Rumination, or extensive mind wandering defines one of the key cognitive symptoms of major depressive disorder (MDD). Several symptoms included in the psychiatric disorder have been associated with altered connectivity within the large-scaled system default mode network (DMN). Although it’s well-known that antidepressant treatment, such as selective serotonin reuptake inhibitor (SSRI) and serotonin norepinephrine reuptake inhibitor (SNRI) tend to positively affect symptoms and alterations of MDD, results are inconsistent regarding DMN connectivity pre-and-post treatment. This systematic review aims to compile findings from studies investigating DMN connectivity in MDD patients’ pre-and post SSRI and SNRItreatment, and to find possible correlations with symptomatic improvements. Five articles were included for further analysis after the literature search in MEDLINE ESBSCO and Scopus. Main findings are in alignment with previous research and suggest both hypo-and hyper DMN connectivity at baseline in MDD patients, and connectivity patterns significantly similar to healthy controls following antidepressant treatment. Future research might consider placebo controlled trials for more diverse, and quantified results, and also consider further investigation on both first-line treatments and other promising antidepressants.

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第21919号 / 情博第702号 / 新制||情||120(附属図書館) / 京都大学大学院情報学研究科システム科学専攻 / (主査)教授 石井 信, 教授 松田 哲也, 教授 加納 学, 川人 光男 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

The brain connectivity of resting-state fMRI (rs-fMRI) represents an intrinsic state of brain architecture, and it has been used as a useful neural marker for detecting psychiatric conditions such as autism spectrum disorder, as well as for predicting psychosocial characteristics such as age. However, most studies using brain connectivity have focused more on the strength of functional connectivity over time (static-FC) than temporal features of connectivity changes (connectome variability). The primary goal of the current study was to investigate the effectiveness of using the connectome variability in classifying an individual’s pathological characteristics from others and predicting psychosocial characteristics. In addition, the current study aimed to prove that benefits of the connectome variability are reliable across various analysis procedures. To this end, three open public large rs-fMRI datasets including ABIDE, COBRE, and NKI were used. The static-FC and the connectome variability metrics were calculated with various brain parcellations and parameters and then utilized for subsequent machine learning (ML) classification and prediction. The results demonstrated that including the connectome variability increased the ML performances significantly in most cases of analytical variations. In addition, including the connectome variability prevented ML performance deterioration when excessive components were used. In conclusion, the current finding proved the usefulness of the connectome variability and its reliability. / M.S. / Functional magnetic resonance imaging (fMRI) with functional connectivity (FC) analysis has been widely used to understand the human brain’s system and cognitive processes. Especially, the resting-state fMRI (rs-fMRI) has been regarded as a comprehensive map of the brain’s large-scale functional architecture. Previous seminal findings demonstrated that brain regions show synchronized patterns even without any external stimulus or task (Biswal et al., 1995; Power et al., 2011), and recent studies also demonstrated that functional network architecture during tasks can be formed based on resting-state network architecture primarily suggesting that the resting-state is an intrinsic and fundamental of brain organization functionally. At the early stage of fMRI FC studies, researchers commonly adopted static measure of connectivity (static-FC) such as Pearson correlation. However, the brain has a dynamic nature, thus the static approach does not capture temporal information of the brain. In this context, time-varying or dynamic-FC has been suggested as a promising substitute. The derived dynamic-FC usually has been used to distinguish several dynamic states by identifying repeated spatial dynamic-FC profiles. Another utilization is quantifying moment-to-moment changes of dynamic-FC (connectome variability) which can represent how much dynamic-FC is stable. Interestingly, although its importance of dynamic-FC temporal features, few studies have utilized connectome variability. In addition, only a few studies compared static-FC and connectome variability (Fong et al., 2019; Wang et al., 2018). Therefore, it is necessary to demonstrate the benefits of connectome variability and its reliability across various cognitive domains and analytic procedures. To this aim, this study used three large open fMRI datasets: ABIDE comprised of autism spectrum disorder and typical development, COBRE comprised of schizophrenia and control group, and NKI which is a developmental dataset across the lifespan. In individuals’ resting-state fMRI, brain signal time series was extracted using various parcellation methods including AAL2 atlas (Rolls et al., 2015), bilateralized AAL2 atlas, and LAIRD network atlas (Laird et al., 2011). To calculate static-FC, pairwise Pearson correlation was used. For the dynamic-FC, sliding-window correlation was used with 60 second window size. Additional 90 second and 120 second sliding window sizes were also used to test the reliability of the current study. The additional sliding window sizes showed almost identical results to that of the main sliding window size (60s). The derived dynamic-FC was used to calculate ‘connectome variability’ using mean square successive difference (MSSD). The calculated static-FC and the connectome variability were inputted to support vector machine (SVM) for group classifications or support vector regression (SVR) for predicting individuals’ characteristics. Before machine learning analysis (SVM, SVR), lasso regression was adopted as a feature selection method. The SVM results showed that including connectome variability increased group classification performances in ABIDE and COBRE datasets. Interestingly, including connectome variability improved the robustness of SVM classification when the number of components was controlled. Similarly, the SVR results also demonstrated that including connectome variability increased prediction performances for autism symptom severity score (ADOS), social responsiveness score (SRS), and individuals’ age. These benefits were consistent across three parcellation schemes. In conclusion, the current study demonstrated that the connectome variability is useful to classify different groups and to predict individuals’ characteristics. Such benefits were reliable across multiple cognitive domains and robust to several analytic procedures. These results emphasized that the connectome variability which has been usually overlooked reflects some aspects of functional brain architecture, and future fMRI studies should more attend connectome variability between brain regions.

Variability in the brain’s functional network connectivity is associated with differences in cognition. The degree to which brain networks flexibly reconfigure, or alternatively remain stable, can differ across regions of cortex, across time, and across individuals. The goal of this dissertation was to investigate how the brain’s functional network architecture is reconfigured to support abstract reasoning and rule learning. I proposed that flexibility within frontoparietal cortex, combined with a stable network core, is beneficial for effective reasoning and rule learning. Experiment One investigated the activation patterns and dynamic community structure of brain networks associated with shifting task demands during abstract reasoning. Twenty-seven subjects underwent fMRI scanning during resting state and during a subsequent abstract reasoning task. When quantifying network reconfiguration between resting and task states, I found a stable system within default and somatomotor networks alongside a more flexible frontoparietal control network. The results motivated a novel understanding of how the brain performs reasoning tasks: an underlying stable functional network acts as a cognitive control mechanism, priming task-active nodes within frontoparietal cortex to variably activate for unique task conditions. Experiment Two used a dynamic network analysis to identify changes in functional brain networks that were associated with context-dependent rule learning. During fMRI scanning, twenty-nine naïve subjects were challenged to learn a set of context-dependent rules. Successful learners showed greater stability in ventral attention and somatomotor regions, increased assortative mixing of cognitive control regions as rules were learned, and greater segregation of attention networks throughout the entire task. The results suggested that a stable ventral attention network and a flexible frontoparietal control network support sustained attention and the formation of rule representations. In Experiment Three, I carried out a separate analysis of data from Experiment 2 to characterize the functional connectivity patterns with the hippocampus that emerged during successful rule learning. The results demonstrated that the hippocampal head became increasingly functionally connected to the lateral frontal pole and caudate in successful learners. Additionally, the entire hippocampus exhibited decreased functional connectivity with the mid-cingulate and precuneus in successful learners. These three experiments demonstrated that stable functional connectivity in somatomotor and ventral attention networks, combined with flexible reconfiguration of frontoparietal cortex, is advantageous for successful rule learning and abstract reasoning. Altogether, this dissertation demonstrated that individual differences in dynamic functional connectivity are associated with learning, and that stability of brain networks across time and tasks supports higher order cognition. / 2025-01-23T00:00:00Z

This dissertation concerns the alteration of functional connectivity in the human brain through different patterns of somatosensory stimulation. In particular, I distinguish whether stimuli are regular (i.e., expected by the subject) or irregular (i.e., unexpected by the subject). An emerging theory of brain function known as Predictive Coding states that the brain is continuously creating an internal model of its environment that is constantly trying to predict what is going to happen. Expected sensory input leads to model consol- idation, while unexpected input leads to model update. In this context it is assumed that central neuronal processing differs significantly between these two cases. Furthermore, in my experiments, the stimulation is applied in two more variants which are also believed to be processed in completely different ways: consciously perceptible (suprathreshold) and imperceptible (subthreshold). To measure functional connectivity in the acquired fMRI data, a method referred to as eigenvector centrality mapping (ECM) was chosen. This method has gained increasing attention in the fMRI community, as it represents a whole-brain approach that can be ap- plied for resting-state experiments. While there are a number of other centrality measures, each with their advantages and disadvantages, ECM stands out as being parameter-free and does not depend on prior assumptions. Similar to Google’s Pagerank algorithm, it assigns areas (“nodes”) in a network with a high centrality score that are closely connected to other central areas as well. Generally, increased connectivity is interpreted as of greater “importance” to the network. As there are different approaches on how to calculate ECM, I critically examine these and delve deeper into the method itself. Three main research questions guided this study: 1. Is there a brain connectivity (ECM) alteration in the human brain for somatosensory stimulation that is pattern dependent (7 Hz irregular vs. regular)? 2. Is there a brain connectivity (ECM) alteration in the human brain for somatosensory stimulation that is intensity dependent (7 Hz suprathreshold vs. subthreshold)? 3. Are these different somatosensory stimulations (subthreshold, suprathreshold, irregular, regular) accompanied by a subsequent behavioral change? Two experiments were conducted. In Experiment 1, participants were exposed to all four stimulation conditions consecutively. Results showed significant ECM alterations compared to the initial baseline, suggesting persisting effects. To counter this, Experiment 2 adopted a different approach. Here, individual stimulations were applied to separate groups, with an additional control group for comparison. The results from Experiment 2 revealed that irregular stimulation compared to regular showed decreased connectivity in specific brain regions, aligning with the Predictive Coding theory. Suprathreshold stimulation showed increased connectivity in areas related to sensory input integration, possibly linked to conscious perception. Furthermore, all participants, regardless of stimulation type, showed heightened connectivity in somatosensory regions, suggesting a shared focus on tactile anticipation. The behavioral session from Experiment 2 found that irregular suprathreshold stimulation led to a decreased sensitivity to near-threshold stimuli. However, this change wasn't mirrored in the functional connectivity data. In conclusion, this research validated the differential neural processing of various somatosensory stimulations, supporting the Predictive Coding theory. The study also underscored the challenges and considerations in using ECM, particularly urging caution with methods that combine positive and negative correlations.:1 - Personal Motivation 2 - Introduction 3 - Background 3.1 Cognitive Neuroscience 3.2 Predictive Coding and the Free Energy Principle 3.3 Functional Magnetic Resonance Imaging (fMRI) 3.4 Blood-Oxygen Level Dependent (BOLD) Signal 3.5 Resting-State Functional Connectivity (RSFC) 3.6 Eigenvector Centrality Mapping (ECM) 3.7 Previous ECM Experiments - an Overview 3.8 Statistical Remarks 4 - Materials and Methods 4.1 Experimental Setup 4.1.1 Subjects 4.1.2 Experimental Procedures 4.1.2.1 Electrical stimulation 4.1.2.2 Absolute detection threshold examination 4.1.2.3 fMRI and behavioral data acquisition 4.2 fMRI Data Preprocessing 4.2.1 Prior Steps 4.2.2 Slice Time Correction 4.2.3 Motion Correction 4.2.4 Coregistration, Segmentation and Normalization 4.2.5 Spatial Filtering 4.2.6 Temporal Filtering 4.2.7 Grey Matter, White Matter and Cerebrospinal Fluid Masking 4.2.8 Nuisance Regression 4.3 ECM Approaches 4.4 Flexible Factorial Design 4.5 Seed-Based Functional Connectivity Analysis 5 - Results 5.1 Experiment 1 5.1.1 Detailed Results 5.1.2 Summary Experiment 1 5.2 Experiment 2 - fMRI Session 5.2.1 Detailed Results - ADD Approach 5.2.2 Detailed REsults - POS / NEG / ABS approach 5.2.3 Summary Experiment 2 5.3 ECM Approach Differences Illustrated by Examples 5.4 Seed-Based Functional Connectivity Analysis 5.5 Distribution of Voxel Timie Series Correlation Foefficients 5.6 Experiment 2 - Behavioral Session 6 - Discussion 6.1 Interpretation of Experimental Results 6.1.1 Experiment 1 6.1.2 Experimemnt 2 - fMRI Session 6.1.3 Experiment 2 - Behavioral Session 6.2 ECM Approaches 6.3 Further Considerations and Future Outlook 7 - Conclusion

Resting-state fMRI (rsfMRI) has been widely used to map intrinsic brain network organization of the human brain both in health and in pathological conditions. However, the neural underpinnings and dynamic rules governing brain-wide rsfMRI coupling remain unclear. Filling this knowledge gap is of crucial importance, given our current inability to decode and reverse-engineer clinical signatures of aberrant connectivity into interpretable neurophysiological events that can help understand or diagnose brain disorders. Toward this goal, here we combined chemogenetics, rsfMRI, and in vivo electrophysiology in the mouse to investigate how regional manipulations of brain activity (i.e. neural inhibition, or excitation) causally contribute to whole-brain fMRI network organization. In a first set of proof of concept investigations, we empirically probed the widely held notion that neural inhibition of a cortical node would result in reduced fMRI coupling of the silenced area and its long-range terminals. Surprisingly, we found that chronic inhibition of the mouse medial prefrontal cortex (PFC) via viral overexpression of a potassium channel paradoxically increased fMRI connectivity between the inhibited area and its direct thalamo-cortical targets. Notably, acute chemogenetic inhibition of the PFC reproduced analogous patterns of fMRI overconnectivity. Using in vivo electrophysiology, we found that chemogenetic inhibition of the PFC enhances low frequency (0.1 - 4 Hz) oscillatory power via suppression of neural firing not phase-locked to slow rhythms, resulting in increased slow and δ band coherence between areas that exhibit fMRI overconnectivity. These results provide causal evidence that cortical inactivation can counterintuitively increase fMRI connectivity via enhanced, less-localized slow oscillatory processes, with important implications for neural modeling and interpretation of fMRI overconnectivity in brain disorders. Importantly, our observation that neural inhibition of the PFC results in fMRI overconnectivity allowed us to predict that neural activation of the same area might produce opposite results, i.e. fMRI underconnectivity and neural desynchronization. To test this hypothesis, we used chemogenetics to increase local excitatory-inhibitory (E/I) balance in the PFC. As predicted, chemogenetic stimulation of CamkII-expressing neurons, or inhibition of fast-spiking parvalbumin-expressing neurons, produced similar rsfMRI signatures of rsfMRI underconnectivity. Both manipulations produced analogous electrophysiological signatures characterized by increased firing activity and a robust LFP power shift towards higher (i.e. γ) frequencies, effectively reversing the corresponding neural signature observed in DREADD inhibition studies. Importantly, the same E/I affecting perturbations were also associated with socio-communicative deficits in behaving mice hence underscoring the behavioral relevance of the employed manipulations. These results show that excitatory/inhibitory balance critically biases brain-wide fMRI coupling, pointing at a possible unifying mechanistic link between E/I imbalance and rsfMRI connectivity disruption in developmental disorders. More broadly, these investigations reveal a set of fundamental rules linking regional brain activity to macroscale functional connectivity, offering opportunities to physiologically interpret rsfMRI signatures of functional dysconnectivity in human brain disorders.

Children and adolescents with concussion display aberrant functional connectivity in some of the major neurocognitive networks. This includes the Default Mode Network, Central Executive Network and Salience Network. Using resting state fMRI, the purpose of this thesis was to explore the functional connectivity of cognition-related networks in youth experiencing concussion. With a prospective cohort study, the functional connectivity (defined as the temporal coherence between spatially separated brain regions) of children and adolescents ages 10-18 years old was evaluated in relation to a number of demographic and injury-specific factors including recovery length, age at the time of injury, symptom severity, and neurocognitive performance. The results showed two general trends: (1) a reduction in connectivity (i.e., hypoconnectivity) between the regions of the Default Mode Network, and (2) an increase in connectivity (i.e., hyperconnectivity) between additional sensory-related regions like the cerebellum and hippocampus. The Default Mode Network, which processes self-referential information, has a long-protracted development across childhood through adulthood. Given that the participants in this cohort exhibited reduced functional connectivity within the Default Mode Network and between the Default Mode Network and other neurocognitive networks suggests that this is an area of vulnerability in youth in the event of concussion. Increased connectivity between the Central Executive Network and Salience Network, and between cognitive- and sensory-related regions such as the hippocampus and cerebellum might be interpreted as a compensatory mechanism to supplement deficits of the Default Mode Network. This thesis sheds light on important concussion-related regions for future research to investigate further and delves into the possible neural mechanisms contributing to the cognitive, sensory, mood, and sleep disturbances in children and adolescents with concussion. / Dissertation / Doctor of Philosophy (PhD) / Your brain at rest is not resting. In fact, your many brain regions are continuously communicating even during rest to maintain important communication between them. This communication between brain regions is termed functional connectivity. When you receive a blow to the head, face, neck, or another part of your body that senses a biomechanical force to your brain, the functional connectivity (i.e., communication lines) between your brain regions may be altered. A blow of this nature is considered a concussion, also known as a mild traumatic brain injury. With disruptions to the typical functional connectivity between your brain regions following a concussion, you may experience difficulty in managing cognitive tasks, emotions, and body coordination. Among those most vulnerable to the effects of concussion are children and adolescents whose brains have yet to develop fully. The goal of this thesis was to evaluate the functional connectivity between brain regions of children and adolescents to determine how brain communication might be disrupted following concussion. These evaluations were done using functional magnetic resonance imaging (fMRI) of the brains of children and adolescents ages 10-18 years old. It was discovered that the functional connectivity of the frontal lobe is related severity of post-concussion symptoms such that individuals with worse symptoms had reduced functional connectivity in the frontal lobe compared to individuals who reported less severe symptoms. Further, children and adolescents with longer recovery periods have a different level of functional connectivity in the temporal lobe compared to youth with relatively shorter recovery periods. This might suggest that both of these regions could provide prognostic value in determining who might have worse symptoms or a longer recovery time following injury. In comparison to children and adolescents who have not had a concussion, children and adolescents experiencing a concussion are more likely to have abnormal functional connectivity between the hippocampus and cerebellum, which are particularly involved in processing sensory information and navigation. This was interpreted to mean that the brain responded to the concussion by increasing the communication between regions that might help a child with a concussion coordinate their bodies so that they can move from place to place. This was additionally supported by a further investigation which showed that children and adolescents have reduced communication between areas of the brain that might allow them to process information about the self (e.g., memories, sensations, relationships with others, etc.). Overall, the results demonstrated that following a concussion, children and adolescents may have a deficit in the functioning of the frontal lobe in a specific region that allows them to process cognitive and sensory information. This might explain why concussion leads to poor memory, body coordination, sensitivity to light and sounds, and even difficulty sleeping. Their brains might then compensate for the disruption by increasing alternate pathways of communication. Together these findings open gateways for future researchers to look more deeply at the specific regions affected by concussion in youth. It draws attention to the many neurocognitive, emotional, and somatic symptoms a child with a concussion exhibits and their symptoms’ underlying neurological processes.

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Post-traumatic stress disorder (PTSD) is a psychiatric disorder that is caused by exposure to traumatic or stressful events in life. 3,4-Methylenedioxymethamphetamine (MDMA) has been shown to be an effective agent in drug-assisted psychotherapy for PTSD. In this systematic review, we aim to evaluate the effect MDMA has on functional connectivity in healthy individuals and individuals with PTSD and investigate the potential mechanisms via which MDMA exerts its effects in MDMA-assisted psychotherapy for PTSD patients. A total of 134 articles from Web of Science and Medline EBSCO were screened and 5 articles relevant for the systematic review were identified. After MDMA administration, an increase and decrease in functional connectivity in multiple brain areas and networks was observed, such as the thalamus, visual cortex, anterior cingulate cortex, hippocampus, amygdala, prefrontal cortex, default mode network, cerebellar network, sensorimotor network, salience network, and executive network. Notably, MDMA increased amygdala-hippocampal functional connectivity which may link to improved emotion regulation and fear extinction in patients receiving MDMA-assisted therapy. The findings evidence the complex effects of MDMA on brain connectivity and highlight the need for further research in this area, although MDMA-assisted therapy does prove to be a promising alternative for treating PTSD.