Normalized Cut Group Clustering of Resting-State fMRI Data

BACKGROUND: Functional brain imaging studies have indicated that distinct anatomical brain regions can show coherent spontaneous neuronal activity during rest. Regions that show such correlated behavior are said to form resting-state networks (RSNs). RSNs have been investigated using seed-dependent functional connectivity maps and by using a number of model-free methods. However, examining RSNs across a group of subjects is still a complex task and often involves human input in selecting meaningful networks. METHODOLOGY/PRINCIPAL FINDINGS: We report on a voxel based model-free normalized cut graph clustering approach with whole brain coverage for group analysis of resting-state data, in which the number of RSNs is computed as an optimal clustering fit of the data. Inter-voxel correlations of time-series are grouped at the individual level and the consistency of the resulting networks across subjects is clustered at the group level, defining the group RSNs. We scanned a group of 26 subjects at rest with a fast BOLD sensitive fMRI scanning protocol on a 3 Tesla MR scanner. CONCLUSIONS/SIGNIFICANCE: An optimal group clustering fit revealed 7 RSNs. The 7 RSNs included motor/visual, auditory and attention networks and the frequently reported default mode network. The found RSNs showed large overlap with recently reported resting-state results and support the idea of the formation of spatially distinct RSNs during rest in the human brain

Active Selection of Classification Features

Some data analysis applications comprise datasets, where explanatory variables are expensive or tedious to acquire, but auxiliary data are readily available and might help to construct an insightful training set. An example is neuroimaging research on mental disorders, specifically learning a diagnosis/prognosis model based on variables derived from expensive Magnetic Resonance Imaging (MRI) scans, which often requires large sample sizes. Auxiliary data, such as demographics, might help in selecting a smaller sample that comprises the individuals with the most informative MRI scans. In active learning literature, this problem has not yet been studied, despite promising results in related problem settings that concern the selection of instances or instance-feature pairs. Therefore, we formulate this complementary problem of Active Selection of Classification Features (ASCF): Given a primary task, which requires to learn a model f: x-> y to explain/predict the relationship between an expensive-to-acquire set of variables x and a class label y. Then, the ASCF-task is to use a set of readily available selection variables z to select these instances, that will improve the primary task's performance most when acquiring their expensive features z and including them to the primary training set. We propose two utility-based approaches for this problem, and evaluate their performance on three public real-world benchmark datasets. In addition, we illustrate the use of these approaches to efficiently acquire MRI scans in the context of neuroimaging research on mental disorders, based on a simulated study design with real MRI data.Comment: Accepted for publication at the 19th Intelligent Data Analysis Symposium, 2021. The final authenticated publication will be made available online at springer.co

Human Fronto-Tectal and Fronto-Striatal-Tectal Pathways Activate Differently During Anti-Saccades

Almost all cortical areas in the vertebrate brain take part in recurrent connections through the subcortical basal ganglia (BG) nuclei, through parallel inhibitory and excitatory loops. It has been suggested that these circuits can modulate our reactions to external events such that appropriate reactions are chosen from many available options, thereby imposing volitional control over behavior. The saccade system is an excellent model system to study cortico-BG interactions. In this study two possible pathways were investigated that might regulate automaticity of eye movements in the human brain; the cortico-tectal pathway, running directly between the frontal eye fields (FEF) and superior colliculus (SC) and the cortico-striatal pathway from the FEF to the SC involving the caudate nucleus (CN) in the BG. In an event-related functional magnetic resonance imaging (fMRI) paradigm participants made pro- and anti-saccades. A diffusion tensor imaging (DTI) scan was made for reconstruction of white matter tracts between the FEF, CN and SC. DTI fiber tracts were used to divide both the left and right FEF into two sub-areas, projecting to either ipsilateral SC or CN. For each of these FEF zones an event-related fMRI timecourse was extracted. In general activity in the FEF was larger for anti-saccades. This increase in activity was lateralized with respect to anti-saccade direction in FEF zones connected to the SC but not for zones only connected to the CN. These findings suggest that activity along the contralateral FEF–SC projection is responsible for directly generating anti-saccades, whereas the pathway through the BG might merely have a gating function withholding or allowing a pro-saccade

Association between gut permeability, brain volume, and cognition in healthy participants and patients with schizophrenia spectrum disorder

INTRODUCTION: The barrier function of the gut is important for many organs and systems, including the brain. If gut permeability increases, bacterial fragments may enter the circulation, giving rise to increased systemic inflammation. Increases in bacterial translocation are reflected in higher values of blood markers, including lipopolysaccharide binding protein (LBP) and soluble cluster of differentiation 14 (sCD14). Some pioneer studies showed a negative association between bacterial translocation markers and brain volumes, but this association remains scarcely investigated. We investigate the effect of bacterial translocation on brain volumes and cognition in both healthy controls and patients with a schizophrenia spectrum disorder (SSD).MATERIALS AND METHODS: Healthy controls (n = 39) and SSD patients (n = 72) underwent an MRI-scan, venipuncture and cognition assessments. We investigated associations between LBP and sCD14 and brain volumes (intracranial volume, total brain volume, and hippocampal volume) using linear regression. We then associated LBP and sCD14 to cognitive function using a mediation analysis, with intracranial volume as mediator.RESULTS: Healthy controls showed a negative association between hippocampal volume and LBP (b = -0.11, p = .04), and intracranial volume and sCD14 (b = -0.25, p = .07). Both markers were indirectly associated with lower cognitive functioning in healthy controls (LBP: b = -0.071, p = .028; sCD14: b = -0.213, p = .052), mediated by low intracranial volume. In the SSD patients, these associations were markedly less present.CONCLUSION: These findings extend earlier studies suggesting that increased bacterial translocation may negatively affect brain volume, which indirectly impacts cognition, even in this young healthy group. If replicated, this finding stresses the importance of a healthy gut for the development and optimal functioning of the brain. Absence of these associations in the SSD group may indicate that other factors such as allostatic load, chronic medication use and interrupted educational carrier had larger impact and attenuated the relative contribution of bacterial translocation.</p

Impaired Structural Motor Connectome in Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a severe neurodegenerative disease selectively affecting upper and lower motor neurons. Patients with ALS suffer from progressive paralysis and eventually die on average after three years. The underlying neurobiology of upper motor neuron degeneration and its effects on the complex network of the brain are, however, largely unknown. Here, we examined the effects of ALS on the structural brain network topology in 35 patients with ALS and 19 healthy controls. Using diffusion tensor imaging (DTI), the brain network was reconstructed for each individual participant. The connectivity of this reconstructed brain network was compared between patients and controls using complexity theory without - a priori selected - regions of interest. Patients with ALS showed an impaired sub-network of regions with reduced white matter connectivity (p = 0.0108, permutation testing). This impaired sub-network was strongly centered around primary motor regions (bilateral precentral gyrus and right paracentral lobule), including secondary motor regions (bilateral caudal middle frontal gyrus and pallidum) as well as high-order hub regions (right posterior cingulate and precuneus). In addition, we found a significant reduction in overall efficiency (p = 0.0095) and clustering (p = 0.0415). From our findings, we conclude that upper motor neuron degeneration in ALS affects both primary motor connections as well as secondary motor connections, together composing an impaired sub-network. The degenerative process in ALS was found to be widespread, but interlinked and targeted to the motor connectome

Neuroimaging auditory verbal hallucinations in schizophrenia patient and healthy populations

BACKGROUND: Auditory verbal hallucinations (AVH) are a cardinal feature of schizophrenia, but they can also appear in otherwise healthy individuals. Imaging studies implicate language networks in the generation of AVH; however, it remains unclear if alterations reflect biologic substrates of AVH, irrespective of diagnostic status, age, or illness-related factors. We applied multimodal imaging to identify AVH-specific pathology, evidenced by overlapping gray or white matter deficits between schizophrenia patients and healthy voice-hearers. METHODS: Diffusion-weighted and T1-weighted magnetic resonance images were acquired in 35 schizophrenia patients with AVH (SCZ-AVH), 32 healthy voice-hearers (H-AVH), and 40 age- and sex-matched controls without AVH. White matter fractional anisotropy (FA) and gray matter thickness (GMT) were computed for each region comprising ICBM-DTI and Desikan-Killiany atlases, respectively. Regions were tested for significant alterations affecting both SCZ-AVH and H-AVH groups, relative to controls. RESULTS: Compared with controls, the SCZ-AVH showed widespread FA and GMT reductions; but no significant differences emerged between H-AVH and control groups. While no overlapping pathology appeared in the overall study groups, younger (<40 years) H-AVH and SCZ-AVH subjects displayed overlapping FA deficits across four regions (p < 0.05): the genu and splenium of the corpus callosum, as well as the anterior limbs of the internal capsule. Analyzing these regions with free-water imaging ascribed overlapping FA abnormalities to tissue-specific anisotropy changes. CONCLUSIONS: We identified white matter pathology associated with the presence of AVH, independent of diagnostic status. However, commonalities were constrained to younger and more homogenous groups, after reducing pathologic variance associated with advancing age and chronicity effects

Shape and volume changes of the superior lateral ventricle after electroconvulsive therapy measured with ultra-high field MRI

The subventricular zone (SVZ) of the lateral ventricles harbors neuronal stem cells in adult mammals. Rodent studies report neurogenic effects in the SVZ of electroconvulsive stimulation. We hypothesize that if this finding translates to depressed patients undergoing electroconvulsive therapy (ECT), this would be reflected in shape changes at the SVZ. Using T1-weighted MR images acquired at ultra-high field strength (7T), the shape and volume of the ventricles were compared from pre to post ECT after 10 ECT sessions (in patients twice weekly) or 5 weeks apart (controls) using linear mixed models with age and gender as covariates. Ventricle shape significantly changed and volume significantly decreased over time in patients for the left ventricle, but not in controls. The decrease in volume of the ventricles was associated to a decrease in depression scores, and an increase in the left dentate gyrus, However, the shape changes of the ventricles were not restricted to the neurogenic niche in the lateral walls of the ventricles, providing no clear evidence for neurogenesis as sole explanation of volume changes in the ventricles after ECT

Functional brain networks in the schizophrenia spectrum and bipolar disorder with psychosis

Psychotic experiences have been proposed to lie on a spectrum, ranging from subclinical experiences to treatment-resistant schizophrenia. We aimed to characterize functional connectivity and brain network characteristics in relation to the schizophrenia spectrum and bipolar disorder with psychosis to disentangle neural correlates to psychosis. Additionally, we studied antipsychotic medication and lithium effects on network characteristics. We analyzed functional connectivity strength and network topology in 487 resting-state functional MRI scans of individuals with schizophrenia spectrum disorder (SCZ), bipolar disorder with a history of psychotic experiences (BD), treatment-naïve subclinical psychosis (SCP), and healthy controls (HC). Since differences in connectivity strength may confound group comparisons of brain network topology, we analyzed characteristics of the minimum spanning tree (MST), a relatively unbiased backbone of the network. SCZ and SCP subjects had a lower connectivity strength than BD and HC individuals but showed no differences in network topology. In contrast, BD patients showed a less integrated network topology but no disturbances in connectivity strength. No differences in outcome measures were found between SCP and SCZ, or between BD patients that used antipsychotic medication or lithium and those that did not. We conclude that functional networks in patients prone to psychosis have different signatures for chronic SCZ patients and SCP compared to euthymic BD patients, with a limited role for medication. Connectivity strength effects may have confounded previous studies, as no functional network alterations were found in SCZ after strict correction for connectivity strength.</p

Multi-site genetic analysis of diffusion images and voxelwise heritability analysis : a pilot project of the ENIGMA–DTI working group

The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Consortium was set up to analyze brain measures and genotypes from multiple sites across the world to improve the power to detect genetic variants that influence the brain. Diffusion tensor imaging (DTI) yields quantitative measures sensitive to brain development and degeneration, and some common genetic variants may be associated with white matter integrity or connectivity. DTI measures, such as the fractional anisotropy (FA) of water diffusion, may be useful for identifying genetic variants that influence brain microstructure. However, genome-wide association studies (GWAS) require large populations to obtain sufficient power to detect and replicate significant effects, motivating a multi-site consortium effort. As part of an ENIGMA–DTI working group, we analyzed high-resolution FA images from multiple imaging sites across North America, Australia, and Europe, to address the challenge of harmonizing imaging data collected at multiple sites. Four hundred images of healthy adults aged 18–85 from four sites were used to create a template and corresponding skeletonized FA image as a common reference space. Using twin and pedigree samples of different ethnicities, we used our common template to evaluate the heritability of tract-derived FA measures. We show that our template is reliable for integrating multiple datasets by combining results through meta-analysis and unifying the data through exploratory mega-analyses. Our results may help prioritize regions of the FA map that are consistently influenced by additive genetic factors for future genetic discovery studies. Protocols and templates are publicly available at (http://enigma.loni.ucla.edu/ongoing/dti-working-group/)