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Zalesky, A.

Publications and source records attributed to Zalesky, A..

6 recordsLinked to original sources
bioRxiv2018

Functional Connectivity of Corticostriatal Circuitry and Psychosis-Like Experiences in the General Community

BackgroundPsychotic symptoms are proposed lie on a continuum, ranging from isolated psychosis-like experiences (PLEs) in non-clinical populations to frank disorder. Here, we investigate neurobiological correlates of this symptomatologic continuum by examining whether functional connectivity of dorsal corticostriatal circuitry, which is disrupted in patients and high-risk individuals, is associated with the severity of subclinical PLEs.\n\nMethodsA community sample of 672 adults with no history of psychiatric or neurological illnesses completed a battery of seven questionnaires spanning various PLE domains. Principal component analysis (PCA) estimated major dimensions of PLEs from the questionnaires. PCA dimension scores were then correlated with whole-brain voxelwise functional connectivity (FC) maps of the striatum in a subset of 353 participants who completed a resting-state neuroimaging protocol.\n\nResultsPCA identified two dimensions of PLEs accounting for 62.57% of variance in the measures, corresponding to positive and negative PLEs. Reduced FC between the dorsal striatum and prefrontal cortex correlated with higher positive PLEs. Negative PLEs correlated with increased FC between the dorsal striatum and visual and sensorimotor areas. In the ventral corticostriatal system, positive and negative PLEs were both associated with FC between the ventro-rostral putamen and sensorimotor cortices.\n\nConclusionsConsistent with past findings in patients and high-risk individuals, subthreshold positive symptomatology is associated with reduced FC of the dorsal circuit. These findings suggest that the connectivity of this circuit tracks the expression of psychotic phenomena across a broad spectrum of severity, extending from the subclinical domain to clinical diagnosis.

neuroscience↗
bioRxiv2018

Brain connectivity dynamics: Multilayer network switching rate predicts brain performance

Large-scale brain dynamics measures repeating spatiotemporal connectivity patterns that reflect a range of putative different brain states that underlie the dynamic repertoire of brain functions. The role of transition between brain networks is poorly understood and whether switching between these states is important for behavior has been little studied. Our aim here is to model switching between functional brain networks using multilayer network methods and test for associations between model parameters and behavioral measures. We calculated time-resolved functional MRI (fMRI) connectivity from one-hour long data recordings in 1003 healthy human adults from the Human Connectome Project. The time-resolved fMRI connectivity data was used to generate a spatiotemporal multilayer modularity model enabling us to quantify network switching which we define as the rate at which each brain region transits between different fMRI networks. We found i) an inverse relationship between network switching and connectivity dynamics -defined as the difference in variance between time-resolved fMRI connectivity signals and phase randomized surrogates-; ii) brain connectivity was lower during intervals of network switching; iii) brain areas with frequent network switching had greater temporal complexity; iv) brain areas with high network switching were located in association cortices; and v) using cross-validated Elastic Net regression, network switching predicted inter-subject variation in working memory performance, planning/reasoning and amount of sleep. Our findings shed new light on the importance of brain dynamics predicting task performance and amount of sleep. The ability to switch between network configurations thus appears to be a fundamental feature of optimal brain function.

neuroscience↗
bioRxiv2017

Brain-behavior patterns define a dimensional biotype in medication-naïve adults with attention-deficit hyperactivity disorder

BackgroundChildhood-onset attention-deficit hyperactivity disorder (ADHD) in adults is clinically heterogeneous and commonly presents with different patterns of cognitive deficits. It is unclear if this clinical heterogeneity expresses a dimensional or categorical difference in ADHD.\n\nMethodsWe first studied differences in functional connectivity in multi-echo resting-state functional magnetic resonance imaging (rs-fMRI) acquired from 80 medication-naive adults with ADHD and 123 matched healthy controls. We then used canonical correlation analysis (CCA) to identify latent relationships between symptoms and patterns of altered functional connectivity (dimensional biotype) in patients. Clustering methods were implemented to test if the individual associations between resting-state brain connectivity and symptoms reflected a non-overlapping categorical biotype.\n\nResultsAdults with ADHD showed stronger functional connectivity compared to healthy controls, predominantly between the default-mode, cingulo-opercular and subcortical networks. CCA identified a single mode of brain-symptom co-variation, corresponding to an ADHD dimensional biotype. This dimensional biotype is characterized by a unique combination of altered connectivity correlating with symptoms of hyperactivity-impulsivity, inattention, and intelligence. Clustering analyses did not support the existence of distinct categorical biotypes of adult ADHD.\n\nConclusionsOverall, our data advance a novel finding that the reduced functional segregation between default-mode and cognitive control networks supports a clinically important dimensional biotype of childhood-onset adult ADHD. Despite the heterogeneity of its presentation, our work suggests that childhood-onset adult ADHD is a single disorder characterized by dimensional brain-symptom mediators.

neuroscience↗
bioRxiv2017

On the relationship between instantaneous phase synchrony and correlation-based sliding windows for time-resolved fMRI connectivity analysis

Correlation-based sliding window analysis (CSWA) is the most commonly used method to estimate time-resolved functional MRI (fMRI) connectivity. However, instantaneous phase synchrony analysis (IPSA) is gaining popularity mainly because it offers single time-point resolution of time-resolved fMRI connectivity. We aim to provide a systematic comparison between these two approaches, on both temporal and topological levels.\n\nFor this purpose, we used resting-state fMRI data from two separate cohorts with different temporal resolutions (45 healthy subjects from Human Connectome Project fMRI data with repetition time of 0.72 s and 25 healthy subjects from a separate validation fMRI dataset with a repetition time of 3 s). For time-resolved functional connectivity analysis, we calculated tapered CSWA over a wide range of different window lengths that were temporally and topologically compared to IPSA.\n\nWe found a strong association in connectivity dynamics between IPSA and CSWA when considering the absolute values of CSWA. This association peaked at a CSWA window length of [~]20 seconds, irrespective of the sampling rate of the underlying fMRI data. Narrow-band filtering of fMRI data (0.03-0.07 Hz) yielded a stronger relationship between IPSA and CSWA than wider-band (0.01-0.1 Hz). On a topological level, time-averaged IPSA and CSWA nodes were non-linearly correlated, mainly because nodes with strong negative correlations (CSWA) displayed high phase synchrony (IPSA).\n\nOur results suggest that IPSA and CSWA provide comparable characterizations of time-resolved fMRI connectivity for appropriately chosen window lengths. Although IPSA requires narrow-band fMRI filtering, we recommend the use of IPSA given that it does not mandate a (semi-)arbitrary choice of window length and window overlap. A MATLAB code for calculating IPSA is provided.

neuroscience↗
bioRxiv2017

Reconfiguration of brain network architectures between resting state and complexity-dependent cognitive reasoning

Our capacity for higher cognitive reasoning has a measureable limit. This limit is thought to arise from the brains capacity to flexibly reconfigure interactions between spatially distributed networks. Recent work, however, has suggested that reconfigurations of task-related networks are modest when compared with intrinsic resting state network architecture. Here we combined resting state and task-driven functional magnetic resonance imaging to examine how flexible, task-specific reconfigurations associated with increasing reasoning demands are integrated within a stable intrinsic brain topology. Human participants (21 males and 28 females) underwent an initial resting state scan, followed by a cognitive reasoning task involving different levels of complexity, followed by a second resting state scan. The reasoning task required participants to deduce the identity of a missing element in a 4 x 4 matrix, and item difficulty was scaled parametrically as determined by relational complexity theory. Analyses revealed that external task engagement was characterized by a significant change in functional brain modules. Specifically, resting state and null-task demand conditions were associated with more segregated brain network topology, whereas increases in reasoning complexity resulted in merging of resting state modules. Further increments in task complexity did not change the established modular architecture, but impacted selective patterns of connectivity between fronto-parietal, subcortical, cingulo-opercular and default-mode networks. Larger increases in network efficiency within the newly established task modules were associated with higher reasoning accuracy. Our results shed light on the network architectures that underlie external task engagement, and highlight selective changes in brain connectivity supporting increases in task complexity.\n\nSignificance StatementHumans have clear limits in their ability to solve complex reasoning problems. It is thought that such limitations arise from flexible, moment-to-moment reconfigurations of functional brain networks. It is less clear how such task-driven adaptive changes in connectivity relate to stable, intrinsic networks of the brain and behavioral performance. We found that increased reasoning demands rely on selective patterns of connectivity within cortical networks that emerged in addition to a more general, task-induced modular architecture. This task-driven architecture reverted to a more segregated resting state architecture both immediately before and after the task. These findings reveal how flexibility in human brain networks is integral to achieving successful reasoning performance across different levels of cognitive demand.

neuroscience↗
bioRxiv2017

Neural decoding of visual stimuli varies with fluctuations in global network efficiency

Functional magnetic resonance imaging (fMRI) studies have shown that neural activity fluctuates spontaneously between different states of global synchronization over a timescale of several seconds. Such fluctuations generate transient states of high and low correlation across distributed cortical areas. It has been hypothesized that such fluctuations in global efficiency might alter patterns of activity in local neuronal populations elicited by changes in incoming sensory stimuli. To test this prediction, we used a linear decoder to discriminate patterns of neural activity elicited by face and motion stimuli presented periodically while participants underwent time-resolved fMRI. As predicted, decoding was reliably higher during states of high global efficiency than during states of low efficiency, and this difference was evident across both visual and non-visual cortical regions. The results indicate that slow fluctuations in global network efficiency are associated with variations in the pattern of activity across widespread cortical regions responsible for representing distinct categories of visual stimulus. More broadly, the findings highlight the importance of understanding the impact of global fluctuations in functional connectivity on specialised, stimulus driven neural processes.

neuroscience↗