NEW APPROACHES FOR ESTIMATING HEMISPHERIC LATERALIZATION FROM RESTING STATE FMRI DATA WITH RELATIONSHIP TO AGE, GENDER AND MENTAL DISORDERS

Lateralization is specialization of the brain hemispheres in certain tasks, such as language, mathematics, cognition and motor skills. It is one of the most queried topics related to the human brain. After the invention of modern medical imaging techniques including functional magnetic resonance imaging (fMRI), scientific research about the human brain, including lateralization, gained huge momentum. There have been a remarkable numbers of studies about lateralization and most of these studies focused on investigating which part of the brain dominates in which tasks. However, there have been very few lateralization studies on brain intrinsic activity, i.e., resting state activity where subjects are asked to stay awake while resting without performing any specific tasks. Independent component analysis (ICA), a data-driven blind source separation method, has become one of the conventional data analysis tools for brain imaging data. ICA can separate the brain imaging data into functional regions that are temporally coherent, and functional network connectivity (FNC) of these regions can be computed. FNC is a measure that captures the temporal covariance of the brain networks. In this dissertation, we focus on the lateralization during the resting state and assess hemispheric differences during the resting state. The lateralization of the resting state networks and their association with age and gender is presented using a large resting state fMRI dataset. A novel approach for generating hemisphere specific time-courses and computing FNC inside the hemispheres and between hemispheres is proposed and the relationship of these FNC values with age, gender and mental illness, schizophrenia is reported. Finally, a new framework to estimate power spectral density of 4D brain imaging data and a dimension reduction method to reduce dimensionality from 4D frequency domain to 2D frequency domain has been proposed. This framework helps us to reveal spatiotemporal organization differences between hemispheres. In summary, our work has made several contributions to advance lateralization analysis and has improved our understanding of various aspects of hemispheric differences during the resting stat

Time-Varying Spatial Propagation of Brain Networks in fMRI Data.

Spontaneous neural activity coherently relays information across the brain. Several efforts have been made to understand how spontaneous neural activity evolves at the macro-scale level as measured by resting-state functional magnetic resonance imaging (rsfMRI). Previous studies observe the global patterns and flow of information in rsfMRI using methods such as sliding window or temporal lags. However, to our knowledge, no studies have examined spatial propagation patterns evolving with time across multiple overlapping 4D networks. Here, we propose a novel approach to study how dynamic states of the brain networks spatially propagate and evaluate whether these propagating states contain information relevant to mental illness. We implement a lagged windowed correlation approach to capture voxel-wise network-specific spatial propagation patterns in dynamic states. Results show systematic spatial state changes over time, which we confirmed are replicable across multiple scan sessions using human connectome project data. We observe networks varying in propagation speed; for example, the default mode network (DMN) propagates slowly and remains positively correlated with blood oxygenation level-dependent (BOLD) signal for 6-8 s, whereas the visual network propagates much quicker. We also show that summaries of network-specific propagative patterns are linked to schizophrenia. More specifically, we find significant group differences in multiple dynamic parameters between patients with schizophrenia and controls within four large-scale networks: default mode, temporal lobe, subcortical, and visual network. Individuals with schizophrenia spend more time in certain propagating states. In summary, this study introduces a promising general approach to exploring the spatial propagation in dynamic states of brain networks and their associated complexity and reveals novel insights into the neurobiology of schizophrenia

Exploring the longitudinal associations of functional network connectivity and psychiatric symptom changes in youth

Background: Functional connectivity has been associated with psychiatric problems, both in children and adults, but inconsistencies are present across studies. Prior research has mostly focused on small clinical samples with cross-sectional designs. Methods: We adopted a longitudinal design with repeated assessments to investigate associations between functional network connectivity (FNC) and psychiatric problems in youth (9- to 17-year-olds, two time points) from the general population. The largest single-site study of pediatric neurodevelopment was used: Generation R (N = 3,131 with data at either time point). Psychiatric symptoms were measured with the Child Behavioral Checklist as broadband internalizing and externalizing problems, and its eight specific syndrome scales (e.g., anxious-depressed). FNC was assessed with two complementary approaches. First, static FNC (sFNC) was measured with graph theory-based metrics. Second, dynamic FNC (dFNC), where connectivity is allowed to vary over time, was summarized into 5 states that participants spent time in. Cross-lagged panel models were used to investigate the longitudinal bidirectional relationships of sFNC with internalizing and externalizing problems. Similar cross-lagged panel models were run for dFNC. Results: Small longitudinal relationships between dFNC and certain syndrome scales were observed, especially for baseline syndrome scales (i.e., rule-breaking, somatic complaints, thought problems, and attention problems) predicting connectivity changes. However, no association between any of the psychiatric problems (broadband and syndrome scales) with either measure of FNC survived correction for multiple testing. Conclusion: We found no or very modest evidence for longitudinal associations between psychiatric problems with dynamic and static FNC in this population-based sample. Differences in findings may stem from the population drawn, study design, developmental timing, and sample sizes.</p

Developmental Changes in Dynamic Functional Connectivity From Childhood Into Adolescence

The longitudinal study of typical neurodevelopment is key for understanding deviations due to specific factors, such as psychopathology. However, research utilizing repeated measurements remains scarce. Resting-state functional magnetic resonance imaging (MRI) studies have traditionally examined connectivity as ‘static’ during the measurement period. In contrast, dynamic approaches offer a more comprehensive representation of functional connectivity by allowing for different connectivity configurations (time varying connectivity) throughout the scanning session. Our objective was to characterize the longitudinal developmental changes in dynamic functional connectivity in a population-based pediatric sample. Resting-state MRI data were acquired at the ages of 10 (range 8-to-12, n = 3,327) and 14 (range 13-to-15, n = 2,404) years old using a single, study-dedicated 3 Tesla scanner. A fully-automated spatially constrained group-independent component analysis (ICA) was applied to decompose multi-subject resting-state data into functionally homogeneous regions. Dynamic functional network connectivity (FNC) between all ICA time courses were computed using a tapered sliding window approach. We used a k-means algorithm to cluster the resulting dynamic FNC windows from each scan session into five dynamic states. We examined age and sex associations using linear mixed-effects models. First, independent from the dynamic states, we found a general increase in the temporal variability of the connections between intrinsic connectivity networks with increasing age. Second, when examining the clusters of dynamic FNC windows, we observed that the time spent in less modularized states, with low intra- and inter-network connectivity, decreased with age. Third, the number of transitions between states also decreased with age. Finally, compared to boys, girls showed a more mature pattern of dynamic brain connectivity, indicated by more time spent in a highly modularized state, less time spent in specific states that are frequently observed at a younger age, and a lower number of transitions between states. This longitudinal population-based study demonstrates age-related maturation in dynamic intrinsic neural activity from childhood into adolescence and offers a meaningful baseline for comparison with deviations from typical development. Given that several behavioral and cognitive processes also show marked changes through childhood and adolescence, dynamic functional connectivity should also be explored as a potential neurobiological determinant of such changes

A method for capturing dynamic spectral coupling in resting fMRI reveals domain-specific patterns in schizophrenia

IntroductionResting-state functional magnetic resonance imaging (rs-fMRI) is a powerful tool for assessing functional brain connectivity. Recent studies have focused on shorter-term connectivity and dynamics in the resting state. However, most of the prior work evaluates changes in time-series correlations. In this study, we propose a framework that focuses on time-resolved spectral coupling (assessed via the correlation between power spectra of the windowed time courses) among different brain circuits determined via independent component analysis (ICA).MethodsMotivated by earlier work suggesting significant spectral differences in people with schizophrenia, we developed an approach to evaluate time-resolved spectral coupling (trSC). To do this, we first calculated the correlation between the power spectra of windowed time-courses pairs of brain components. Then, we subgrouped each correlation map into four subgroups based on the connectivity strength utilizing quartiles and clustering techniques. Lastly, we examined clinical group differences by regression analysis for each averaged count and average cluster size matrices in each quartile. We evaluated the method by applying it to resting-state data collected from 151 (114 males, 37 females) people with schizophrenia (SZ) and 163 (117 males, 46 females) healthy controls (HC).ResultsOur proposed approach enables us to observe the change of connectivity strength within each quartile for different subgroups. People with schizophrenia showed highly modularized and significant differences in multiple network domains, whereas males and females showed less modular differences. Both cell count and average cluster size analysis for subgroups indicate a higher connectivity rate in the fourth quartile for the visual network in the control group. This indicates increased trSC in visual networks in the controls. In other words, this shows that the visual networks in people with schizophrenia have less mutually consistent spectra. It is also the case that the visual networks are less spectrally correlated on short timescales with networks of all other functional domains.ConclusionsThe results of this study reveal significant differences in the degree to which spectral power profiles are coupled over time. Importantly, there are significant but distinct differences both between males and females and between people with schizophrenia and controls. We observed a more significant coupling rate in the visual network for the healthy controls and males in the upper quartile. Fluctuations over time are complex, and focusing on only time-resolved coupling among time-courses is likely to miss important information. Also, people with schizophrenia are known to have impairments in visual processing but the underlying reasons for the impairment are still unknown. Therefore, the trSC approach can be a useful tool to explore the reasons for the impairments

Multidimensional Frequency Domain Analysis of Full-Volume fMRI Reveals Significant Effects of Age, Gender, and Mental Illness on the Spatiotemporal Organization of Resting-State Brain Activity

Clinical research employing functional magnetic resonance imaging (fMRI) is often conducted within the connectionist paradigm, focusing on patterns of connectivity between voxels, regions of interest (ROIs) or spatially distributed functional networks. Connectivity-based analyses are concerned with pairwise correlations of the temporal activation associated with restrictions of the whole-brain hemodynamic signal to locations of a priori interest. There is a more abstract question however that such spatially granular correlation-based approaches do not elucidate: Are the broad spatiotemporal organizing principles of brains in certain populations distinguishable from those of others? Global patterns (in space and time) of hemodynamic activation are rarely scrutinized for features that might characterize complex psychiatric conditions, aging effects or gender—among other variables of potential interest to researchers. We introduce a canonical, transparent technique for characterizing the role in overall brain activation of spatially scaled periodic patterns with given temporal recurrence rates. A core feature of our technique is the spatiotemporal spectral profile (STSP), a readily interpretable 2D reduction of the native four-dimensional brain × time frequency domain that is still “big enough” to capture important group differences in globally patterned brain activation. Its power to distinguish populations of interest is demonstrated on a large balanced multi-site resting fMRI dataset with nearly equal numbers of schizophrenia patients and healthy controls. Our analysis reveals striking differences in the spatiotemporal organization of brain activity that correlate with the presence of diagnosed schizophrenia, as well as with gender and age. To the best of our knowledge, this is the first demonstration that a 4D frequency domain analysis of full volume fMRI data exposes clinically or demographically relevant differences in resting-state brain function

Local thrombolytic therapy in acute mesenteric ischemia

BACKGROUND: The aim of the study was to evaluate the local thrombolytic therapy (LTT) in combination with laparoscopy, in management of acute mesenteric ischemia (AMI). METHODS: From January 2000 to January 2010, patients who were admitted to the hospital with AMI due to acute arterial occlusion were analysed retrospectively. Patients presenting with acute abdomen with a suspicion of AMI were evaluated with computerized tomography angiography (CTA). Patients who had findigs of AMI on CTA, were underwent selective mesenteric angiography and LTT eventhough without peritoneal signs. LTT was carried out before or after laparoscopy or laparotomy, and initiated with recombinant plasminogen activator. RESULTS: LTT was performed in 13 (17.1%), out of 76 patients. From the remaining patients, 56 underwent necrotic bowel resection and 7 underwent tromboembolectomy. The median age was 62 years (45–87). The median duration of symptoms was 24 h. Four (30.7%) patients presented within 24 h onset of symptoms, whilst 9 (69.3%) patients presented after 24 h onset of symptoms. There were 5 (39.5%) patients, who presented with abdominal pain without peritoneal signs on physical examination and 8 (61.5%) patients, who had peritoneal signs. The mortality rate was 20% (1/5) in the first group who presented without peritoneal signs, whilst it was 62.5% (5/8) in the remaining. CONCLUSION: Early intervention in AMI is the key to better results. CTA combined with early laparoscopy and LTT may have beneficial effects at this setting

Spatial Dynamic Subspaces Encode Sex-Specific Schizophrenia Disruptions in Transient Network Overlap and its Links to Genetic Risk

BACKGROUND: Recent advances in resting-state fMRI allow us to study spatial dynamics, the phenomenon of brain networks spatially evolving over time. However, most dynamic studies still use subject-specific, spatially-static nodes. As recent studies have demonstrated, incorporating time-resolved spatial properties is crucial for precise functional connectivity estimation and gaining unique insights into brain function. Nevertheless, estimating time-resolved networks poses challenges due to the low signal-to-noise ratio, limited information in short time segments, and uncertain identification of corresponding networks within and between subjects. METHODS: We adapt a reference-informed network estimation technique to capture time-resolved spatial networks and their dynamic spatial integration and segregation. We focus on time-resolved spatial functional network connectivity (spFNC), an estimate of network spatial coupling, to study sex-specific alterations in schizophrenia and their links to multi-factorial genomic data. RESULTS: Our findings are consistent with the dysconnectivity and neurodevelopment hypotheses and align with the cerebello-thalamo-cortical, triple-network, and frontoparietal dysconnectivity models, helping to unify them. The potential unification offers a new understanding of the underlying mechanisms. Notably, the posterior default mode/salience spFNC exhibits sex-specific schizophrenia alteration during the state with the highest global network integration and correlates with genetic risk for schizophrenia. This dysfunction is also reflected in high-dimensional (voxel-level) space in regions with weak functional connectivity to corresponding networks. CONCLUSIONS: Our method can effectively capture spatially dynamic networks, detect nuanced SZ effects, and reveal the intricate relationship of dynamic information to genomic data. The results also underscore the potential of dynamic spatial dependence and weak connectivity in the clinical landscape

Factors That Affect the False-Negative Outcomes of Fine-Needle Aspiration Biopsy in Thyroid Nodules

Background. The purpose of this study was to assess the factors that affect the false-negative outcomes of fine-needle aspiration biopsies (FNABs) in thyroid nodules. Methods. Thyroid nodules that underwent FNAB and surgery between August 2005 and January 2012 were analyzed. FNABs were taken from the suspicious nodules regardless of nodule size. Results. Nodules were analyzed in 2 different groups: Group 1 was the false-negatives (n=81) and Group 2 was the remaining true-positives, true-negatives, and false-positives (n=649). A cytopathologist attended in 559 (77%) of FNAB procedures. There was a positive correlation between the nodule size and false-negative rates, and the absence of an interpreting cytopathologist for the examination of the FNAB procedure was the most significant parameter with a 76-fold increased risk of false-negative results. Conclusion. The contribution of cytopathologists extends the time of the procedure, and this could be a difficult practice in centres with high patient turnovers. We currently request the contribution of a cytopathologist for selected patients whom should be followed up without surgery

Association of mechanical bowel preparation with oral antibiotics and anastomotic leak following left sided colorectal resection:an international, multi-centre, prospective audit

Introduction: The optimal bowel preparation strategy to minimise the risk of anastomotic leak is yet to be determined. This study aimed to determine whether oral antibiotics combined with mechanical bowel preparation (MBP+Abx) was associated with a reduced risk of anastomotic leak when compared to mechanical bowel preparation alone (MBP) or no bowel preparation (NBP). Methods: A pre-planned analysis of the European Society of Coloproctology (ESCP) 2017 Left Sided Colorectal Resection audit was performed. Patients undergoing elective left sided colonic or rectal resection with primary anastomosis between 1 January 2017 and 15 March 2017 by any operative approach were included. The primary outcome measure was anastomotic leak. Results: Of 3676 patients across 343 centres in 47 countries, 618 (16.8%) received MBP+ABx, 1945 MBP (52.9%) and 1099 patients NBP (29.9%). Patients undergoing MBP+ABx had the lowest overall rate of anastomotic leak (6.1%, 9.2%, 8.7% respectively) in unadjusted analysis. After case-mix adjustment using a mixed-effects multivariable regression model, MBP+Abx was associated with a lower risk of anastomotic leak (OR 0.52, 0.30–0.92, P&nbsp;=&nbsp;0.02) but MBP was not (OR 0.92, 0.63–1.36, P&nbsp;=&nbsp;0.69) compared to NBP. Conclusion: This non-randomised study adds ‘real-world’, contemporaneous, and prospective evidence of the beneficial effects of combined mechanical bowel preparation and oral antibiotics in the prevention of anastomotic leak following left sided colorectal resection across diverse settings. We have also demonstrated limited uptake of this strategy in current international colorectal practice