 semanas, realizamos un análisis de morfometría basado en vóxel. Las principales estructuras cuyo volumen de MG disminuyó en los sujetos en abstinencia fueron el giro precentral (GPreC), el giro postcentral (GPostC), la corteza motora suplementaria (CMS), el giro frontal medio (GFM), el precúneo (PCUN) y el lóbulo parietal superior (LPS). Disminuciones de MG en el volumen de esas áreas pueden dar lugar a cambios en el control de los movimientos (GPreC y CMS), en el procesamiento de información táctil y propioceptiva (GPostC), personalidad, previsión (GFM), reconocimiento sensorial, entendimiento del lenguaje, orientación (PCUN) y reconocimiento de objetos a través de su forma (LPS). iii) Caracterizar estados cerebrales dinámicos en señales de RMf mediante una metodología basada en un modelo oculto de Markov (HMM en inglés)-Gaussiano en un paradigma con diseño de bloques, junto con distintas señales temporales de múltiples redes: componentes independientes y modos funcionales probabilísticos (PFMs en inglés) en 14 sujetos sanos. Cuatro condiciones experimentales formaron el paradigma de bloques: reposo, visual, motora y visual-motora. Mediante la aplicación de HMM-Gaussiano a los PFMs pudimos caracterizar cuatro estados cerebrales a partir de la actividad media de cada PFM. Los cuatro mapas espaciales obtenidos fueron llamados HMM-reposo, HMM-visual, HMM-motor y HMM-RND (red neuronal por defecto). HMM-RND apareció una vez el estado de tarea se había estabilizado. En un futuro cercano se espera obtener estados cerebrales en nuestros datos de RMf-er en ratas, para comparar dinámicamente el comportamiento de las redes cerebrales como un biomarcador de TCA. En conclusión, las técnicas de neuroimagen aplicadas en imagen de RM multimodal para estimar la conectividad cerebral en estado de reposo, la actividad cerebral y el volumen de materia gris han permitido avanzar en el entendimiento de los mecanismos homeostático
La ingesta d'alcohol altera el balanç del cervell a nivell estructural i funcional i pot causar trastorns per consum d' alcohol (TCA). L'objectiu d'aquesta Tesi Doctoral fou estudiar els efectes en el cervell del consum crònic i excessiu d'alcohol, des d'un punt de vista funcional i estructural i per mitjà d'anàlisi d'imatges de ressonància magnètica (RM). Vam realitzar tres anàlisis amb objectius específics: i) Per a entendre com les neuroadaptacions desencadenades pel consum d'alcohol es veuen reflectides en la connectivitat cerebral funcional entre xarxes cerebrals, així com en l'activitat cerebral, vam realitzar estudis en rates msP en les condicions de control i després d'un mes amb accés a alcohol. Per a cada subjecte vam obtindre els senyals de les xarxes cerebrals tras aplicar a les imatges funcionals de RM en estat de repòs una anàlisi probabilística de components independents i regressió espacial. Després, estimàrem la connectivitat cerebral en estat de repòs per mitjà de correlació parcial regularitzada. Per a una lectura de l'activitat cerebral vam adquirir imatges de RM realçades amb manganés. En la condició d'alcohol vam trobar hipoconnectivitats entre la xarxa visual i les xarxes estriatal i sensorial, totes amb increments en activitat. Al contrari, va haver-hi hiperconnectivitats entre tres parells de xarxes cerebrals: 1) xarxa prefrontal cingulada mitja i xarxa estriatal, 2) xarxa sensorial i xarxa parietal d'associació i 3) xarxa motora-retroesplenial i xarxa sensorial, sent la xarxa parietal d'associació l'única xarxa sense increment d'activitat. Aquests resultats indiquen que les xarxes cerebrals ja s'alteren des d'una fase primerenca caracteritzada per consum continu i prolongat d'alcohol, disminuint el control executiu i la flexibilitat comportamental. ii) Per a comparar el volum de MG cortical entre 34 controls sans i 35 pacients amb dependència a l'alcohol, desintoxicats i en abstinència de 1 a 5 setmanes vam emprar anàlisi de morfometria basada en vòxel. Les principals estructures on el volum de MG va disminuir en els subjectes en abstinència van ser el gir precentral (GPreC), el gir postcentral (GPostC), la corteça motora suplementària (CMS), el gir frontal mig (GFM), el precuni (PCUN) i el lòbul parietal superior (LPS). Les disminucions de MG en eixes àrees poden donar lloc a canvis en el control dels moviments (GPreC i CMS), en el processament d'informació tàctil i propioceptiva (GPostC), personalitat, previsió (GFM), reconeixement sensorial, enteniment del llenguatge, orientació (PCUN) i reconeixement d'objectes a través de la seua forma (LPS). iii) Caracterització de les dinàmiques temporals del cervell com a diferents estats cerebrals, en senyals de RMf mitjançant una metodologia basada en un model ocult de Markov (HMM en anglès)-Gaussià en imatges de RMf, junt amb dos tipus de senyals temporals de múltiples xarxes cerebrals: components independents i modes funcionals probabilístics (PFMs en anglès) en 14 subjectes sans. Quatre condicions experimentals van formar el paradigma de blocs: repòs, visual, motora i visual-motora. HMM-Gaussià aplicat als PFMs (senyals de RM funcional de xarxes cerebrals) va permetre la millor caracterització dels quatre estats cerebrals a partir de l'activitat mitjana de cada PFM. Els quatre mapes espacials obtinguts van ser anomenats HMM-repòs, HMM-visual, HMM-motor i HMM-XND (xarxa neuronal per defecte). HMM-XND va aparèixer una vegada una tasca estava estabilitzada. En un futur pròxim s'espera obtindre estats cerebrals en les nostres dades de RMf-er en rates, per a comparar dinàmicament el comportament de les xarxes cerebrals com a biomarcador de TCA. En conclusió, s'han aplicat tècniques de neuroimatge per a estimar la connectivitat cerebral en estat de repòs, l'activitat cerebral i el volum de MG, aplicades a imatges multimodals de RM i s'han obtés resultats que han permés avançar en l'enteniment dels m
Alcohol intake alters brain balance, affecting its structure and function, and it may cause Alcohol Use Disorders (AUDs). We aimed to study the effects of chronic, excessive alcohol consumption on the brain from a functional and structural point of view, via analysis of multimodal magnetic resonance (MR) images. We conducted three studies with specific aims: i) To understand how the neuroadaptations triggered by alcohol intake are reflected in between-network resting-state functional connectivity (rs-FC) and brain activity in the onset of alcohol dependence, we performed studies in msP rats in control and alcohol conditions. Group probabilistic independent component analysis (group-PICA) and spatial regression were applied to resting-state functional magnetic resonance imaging (rs-fMRI) images to obtain subject-specific time courses of seven resting-state networks (RSNs). Then, we estimated rs-FC via L2-regularized partial correlation. We performed a manganese-enhanced (MEMRI) experiment as a readout of neuronal activity. In alcohol condition, we found hypoconnectivities between the visual network (VN), and striatal (StrN) and sensory-cortex (SCN) networks, all with increased brain activity. On the contrary, hyperconnectivities were found between three pairs of RSNs: 1) medial prefrontal-cingulate (mPRN) and StrN, 2) SCN and parietal association (PAN) and 3) motor-retrosplenial (MRN) and SCN networks, being PAN the only network without brain activity rise. Interestingly, the hypoconnectivities could be explained as control to alcohol transitions from direct to indirect connectivity, whereas the hyperconnectivities reflected an indirect to an even more indirect connection. These findings indicate that RSNs are early altered by prolonged and moderate alcohol exposure, diminishing the executive control and behavioral flexibility. ii) To compare cortical gray matter (GM) volume between 34 healthy controls and 35 alcohol-dependent patients who were detoxified and remained abstinent for 1-5 weeks before MRI acquisition, we performed a voxel-based morphometry analysis. The main structures whose GM volume decreased in abstinent subjects compared to controls were precentral gyrus (PreCG), postcentral gyrus (PostCG), supplementary motor cortex (SMC), middle frontal gyrus (MFG), precuneus (PCUN) and superior parietal lobule (SPL). Decreases in GM volume in these areas may lead to changes in control of movement (PreCG and SMC), in processing tactile and proprioceptive information (PostCG), personality, insight, prevision (MFG), sensory appreciation, language understanding, orientation (PCUN) and the recognition of objects by touch and shapes (SPL). iii) To characterize dynamic brain states in functional MRI (fMRI) signals by means of an approach based on the Hidden Markov model (HMM). Several parameter configurations of HMM-Gaussian in a block-design paradigm were considered, together with different time series: independent components (ICs) and probabilistic functional modes (PFMs) on 14 healthy subjects. The block-design fMRI paradigm consisted of four experimental conditions: rest, visual, motor and visual-motor. Characterizing brain states' dynamics in fMRI data was possible applying the HMM-Gaussian approach to PFMs, with mean activity driving the states. The four spatial maps obtained were named HMM-rest, HMM-visual, HMM-motor and HMM-DMN (default mode network). HMM-DMN appeared once a task state had stabilized. The ultimate goal will be to obtain brain states in our rs-fMRI rat data, to dynamically compare the behavior of brain RSNs as a biomarker of AUD. In conclusion, neuroimaging techniques to estimate rs-FC, brain activity and GM volume can be successfully applied to multimodal MRI in the advance of the understanding of brain homeostasis in AUDs. These functional and structural alterations are a biomarker of chronic alcoholism to explain impairments in executive control, reward evaluation and visuospatial processing.
Pérez Ramírez, MÚ. (2018). Characterizing functional and structural brain alterations driven by chronic alcohol drinking: a resting-state fMRI connectivity and voxel-based morphometry analysis [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/113164
TESIS

The development of functional connectivity is often described as changing from local to distributed connections which give rise to the functional brain networks observed in adulthood. In contrast to the well-explored pattern found in functional connectivity, no research has been published describing effective connectivity development. Also, there is a plethora of literature describing functional connectivity patterns in a variety of neurodevelopmental and internalizing disorders, but there is little consistency in the connectivity patterns discovered for each disorder. Hence, this study aimed to describe functional and effective resting-state connectivity during adolescent development in a typically developing adolescent (TDA) group (n = 128) and to determine how adolescents with comorbid neurodevelopmental disorders (CND) (n = 46) differed. This was accomplished through functional and effective connectivity analysis within and between four networks: the Default Mode Network (DMN), the Salience Network (SN), the Dorsal Attention Network (DAN), and the Frontal Parietal Control Network (FPCN). The results from this study indicate that within-network connectivity decreased across age in the TDA group, which is in opposition to previous work which suggests strengthening within-network connectivity. The CND group displayed hyper-connectivity compared to the TDA group in between-network connectivity with no effect of age. The effective connectivity in the TDA group displayed decreasing connectivity within networks with increasing age, a novel effect not previously reported in the literature. The CND group’s effective connectivity was overall hyper-connected (for within- and between-networks). The functional connectivity patterns in the TDA group suggest that functional connectivity has subtle developmental change during adolescence. Further, the CND group consistently displayed hyper-connectivity in functional and effective connectivity. The CND group, and perhaps similar comorbid groups, may have less efficient networks which could contribute to their disorder(s).

Converging evidence from a variety of fields, including psychiatry, suggests that the temporal correlates of the brain’s resting state could serve as essential markers of a healthy and efficient brain. We use ketamine to induce schizophrenia-like states in 32 healthy individuals to examine the brain’s resting states using fMRI. We found a global reduction in temporal variability quantified by the time series’ standard deviation and an increase in scale-free properties quantified by the Hurst exponent representing the signal self-affinity over time. We also found network-specific and frequency-specific effects of ketamine on these temporal measures. Our results confirm prior studies in aging, sleep, anesthesia, and psychiatry suggesting that increased self-affinity and decreased temporal variability of the brain resting state could indicate a compromised and inefficient brain state. Our results expand our systemic view of the temporal structure of the brain and shed light on promising biomarkers in psychiatry

La connectivité fonctionnelle à l'état de repos (CF), mesurée avec l'imagerie par résonance magnétique fonctionnelle (IRMf), a mis en évidence des connexions fonctionnelles entre le cervelet et les régions cognitives du cerveau,qui a soutenu un rôle important pour le cervelet dans la cognition. Ces résultats ont été basés sur des mesures statiques de la CF. Cependant, il s'agit d'une approche simpliste du CF qui a récemment été remise en question, les résultats indiquant la présence d'une dynamique continue et non aléatoire dans le CF à de courts intervalles de quelques secondes, ce qui, étant donné la nature dynamique du cerveau, est une vision plus naturelle qui peut coder des informations sur des fonctions cognitives complexes. Jusqu'à présent, le cervelet a été négligé dans la plupart des études sur la CF dynamique, malgré son rôle bien reconnu dans les fonctions cognitives complexes. Dans cette thèse, nous avons émis l'hypothèse que la dynamique du cervelet au repos peut être significative, en saisissant des aspects de la cognition et du comportement non pris en compte par le cervelet statique et en présentant des altérations des troubles cérébraux associés au dysfonctionnement cérébro-cérébelleux, comme l'alcoolisme. Nous avons testé ces hypothèses dans deux études distinctes portant sur la dynamique de la CF cérébro-cérébelleuse en relation avec des traits complexes, tels que l'impulsivité (première étude) et l'alcoolisme (deuxième étude). La première étude a été motivée par une hypothèse récente sur le rôle du cervelet dans l'impulsivité, un trait de personnalité complexe défini comme la tendance à agir sans prévoyance. Nous avons émis l'hypothèse que les différences individuelles dans les traits normaux d'impulsivité pouvaient être associées à la force (statique) et à la variabilité temporelle (dynamique) du CF cérébro-cérébelleux. Nous avons testé cette hypothèse en utilisant des données d'IRMf à l'état de repos et des auto-rapports d'impulsivité (UPPS-P et BIS/BAS) d'un groupe d'individus en bonne santé (N=134). En particulier, nous avons utilisé des techniques robustes pour identifier les réseaux cérébraux et cérébelleux, calculer des mesures sommaires de la CF statique et dynamique, et tester les associations avec l'impulsivité. Nous avons observé des preuves liant de multiples formes d'impulsivité à la force et à la variabilité temporelle de la CF au repos entre le cervelet et un ensemble de réseaux cérébraux dynamiques et intégratifs qui soutiennent le contrôle cognitif et les processus de récompense, ce qui soutient notre hypothèse selon laquelle la dynamique de la CF cérébro-cérébelleuse est pertinente sur le plan comportemental. Dans la seconde étude, nous avons émis l'hypothèse que la dynamiques de la CF cérébro-cérébelleuse différerait entre les les patients alcooliques et les contrôles, en particulier dans les circuits frontocérébelleux. Pour tester cette hypothèse, nous avons exploré les différences de dynamiques de la CF cérébro-cérébelleuse entre un groupe de patients alcooliques (N=18) et un groupe de contrôles (N=18), en comparant des groupes sur différentes mesures de connectivité dynamique. Les résultats ont révélé une altération de la dynamique du réseau fonctionnel cérébro-cérébelleux chez les sujets alcooliques, caractérisée par une hypervariabilité de la CF dans les réseaux fronto-parieto-cérébelleux, une réduction de la flexibilité cérébelleuse et une augmentation de l'intégration cérébelleuse. Ces résultats suggèrent un rôle possible de la dynamique des réseaux fronto-pariétal-cérébelleux dans la physiopathologie de ce trouble. Pris ensemble, les résultats de cette thèse soulignent l'utilité de compléter les approches statiques de la CF par une analyse dynamique de la CF pour approfondir notre compréhension du fonctions des réseaux cérébro-cérébelleux et les neurobiologie des comportements complexes et les troubles du cerveau
Studies of resting-state functional connectivity (FC), measured by functional magnetic resonance imaging (rsfMRI), have revealed extensive functional connections between the cerebellum and association regions in the brain, supporting an important role for the cerebellum in cognition. These findings have been based on static FC measures averaged across entire scans spanning a few minutes. However, this is a narrow view that has been recently challenged, with findings pointing to the presence of an ongoing, behaviorally relevant dynamics in resting-state FC occurring at short timescales of a few seconds, which, given the dynamic nature of the brain, is a more natural view that may encode information about complex cognitive functions. So far, however, the cerebellum has been overlooked in most, if not all, studies of dynamic FC, despite its well-recognized role in coordinating complex cognitive functions. In this thesis, we hypothesized that the dynamics of cerebro-cerebellar FC, during rest, may be behaviorally relevant, capturing aspects of cognition and behavior not accounted for by static FC and exhibiting alterations in brain disorders commonly associated with cerebro-cerebellar dysfunction, such as alcohol use disorder (AUD). We tested these hypotheses in two separate studies focusing on the dynamics of cerebro-cerebellar FC in relation to complex traits and disorders, such as impulsivity (first study) and AUD (second study). The first study has been motivated by a recent hypothesis for a role of the cerebellum in impulsivity; a complex personality trait defined as the tendency to act without foresight. We hypothesized that individual differences in normal impulsivity traits could be associated with the (static) strength and (dynamic) temporal variability of cerebro-cerebellar resting-state FC. We tested this hypothesis using rsfMRI data and self-report questionnaires of impulsivity (UPPS-P and BIS/BAS) collected from a group of healthy individuals. In particular, we employed data-driven techniques to identify cerebral and cerebellar resting-state networks, compute summary measures of static and dynamic FC, and test for associations with self-reported impulsivity. We observed evidence linking multiple forms of impulsivity to the strength and temporal variability of resting-state FC between the cerebellum and a set of highly dynamic and integrative brain networks that support top-down cognitive control and bottom-up reward/saliency processes, supporting our hypothesis that cerebro-cerebellar FC dynamics are behaviorally relevant. In the second study, we hypothesized that the dynamics of cerebro-cerebellar FC at short timescales would differ between AUD and controls, especially in the frontocerebellar circuits. To test this hypothesis, we explored the differences in the dynamic cerebro-cerebellar FC between an AUD group (N=18) and a group of unaffected controls (N=18) by comparing groups on different dynamic connectivity measures. Results revealed altered cerebro-cerebellar FC dynamics in the AUD group characterized by hypervariability of FC within fronto-parieto-cerebellar networks, reduced cerebellar flexibility, and increased cerebellar integration, compared with controls. These results suggest a possible role for the dynamics of fronto-parieto-cerebellar networks in the pathophysiology of this disorder. Taken together, the findings from this thesis highlight the utility of complementing static FC approaches with dynamic FC analysis in furthering our understanding of the functional repertoire of cerebro-cerebellar networks and the neurobiological architecture of complex behaviors and brain disorders

The cognitive decline associated with healthy aging begins in early adulthood and is important to understand as a precursor of and relative to mild cognitive impairment and Alzheimer disease. Anatomical atrophy, functional compensation, and network reorganization have been observed in populations of older adults. In the current study, we examine functional network correlates of memory performance on the Wechsler Memory Scale IV and the Mnemonic Discrimination Task (MST). We report a lack of association between global graph theory metrics and age or memory performance. In addition, we observed a positive association between lure discrimination scores from the MST and right hippocampus centrality. Upon further investigation, we confirmed that old subjects with poor memory performance had lower right hippocampus centrality scores than young subjects with high average memory performance. These novel results connect the role of the hippocampus in global brain network information flow to cognitive function and have implications for better characterizing and predicting memory decline in aging.

The default mode network (DMN) is active in the absence of task demands and during self-referential thought. Considerable evidence suggests that the DMN is involved in normative aspects of social cognition, and as such, disruptions in the function of DMN would be expected in disorders characterized by alterations in social function. Consistent with this notion, work in autism spectrum disorder (ASD) and social anxiety disorder (SAD) has demonstrated altered activation of several core regions of the DMN relative to neurotypical controls. Despite emergent evidence for alterations within the same brain systems in SAD and ASD, as well as a behavioral continuum of social impairments, no study to date has examined what is unique and what is common to the brain systems within these disorders. Therefore, the primary aim of the current study is to precisely characterize the topology of neural connectivity within the DMN in SAD and ASD and neurotypical controls in order to test the following hypotheses through functional and structural connectivity analyses of the DMN. Our analyses demonstrate increased coavtivation of the dorsomedial prefrontal cortex in ASD and SAD compared to controls, as well as over and under connectivity in structural brain connectivity in ASD. These results may reflect general deficits in social function at rest, and disorder specific alterations in structural connectivity in ASD.
Master of Science

La mémoire de reconnaissance nous permet à la fois de détecter rapidement un stimulus précédemment perçu (familiarité), et de récupérer des informations relatives au contexte de notre rencontre avec ce stimulus (recollection). Les modèles neuro-anatomiques d'Aggleton et Brown (1999) puis d'Aggleton et al. (2011) postulent que le noyau antérieur (NA) du thalamus et le tractus mamillo-thalamique (TMT) du fait de leurs connexions avec l'hippocampe font partie du circuit de la recollection tandis que le noyau dorso-médian (DM) participerait à la familiarité en raison de ses connexions avec le cortex périrhinal. Dans cette thèse nous avons testé cette hypothèse d'indépendance. 12 patients avec un infarctus thalamique gauche ont été recrutés ainsi qu'un groupe de sujets contrôles appariés. Tous les participants ont été soumis à un bilan neuropsychologique, à trois tâches expérimentales de mémoire de reconnaissance et à un examen d'IRM morphologique et d'IRM fonctionnelle de repos. Selon les tâches nous avons estimé la contribution de la recollection et de la familiarité à la réponse sur la base de la verbalisation de la source, du degré de confiance dans la réponse ou de la catégorisation des réponses. Les lésions thalamiques ont été quantifiées et localisées automatiquement grâce à une nouvelle approche méthodologique que nous avons développée. Le profil neuropsychologique des patients a mis en évidence une amnésie antérograde verbale et un trouble exécutif modéré (Etude 1). Les lésions atteignaient principalement le DM alors que le NA était intact chez tous. Le TMT était lésé chez les 7 patients les plus amnésiques (Etudes 1 et 2). La recollection était altérée chez les patients quelle que soit la tâche alors que la familiarité était préservée. De plus l'indice de recollection corrélait avec la lésion du DM (Etude 2). Enfin, des corrélations ont été trouvées dans l'étude en connectivité fonctionnelle entre la disconnexion thalamo-frontale et la recollection (Etude 3). En somme, ces résultats signifient i\ qu'une lésion du NA n'est pas nécessaire pour causer une amnésie ii\ qu'une lésion du DM est suffisante pour causer un défaut de recollection mais pas nécessaire pour atteindre la familiarité iii\ qu'une lésion du TMT prédit une amnésie sévère, enfin iv\ que le réseau reliant fonctionnellement le DM au cortex préfrontal semble être impliqué dans l'expérience subjective de la mémoire de reconnaissance plutôt que dans ses contenus. Ils suggèrent de plus que le modèle d'Aggleton et al (2011) devrait être révisé en ce qui concerne la relation familiarité / DM
Recognition memory allows determining whether a stimulus has been previously encountered based on either a rapid detection process (familiarity) or a longer retrieval of the context associated with the stimulus (recollection). Aggleton and Brown's model (1999) and Aggleton and colleagues (2011) postulated that recollection and familiarity are anatomically and functionally independent. They hypothesized that the anterior nucleus (AN) / mamillothalamic tract (MTT) complex of the thalamus would be critical for recollection due to its connections with the hippocampus. The Mediodorsal (MD) nucleus would support familiarity owing to its links with the perirhinal cortex. In this thesis we tested this independence hypothesis. The 12 subjects with a pure left thalamic infarction were included along with a healthy matched control group. Every subject underwent a neuropsychological assessment, three experimental verbal recognition memory tasks, a high-resolution structural volumetric MRI scan and resting state functional imaging. Recollection and familiarity estimations were derived from subjective reports or responses categorization. We specifically developed the methods used to automatically analyse the volume and localization of the lesions. Patients performed worse than controls on verbal memory and to a lesser extent on executive tasks (Study 1). Most of the lesions were located in the MD while no lesion of the AN was found. The seven patients exhibiting MTT damage had the lowest memory performance (Studies 1 and 2). Recollection was lower in patients than in controls in all the three tasks whereas familiarity was systematically normal. In addition we found a significant correlation between the recollection index and the DM damage, suggesting that DM is directly involved in recollection (Article 2). Finally the functional connectivity results showed a correlation between recollection and a pattern of thalamofrontal disconnection in the patients, helping to understand the DM-recollection relationship. Overall, the findings of the different studies mean that i\ AN damage is rare and is not necessary to cause an amnesia, ii\ MD damage is sufficient to cause a recollection impairment but not necessary to impair familiarity, iii\ MTT damage predicts the severity of the amnesia, iv\ the network linking functionally the MD with the prefrontal cortex seems to be involved in the subjective experience associated with recognition memory