Functional magnetic resonance imaging (fMRI) enables non-invasive investigation of the neural functions with excellent spatial resolution. Although fMRI has primarily been developed in young adult populations, its use is becoming widespread in paediatrics. However, there are many differences, both behavioural and physical, between adults and children requiring adjustments to imaging and analysis methodology to optimise the results in children. This thesis examines fMRI analysis methodology to improve the detection of developmental changes in the brain. The work uses an emotional and familiar face paradigm that elicits strong BOLD fMRI responses in the fusiform, a region that is still developing across childhood. This face paradigm also enables the comparison of the fusiform responses to the primary visual cortex to link to extensive results in the literature. Thirty five 4-8 year old children and fourteen adults (18-30 years old) were scanned. To address the concern of anatomical size differences between the brains of adults and children, the anatomical variability of the fusiform was measured and the validity of stereotaxic transformation into an adult template was confirmed for the children. To investigate the effect of threshold settings between the adults and children, individual subject analyses of the peak activation location, estimated signal percent change and noise values were calculated using the general linear model (GLM). Similar functional peak locations between individuals were quantitatively selected using a novel application of the activation likelihood estimation (ALE). Also, several different preprocessing steps were evaluated for their ability to correct for the increased motion frequently seen in children, in a quantitative framework (NPAIRS) using canonical variates analysis (CVA), a data driven multivariate model as well as the standard univariate GLM. Functional differences between the adults and the children were identified in the fusiform by applying these optimised procedures. The results of this thesis demonstrate that thresholding and preprocessing pipelines must be made in a group-specific fashion. These methods can also be extended to elderly populations, enabling the investigation of the complete ageing spectrum with fMRI.

Functional magnetic resonance imaging (fMRI) enables non-invasive investigation of the neural functions with excellent spatial resolution. Although fMRI has primarily been developed in young adult populations, its use is becoming widespread in paediatrics. However, there are many differences, both behavioural and physical, between adults and children requiring adjustments to imaging and analysis methodology to optimise the results in children. This thesis examines fMRI analysis methodology to improve the detection of developmental changes in the brain. The work uses an emotional and familiar face paradigm that elicits strong BOLD fMRI responses in the fusiform, a region that is still developing across childhood. This face paradigm also enables the comparison of the fusiform responses to the primary visual cortex to link to extensive results in the literature. Thirty five 4-8 year old children and fourteen adults (18-30 years old) were scanned. To address the concern of anatomical size differences between the brains of adults and children, the anatomical variability of the fusiform was measured and the validity of stereotaxic transformation into an adult template was confirmed for the children. To investigate the effect of threshold settings between the adults and children, individual subject analyses of the peak activation location, estimated signal percent change and noise values were calculated using the general linear model (GLM). Similar functional peak locations between individuals were quantitatively selected using a novel application of the activation likelihood estimation (ALE). Also, several different preprocessing steps were evaluated for their ability to correct for the increased motion frequently seen in children, in a quantitative framework (NPAIRS) using canonical variates analysis (CVA), a data driven multivariate model as well as the standard univariate GLM. Functional differences between the adults and the children were identified in the fusiform by applying these optimised procedures. The results of this thesis demonstrate that thresholding and preprocessing pipelines must be made in a group-specific fashion. These methods can also be extended to elderly populations, enabling the investigation of the complete ageing spectrum with fMRI.

Neuroscience research has followed two fairly distinct paths in investigating central neural mechanisms of pain and emotion. Rarely have studies been conducted which intentionally combined painful and emotional stimulation while observing brain function. Theories of emotion and pain processing predict an interaction between pain and emotion such that emotional states may serve to both increase or decrease pain. This increase or decrease may also correspond to different effects on different dimensions of the overall pain experience as defined in pain neuromatrix theory. Theories of emotion begin with emotions as interpretations of bodily states, to more contemporary theories focusing on the functions of emotions. These emotion theories predict neuroanotomic relations between emotion and pain in the brain. Similarly neuromatrix theory predicts an affective dimension of pain experience, which has been defined in terms of pain unpleasantness and secondary affect, emphasizing the role of emotion in pain experience. To further explore the relationship between pain and emotion, in the present study, painful heat stimulation is applied to the face while simultaneously conducting whole brain imaging using functional magnetic resonance imaging (fMRI). Also personal episodes involving anger, fear, and neutral emotion are recalled during fMRI both with, and without, painful heat stimulation. Similar brain regions are involved in processing pain, anger, and fear, and these responses compare favorably with those in the literature. The results also demonstrate that simultaneous emotional episode recall modulates the patterns of brain activity involved in pain. Anger recall especially seems to increase pain-related activity. The study allows greater understanding about the way that the brain's emotional processing networks for fear and anger affect pain experience and how pain affects the emotional processing network to produce affective experience, such as fear and anger, related to pain. Further application of these procedures to patients with chronic pain can aid understanding of central pathological mechanisms involved.

Reliving the past requires the integration of multi-modal sensory details into a coherent mental impression of the initial event. In most people, memory for life episodes, or Autobiographical Memory (AM), is rich in sensory-perceptual elements that provide the vivid impression of travelling back in time. Abundant evidence indicates that the hippocampus plays a central role in AM recollection, but much research is still needed to determine which AM attributes engage the hippocampus at retrieval. My work assessed the relationship between hippocampal function and the perceptual richness of memory episodes. I designed a paradigm that captured the complexity of AM, and that manipulated perceptual richness while controlling for other AM confounds, such as recency, rehearsal, personal relevance, and “story” content. Participants studied and recalled perceptually enriched and impoverished laboratory events (film clips and written narratives, respectively) matched for the complexity of their storyline. An AM condition was also included for comparison. I tested healthy individuals and participants with unilateral medial temporal lobe epilepsy (mTLE), a clinical population with well documented hippocampal damage, on this paradigm. Perceptual richness was greatly reduced in people with mTLE, an effect that was most salient in the perceptually enriched conditions (AM and film clips). In a functional MRI version of this paradigm conducted on healthy individuals, I identified neural regions sensitive to the perceptual richness of AM and laboratory events, which included the anterior portion of the right hippocampus and other regions known to play a role in imagery and visual processing. In patients with right-lateralized mTLE, activation in these brain regions was markedly reduced in all memory conditions, which was consistent with the reduced perceptual richness I observed behaviourally. I reveal a clear relationship between hippocampal function and the perceptual richness of episodic memory, suggesting that the hippocampus plays a central role among brain regions that support the integration of multi-modal details into enriched memory experiences. My findings also advance our knowledge of how pathology and the nature of memory representation affect the neural correlates of episodic memory.

Reliving the past requires the integration of multi-modal sensory details into a coherent mental impression of the initial event. In most people, memory for life episodes, or Autobiographical Memory (AM), is rich in sensory-perceptual elements that provide the vivid impression of travelling back in time. Abundant evidence indicates that the hippocampus plays a central role in AM recollection, but much research is still needed to determine which AM attributes engage the hippocampus at retrieval. My work assessed the relationship between hippocampal function and the perceptual richness of memory episodes. I designed a paradigm that captured the complexity of AM, and that manipulated perceptual richness while controlling for other AM confounds, such as recency, rehearsal, personal relevance, and “story” content. Participants studied and recalled perceptually enriched and impoverished laboratory events (film clips and written narratives, respectively) matched for the complexity of their storyline. An AM condition was also included for comparison. I tested healthy individuals and participants with unilateral medial temporal lobe epilepsy (mTLE), a clinical population with well documented hippocampal damage, on this paradigm. Perceptual richness was greatly reduced in people with mTLE, an effect that was most salient in the perceptually enriched conditions (AM and film clips). In a functional MRI version of this paradigm conducted on healthy individuals, I identified neural regions sensitive to the perceptual richness of AM and laboratory events, which included the anterior portion of the right hippocampus and other regions known to play a role in imagery and visual processing. In patients with right-lateralized mTLE, activation in these brain regions was markedly reduced in all memory conditions, which was consistent with the reduced perceptual richness I observed behaviourally. I reveal a clear relationship between hippocampal function and the perceptual richness of episodic memory, suggesting that the hippocampus plays a central role among brain regions that support the integration of multi-modal details into enriched memory experiences. My findings also advance our knowledge of how pathology and the nature of memory representation affect the neural correlates of episodic memory.

This document addresses the development and implementation of a routine for analyzing resting-state functional Magnetic Resonance Imaging (rs-fMRI) data in rodents. Even though resting-state connectivity is studied in humans already for several years with diverse applications in mental disorders or degenerative brain diseases, the interest for this modality is much more recent and less common in rodents. The goal of this project is to set an ensemble of tools in order to be able for the experimental MR team of KERIC to analyze rs-fMRI in rodents in a well defined and easy way. During this project several critical choices have been done, one of them is to use the Independent Component Analysis (ICA) in order to process the data rather than a seed-based approach. Also it was decided to use medetomidine as anesthesia rather than isoflurane for the experiments. The routine developed during this project was applied for a project studying the effects of running on an animal model of depression. The routine is composed of several steps, the preprocessing of the data mainly realized with SPM8, the processing using GIFT and the postprocessing which is some statistic tests on the results from GIFT in order to reveal differences between groups using the 2nd level analysis from SPM8 and the testing the correlations between components using the FNC toolbox. / Detta dokument behandlar utvecklingen och implementeringen av en rutin för att analysera bilder från resting-state funktionell Magnetisk Resonenstomografi i gnagare. Även om resting-state connectivity studerats i människor i några år, med olika applikationer i psykiska störningar och neurodegenerativa sjukdomar, är intresset för detta område är betydligt nyare bland experimentell förskare som arbetar med gnagare. Målet av denna projekt är att inställa en procedur så att KERICs experimentell MR team kan lätt analysera resting-state funktionnell MRT data. Under denna projekt har olika viktiga val gjorts, en av dem är att använda Independent Component Analysis procedur för att analysera data framför en seed-baserad teknik. En andra var att använda för anestesi medetomidin och inte isofluran för experiment. Rutinen som var utvecklad under denna projekt blev användad på data från en projekt som studerar effekter av löpning på depression hos råttorna. Rutinen är delad i några delar, den första är att förbehandla data främst med SPM8, den andra är att använda GIFT för att behandla data och den sista är att testa statistiskt resultat från ICA med SPM8 och att testa korrelation mellan komponenter med FNC.

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Depression has increased among adolescents and adults over the last decade. Effective treatments and techniques to improve personal well being and disorders like depression are much needed. Mindfulness is a psychological technique that involves actively paying attention to one's awareness of the present moment with nonjudgmental acceptance (Kabat-Zinn, 1990). While the effectiveness of mindfulness interventions has been widely studied, relatively little research has been done on the effects of mindfulness interventions on depression using neuroimaging techniques such as functional magnetic resonance imaging (fMRI) or electroencephalography (EEG). This systematic review includes four studies that investigates the effectiveness of mindfulness interventions on depression measured by fMRI or EEG and different rating scales measuring depression. The results from this systematic review shows that mindfulness interventions may have an effect on depression. Two of the studies (Ferri et al., (2017); Yang et al., (2016), found significant differences in the rating scales for depression. The studies also suggest that mindfulness interventions can impact the brain regions involved in negative emotional processing in individuals with depression, such as the default mode network (DMN) and the dorsal anterior cingulate cortex (dACC).

An ever-increasing number of functional magnetic resonance imaging (fMRI) studies are now using information-based multi-voxel pattern analysis (MVPA) techniques to decode mental states. In doing so, they achieve a significantly greater sensitivity compared to when they use univariate analysis frameworks. Two most prominent MVPA methods for information mapping are searchlight decoding and classifier weight mapping. The new MVPA brain mapping methods, however, have also posed new challenges for analysis and statistical inference on the group level. In this thesis, I discuss why the usual procedure of performing t-tests on MVPA derived information maps across subjects in order to produce a group statistic is inappropriate. I propose a fully nonparametric solution to this problem, which achieves higher sensitivity than the most commonly used t-based procedure. The proposed method is based on resampling methods and preserves the spatial dependencies in the MVPA-derived information maps. This enables to incorporate a cluster size control for the multiple testing problem. Using a volumetric searchlight decoding procedure and classifier weight maps, I demonstrate the validity and sensitivity of the new approach using both simulated and real fMRI data sets. In comparison to the standard t-test procedure implemented in SPM8, the new results showed a higher sensitivity and spatial specificity. The second goal of this thesis is the comparison of the two widely used information mapping approaches -- the searchlight technique and classifier weight mapping. Both methods take into account the spatially distributed patterns of activation in order to predict stimulus conditions, however the searchlight method solely operates on the local scale. The searchlight decoding technique has furthermore been found to be prone to spatial inaccuracies. For instance, the spatial extent of informative areas is generally exaggerated, and their spatial configuration is distorted. In this thesis, I compare searchlight decoding with linear classifier weight mapping, both using the formerly proposed non-parametric statistical framework using a simulation and ultra-high-field 7T experimental data. It was found that the searchlight method led to spatial inaccuracies that are especially noticeable in high-resolution fMRI data. In contrast, the weight mapping method was more spatially precise, revealing both informative anatomical structures as well as the direction by which voxels contribute to the classification. By maximizing the spatial accuracy of ultra-high-field fMRI results, such global multivariate methods provide a substantial improvement for characterizing structure-function relationships.