•We evaluate three parameter estimation methods for connectome generative models.•The three methods show a tradeoff between accuracy, reliability, and computational expense.•We develop a new fast, ...accurate and reliable estimation method.•We report minimum sample sizes required to detect between-group differences in model parameters.
Generative models of the human connectome enable in silico generation of brain networks based on probabilistic wiring rules. These wiring rules are governed by a small number of parameters that are typically fitted to individual connectomes and quantify the extent to which geometry and topology shape the generative process. A significant shortcoming of generative modeling in large cohort studies is that parameter estimation is computationally burdensome, and the accuracy and reliability of current estimation methods remain untested. Here, we propose a fast, reliable, and accurate parameter estimation method for connectome generative models that is scalable to large sample sizes. Our method achieves improved estimation accuracy and reliability and reduces computational cost by orders of magnitude, compared to established methods. We demonstrate an inherent tradeoff between accuracy, reliability, and computational expense in parameter estimation and provide recommendations for leveraging this tradeoff. To enable power analyses in future studies, we empirically approximate the minimum sample size required to detect between-group differences in generative model parameters. While we focus on the classic two-parameter generative model based on connection length and the topological matching index, our method can be generalized to other growth-based generative models. Our work provides a statistical and practical guide to parameter estimation for connectome generative models.
•We developed diffusion MRI phantoms that feature both morphologically complex brain-like and simple tubular fiber bundles.•We evaluated the performance of several filtering algorithms using ...diffusion MRI on these complex phantoms.•Microstructure-informed filtering algorithms can improve the accuracy of tractrography-derived connectomes.•Filtering algorithms can remove false positive connections but at the expense of filtering true connections.•Filtering performance worsens for complex fiber architectures.
Tractography algorithms are prone to reconstructing spurious connections. The set of streamlines generated with tractography can be post-processed to retain the streamlines that are most biologically plausible. Several microstructure-informed filtering algorithms are available for this purpose, however, the comparative performance of these methods has not been extensively evaluated. In this study, we aim to evaluate streamline filtering and post-processing algorithms using simulated connectome phantoms. We first establish a framework for generating connectome phantoms featuring brain-like white matter fiber architectures. We then use our phantoms to systematically evaluate the performance of a range of streamline filtering algorithms, including SIFT, COMMIT, and LiFE. We find that all filtering methods successfully improve connectome accuracy, although filter performance depends on the complexity of the underlying white matter fiber architecture. Filtering algorithms can markedly improve tractography accuracy for simple tubular fiber bundles (F-measure deterministic– unfiltered: 0.49 and best filter: 0.72; F-measure probabilistic– unfiltered: 0.37 and best filter: 0.81), but for more complex brain-like fiber architectures, the improvement is modest (F-measure deterministic– unfiltered: 0.53 and best filter: 0.54; F-measure probabilistic– unfiltered: 0.46 and best filter: 0.50). Overall, filtering algorithms have the potential to improve the accuracy of connectome mapping pipelines, particularly for weighted connectomes and pipelines using probabilistic tractography methods. Our results highlight the need for further advances tractography and streamline filtering to improve the accuracy of connectome mapping.
Complex human behavior emerges from dynamic patterns of neural activity that transiently synchronize between distributed brain networks. This study aims to model the dynamics of neural activity in ...individuals with schizophrenia and to investigate whether the attributes of these dynamics associate with the disorder's behavioral and cognitive deficits. A hidden Markov model (HMM) was inferred from resting‐state functional magnetic resonance imaging (fMRI) data that was temporally concatenated across individuals with schizophrenia (n = 41) and healthy comparison individuals (n = 41). Under the HMM, fluctuations in fMRI activity within 14 canonical resting‐state networks were described using a repertoire of 12 brain states. The proportion of time spent in each state and the mean length of visits to each state were compared between groups, and canonical correlation analysis was used to test for associations between these state descriptors and symptom severity. Individuals with schizophrenia activated default mode and executive networks for a significantly shorter proportion of the 8‐min acquisition than healthy comparison individuals. While the default mode was activated less frequently in schizophrenia, the duration of each activation was on average 4–5 s longer than the comparison group. Severity of positive symptoms was associated with a longer proportion of time spent in states characterized by inactive default mode and executive networks, together with heightened activity in sensory networks. Furthermore, classifiers trained on the state descriptors predicted individual diagnostic status with an accuracy of 76–85%.
The neurobiology of major depressive disorder (MDD) remains incompletely understood, and many individuals fail to respond to standard treatments. Repetitive transcranial magnetic stimulation (rTMS) ...of the dorsolateral prefrontal cortex (DLPFC) has emerged as a promising antidepressant therapy. However, the heterogeneity of response underscores a pressing need for biomarkers of treatment outcome. We acquired resting state functional magnetic resonance imaging (rsfMRI) data in 47 MDD individuals prior to 5–8 weeks of rTMS treatment targeted using the F3 beam approach and in 29 healthy comparison subjects. The caudate, prefrontal cortex, and thalamus showed significantly lower blood oxygenation level‐dependent (BOLD) signal power in MDD individuals at baseline. Critically, individuals who responded best to treatment were associated with lower pre‐treatment BOLD power in these regions. Additionally, functional connectivity (FC) in the default mode and affective networks was associated with treatment response. We leveraged these findings to train support vector machines (SVMs) to predict individual treatment responses, based on learned patterns of baseline FC, BOLD signal power and clinical features. Treatment response (responder vs. nonresponder) was predicted with 85–95% accuracy. Reduction in symptoms was predicted to within a mean error of ±16% (r = .68, p < .001). These preliminary findings suggest that therapeutic outcome to DLPFC‐rTMS could be predicted at a clinically meaningful level using only a small number of core neurobiological features of MDD, warranting prospective testing to ascertain generalizability. This provides a novel, transparent and physiologically plausible multivariate approach for classification of individual response to what has become the most commonly employed rTMS treatment worldwide. This study utilizes data from a larger clinical study (Australian New Zealand Clinical Trials Registry: Investigating Predictors of Response to Transcranial Magnetic Stimulation for the Treatment of Depression; ACTRN12610001071011; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=336262).
The complement cascade has been proposed to contribute to the pathogenesis of schizophrenia. However, it remains unclear whether peripheral complement levels differ in cases compared to controls, ...change over the course of illness and whether they are associated with current symptomatology. This study aimed to: i) investigate whether peripheral complement protein levels are altered at different stages of illness, and ii) identify patterns among complement protein levels that predict clinical symptoms.
Complement factors C1q, C3 and C4 were quantified in 183 participants n = 83 Healthy Controls (HC), n = 10 Ultra-High Risk (UHR) for psychosis, n = 40 First Episode Psychosis (FEP), n = 50 Chronic schizophrenia using Multiplex ELISA. Permutation-based t-tests were used to assess between-group differences in complement protein levels at each of the three illness stages, relative to age- and gender-matched healthy controls. Canonical correlation analysis was used to identify patterns of complement protein levels that correlated with clinical symptoms.
C4 was significantly increased in chronic schizophrenia patients, while C3 and C4 were significantly increased in UHR patients. There were no differences in C1q, C3 and C4 in FEP patients when adjusting for BMI. A molecular pattern of increased C4 and decreased C3 was associated with positive and negative symptom severity in the pooled patient sample.
Our findings indicate that peripheral complement concentration is increased across specific stages of psychosis and its imbalance may be associated with symptom severity. Given the small sample size of the UHR group, these findings should be regarded as exploratory, requiring replication.
Mapping the neurobiology of meditation has been bolstered by functional MRI (fMRI) research, with advancements in ultra-high field 7 Tesla fMRI further enhancing signal quality and neuroanatomical ...resolution. Here, we utilize 7 Tesla fMRI to examine the neural substrates of meditation and replicate existing widespread findings, after accounting for relevant physiological confounds.
In this feasibility study, we scanned 10 beginner meditators (N = 10) while they either attended to breathing (focused attention meditation) or engaged in restful thinking (non-focused rest). We also measured and adjusted the fMRI signal for key physiological differences between meditation and rest. Finally, we explored changes in state mindfulness, state anxiety and focused attention attributes for up to 2 weeks following the single fMRI meditation session.
Group-level task fMRI analyses revealed significant reductions in activity during meditation relative to rest in default-mode network hubs, i.e., antero-medial prefrontal and posterior cingulate cortices, precuneus, as well as visual and thalamic regions. These findings survived stringent statistical corrections for fluctuations in physiological responses which demonstrated significant differences (p < 0.05/n, Bonferroni controlled) between meditation and rest. Compared to baseline, State Mindfulness Scale (SMS) scores were significantly elevated (F(3,9) = 8.16, p < 0.05/n, Bonferroni controlled) following the fMRI meditation session, and were closely maintained at 2-week follow up.
This pilot study establishes the feasibility and utility of investigating focused attention meditation using ultra-high field (7 Tesla) fMRI, by supporting widespread evidence that focused attention meditation attenuates default-mode activity responsible for self-referential processing. Future functional neuroimaging studies of meditation should control for physiological confounds and include behavioural assessments.
•Ultra-high field 7 Tesla fMRI replicates default-mode network attenuation during meditation.•Decrease in thalamus and occipital activity as well, suggesting perceptual decoupling during meditation.•Findings significant after stringent control for physiological signals and other confounds.•Changes in state mindfulness for up to 2 weeks after MRI meditation session.•Meditation neuroimaging studies should include physiological control and behavioural assessments.
We mapped functional and structural brain networks for more than 40,000 UK Biobank participants. Structural connectivity was estimated with tractography and diffusion MRI. Resting-state functional ...MRI was used to infer regional functional connectivity. We provide high-quality structural and functional connectomes for multiple parcellation granularities, several alternative measures of interregional connectivity, and a variety of common data pre-processing techniques, yielding more than one million connectomes in total and requiring more than 200,000 h of compute time. For a single subject, we provide 28 out-of-the-box versions of structural and functional brain networks, allowing users to select, e.g., the parcellation and connectivity measure that best suit their research goals. Furthermore, we provide code and intermediate data for the time-efficient reconstruction of more than 1000 different versions of a subject’s connectome based on an array of methodological choices. All connectomes are available via the UK Biobank data-sharing platform and our connectome mapping pipelines are openly available. In this report, we describe our connectome resource in detail for users, outline key considerations in developing an efficient pipeline to map an unprecedented number of connectomes, and report on the quality control procedures that were completed to ensure connectome reliability and accuracy. We demonstrate that our structural and functional connectivity matrices meet a number of quality control checks and replicate previously established findings in network neuroscience. We envisage that our resource will enable new studies of the human connectome in health, disease, and aging at an unprecedented scale.
•We provide a brain network resource for more than 40,000 UK Biobank participants.•Diffusion MRI data was used to compute structural connectivity and resting-state functional MRI was used to infer regional functional connectivity.•For every individual, we provide 28 ready-to-use precomputed structural and functional brain networks for a range of alternative parcellations and connection metrics.•We provide supporting code and data enabling time-efficient reconstruction of more than 1000 different versions of an individual’s connectome.•A battery of quality control procedures was conducted to ensure connectome reliability and accuracy.
•Associations between neighborhood disadvantage and trajectories of the difference between brain-predicted age and chronological age (brainAGE), and moderating factors, were investigated during ...adolescence.•Neighborhood disadvantage was positively associated with brainAGE during early adolescence and a deceleration (decreasing brainAGE) thereafter•Temperamental effortful control moderated this association. In adolescents exposed to high neighborhood disadvantage, low effortful control was associated with delayed development during late adolescence•Temperamental effortful control and positive parenting were independently associated with brainAGE trajectory
Neighborhood disadvantage has consistently been linked to alterations in brain structure; however, positive environmental (e.g., positive parenting) and psychological factors (e.g., temperament) may buffer these effects. We aimed to investigate associations between neighborhood disadvantage and deviations from typical neurodevelopmental trajectories during adolescence, and examine the moderating role of positive parenting and temperamental effortful control (EC). Using a large dataset (n = 1313), a normative model of brain morphology was established, which was then used to predict the age of youth from a longitudinal dataset (n = 166, three time-points at age 12, 16, and 19). Using linear mixed models, we investigated whether trajectories of the difference between brain-predicted-age and chronological age (brainAGE) were associated with neighborhood disadvantage, and whether positive parenting (positive behavior during a problem-solving task) and EC moderated these associations. We found that neighborhood disadvantage was associated with positive brainAGE during early adolescence and a deceleration (decreasing brainAGE) thereafter. EC moderated this association such that in disadvantaged adolescents, low EC was associated with delayed development (negative brainAGE) during late adolescence. Findings provide evidence for complex associations between environmental and psychological factors, and brain maturation. They suggest that neighborhood disadvantage may have long-term effects on neurodevelopment during adolescence, but high EC could buffer these effects.
The human cerebral cortex is a complex network of functionally specialized regions interconnected by axonal fibers, but the organizational principles underlying cortical connectivity remain unknown. ...Here, we report evidence that one such principle for functional cortical networks involves finding a balance between maximizing communication efficiency and minimizing connection cost, referred to as optimization of network cost-efficiency. We measured spontaneous fluctuations of the blood oxygenation level-dependent signal using functional magnetic resonance imaging in healthy monozygotic (16 pairs) and dizygotic (13 pairs) twins and characterized cost-efficient properties of brain network functional connectivity between 1041 distinct cortical regions. At the global network level, 60% of the interindividual variance in cost-efficiency of cortical functional networks was attributable to additive genetic effects. Regionally, significant genetic effects were observed throughout the cortex in a largely bilateral pattern, including bilateral posterior cingulate and medial prefrontal cortices, dorsolateral prefrontal and superior parietal cortices, and lateral temporal and inferomedial occipital regions. Genetic effects were stronger for cost-efficiency than for other metrics considered, and were more clearly significant in functional networks operating in the 0.09-0.18 Hz frequency interval than at higher or lower frequencies. These findings are consistent with the hypothesis that brain networks evolved to satisfy competitive selection criteria of maximizing efficiency and minimizing cost, and that optimization of network cost-efficiency represents an important principle for the brain's functional organization.
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