Brain network communication: concepts, models and applications.

Understanding communication and information processing in nervous systems is a central goal of neuroscience. Over the past two decades, advances in connectomics and network neuroscience have opened new avenues for investigating polysynaptic communication in complex brain networks. Recent work has brought into question the mainstay assumption that connectome signalling occurs exclusively via shortest paths, resulting in a sprawling constellation of alternative network communication models. This Review surveys the latest developments in models of brain network communication. We begin by drawing a conceptual link between the mathematics of graph theory and biological aspects of neural signalling such as transmission delays and metabolic cost. We organize key network communication models and measures into a taxonomy, aimed at helping researchers navigate the growing number of concepts and methods in the literature. The taxonomy highlights the pros, cons and interpretations of different conceptualizations of connectome signalling. We showcase the utility of network communication models as a flexible, interpretable and tractable framework to study brain function by reviewing prominent applications in basic, cognitive and clinical neurosciences. Finally, we provide recommendations to guide the future development, application and validation of network communication models.

Mapping human brain charts cross-sectionally and longitudinally.

Brain scans acquired across large, age-diverse cohorts have facilitated recent progress in establishing normative brain aging charts. Here, we ask the critical question of whether cross-sectional estimates of age-related brain trajectories resemble those directly measured from longitudinal data. We show that age-related brain changes inferred from cross-sectionally mapped brain charts can substantially underestimate actual changes measured longitudinally. We further find that brain aging trajectories vary markedly between individuals and are difficult to predict with population-level age trends estimated cross-sectionally. Prediction errors relate modestly to neuroimaging confounds and lifestyle factors. Our findings provide explicit evidence for the importance of longitudinal measurements in ascertaining brain development and aging trajectories.

Improving power in functional magnetic resonance imaging by moving beyond cluster-level inference.

Inference in neuroimaging typically occurs at the level of focal brain areas or circuits. Yet, increasingly, well-powered studies paint a much richer picture of broad-scale effects distributed throughout the brain, suggesting that many focal reports may only reflect the tip of the iceberg of underlying effects. How focal versus broad-scale perspectives influence the inferences we make has not yet been comprehensively evaluated using real data. Here, we compare sensitivity and specificity across procedures representing multiple levels of inference using an empirical benchmarking procedure that resamples task-based connectomes from the Human Connectome Project dataset (∼1,000 subjects, 7 tasks, 3 resampling group sizes, 7 inferential procedures). Only broad-scale (network and whole brain) procedures obtained the traditional 80% statistical power level to detect an average effect, reflecting >20% more statistical power than focal (edge and cluster) procedures. Power also increased substantially for false discovery rate- compared with familywise error rate-controlling procedures. The downsides are fairly limited; the loss in specificity for broad-scale and FDR procedures was relatively modest compared to the gains in power. Furthermore, the broad-scale methods we introduce are simple, fast, and easy to use, providing a straightforward starting point for researchers. This also points to the promise of more sophisticated broad-scale methods for not only functional connectivity but also related fields, including task-based activation. Altogether, this work demonstrates that shifting the scale of inference and choosing FDR control are both immediately attainable and can help remedy the issues with statistical power plaguing typical studies in the field.

Within-subject reliability of brain networks during advanced meditation: An intensively sampled 7 Tesla MRI case study.

Advanced meditation such as jhana meditation can produce various altered states of consciousness (jhanas) and cultivate rewarding psychological qualities including joy, peace, compassion, and attentional stability. Mapping the neurobiological substrates of jhana meditation can inform the development and application of advanced meditation to enhance well-being. Only two prior studies have attempted to investigate the neural correlates of jhana meditation, and the rarity of adept practitioners has largely restricted the size and extent of these studies. Therefore, examining the consistency and reliability of observed brain responses associated with jhana meditation can be valuable. In this study, we aimed to characterize functional magnetic resonance imaging (fMRI) reliability within a single subject over repeated runs in canonical brain networks during jhana meditation performed by an adept practitioner over 5 days (27 fMRI runs) inside an ultra-high field 7 Tesla MRI scanner. We found that thalamus and several cortical networks, that is, the somatomotor, limbic, default-mode, control, and temporo-parietal, demonstrated good within-subject reliability across all jhanas. Additionally, we found that several other relevant brain networks (e.g., attention, salience) showed noticeable increases in reliability when fMRI measurements were adjusted for variability in self-reported phenomenology related to jhana meditation. Overall, we present a preliminary template of reliable brain areas likely underpinning core neurocognitive elements of jhana meditation, and highlight the utility of neurophenomenological experimental designs for better characterizing neuronal variability associated with advanced meditative states.

Mechanisms of imbalanced frontostriatal functional connectivity in obsessive-compulsive disorder.

The diagnosis of obsessive-compulsive disorder (OCD) has been linked with changes in frontostriatal resting-state connectivity. However, replication of prior findings is lacking, and the mechanistic understanding of these effects is incomplete. To confirm and advance knowledge on changes in frontostriatal functional connectivity in OCD, participants with OCD and matched healthy controls underwent resting-state functional, structural and diffusion neuroimaging. Functional connectivity changes in frontostriatal systems were here replicated in individuals with OCD (n = 52) compared with controls (n = 45). OCD participants showed greater functional connectivity (t = 4.3, PFWE = 0.01) between the nucleus accumbens (NAcc) and the orbitofrontal cortex (OFC) but lower functional connectivity between the dorsal putamen and lateral prefrontal cortex (t = 3.8, PFWE = 0.04) relative to controls. Computational modelling suggests that NAcc-OFC connectivity changes reflect an increased influence of NAcc over OFC activity and reduced OFC influence over NAcc activity (posterior probability, Pp > 0.66). Conversely, dorsal putamen showed reduced modulation over lateral prefrontal cortex activity (Pp > 0.90). These functional deregulations emerged on top of a generally intact anatomical substrate. We provide out-of-sample replication of opposite changes in ventro-anterior and dorso-posterior frontostriatal connectivity in OCD and advance the understanding of the neural underpinnings of these functional perturbations. These findings inform the development of targeted therapies normalizing frontostriatal dynamics in OCD.

Network analysis of neuropsychiatric, cognitive, and functional complications of stroke: implications for novel treatment targets.

AIM: Recovery from stroke is adversely affected by neuropsychiatric complications, cognitive impairment, and functional disability. Better knowledge of their mutual relationships is required to inform effective interventions. Network theory enables the conceptualization of symptoms and impairments as dynamic and mutually interacting systems. We aimed to identify interactions of poststroke complications using network analysis in diverse stroke samples. METHODS: Data from 2185 patients were sourced from member studies of STROKOG (Stroke and Cognition Consortium), an international collaboration of stroke studies. Networks were generated for each cohort, whereby nodes represented neuropsychiatric symptoms, cognitive deficits, and disabilities on activities of daily living. Edges characterized associations between them. Centrality measures were used to identify hub items. RESULTS: Across cohorts, a single network of interrelated poststroke complications emerged. Networks exhibited dissociable depression, apathy, fatigue, cognitive impairment, and functional disability modules. Worry was the most central symptom across cohorts, irrespective of the depression scale used. Items relating to activities of daily living were also highly central nodes. Follow-up analysis in two studies revealed that individuals who worried had more densely connected networks than those free of worry (CASPER [Cognition and Affect after Stroke: Prospective Evaluation of Risks] study: S = 9.72, P = 0.038; SSS [Sydney Stroke Study]: S = 13.56, P = 0.069). CONCLUSION: Neuropsychiatric symptoms are highly interconnected with cognitive deficits and functional disabilities resulting from stroke. Given their central position and high level of connectedness, worry and activities of daily living have the potential to drive multimorbidity and mutual reinforcement between domains of poststroke complications. Targeting these factors early after stroke may have benefits that extend to other complications, leading to better stroke outcomes.

Evaluation of tractogram filtering methods using human-like connectome phantoms.

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.

Parameter estimation for connectome generative models: Accuracy, reliability, and a fast parameter fitting method.

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.

Connectomes for 40,000 UK Biobank participants: A multi-modal, multi-scale brain network resource.

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.

Functional re-organization of hippocampal-cortical gradients during naturalistic memory processes.

The functional organization of the hippocampus mirrors that of the cortex, changing smoothly along connectivity gradients and abruptly at inter-areal boundaries. Hippocampal-dependent cognitive processes require flexible integration of these hippocampal gradients into functionally related cortical networks. To understand the cognitive relevance of this functional embedding, we acquired fMRI data while participants viewed brief news clips, either containing or lacking recently familiarized cues. Participants were 188 healthy mid-life adults and 31 adults with mild cognitive impairment (MCI) or Alzheimer's disease (AD). We employed a recently developed technique - connectivity gradientography - to study gradually changing patterns of voxel to whole brain functional connectivity and their sudden transitions. We observed that functional connectivity gradients of the anterior hippocampus map onto connectivity gradients across the default mode network during these naturalistic stimuli. The presence of familiar cues in the news clips accentuates a stepwise transition across the boundary from the anterior to the posterior hippocampus. This functional transition is shifted in the posterior direction in the left hippocampus of individuals with MCI or AD. These findings shed new light on the functional integration of hippocampal connectivity gradients into large-scale cortical networks, how these adapt with memory context and how these change in the presence of neurodegenerative disease.

Revisiting deficits in threat and safety appraisal in obsessive-compulsive disorder.

Current behavioural treatment of obsessive-compulsive disorder (OCD) is informed by fear conditioning and involves iteratively re-evaluating previously threatening stimuli as safe. However, there is limited research investigating the neurobiological response to conditioning and reversal of threatening stimuli in individuals with OCD. A clinical sample of individuals with OCD (N = 45) and matched healthy controls (N = 45) underwent functional magnetic resonance imaging. While in the scanner, participants completed a well-validated fear reversal task and a resting-state scan. We found no evidence for group differences in task-evoked brain activation or functional connectivity in OCD. Multivariate analyses encompassing all participants in the clinical and control groups suggested that subjective appraisal of threatening and safe stimuli were associated with a larger difference in brain activity than the contribution of OCD symptoms. In particular, we observed a brain-behaviour continuum whereby heightened affective appraisal was related to increased bilateral insula activation during the task (r = 0.39, pFWE = .001). These findings suggest that changes in conditioned threat-related processes may not be a core neurobiological feature of OCD and encourage further research on the role of subjective experience in fear conditioning.

Cell type-specific manifestations of cortical thickness heterogeneity in schizophrenia.

Brain morphology differs markedly between individuals with schizophrenia, but the cellular and genetic basis of this heterogeneity is poorly understood. Here, we sought to determine whether cortical thickness (CTh) heterogeneity in schizophrenia relates to interregional variation in distinct neural cell types, as inferred from established gene expression data and person-specific genomic variation. This study comprised 1849 participants in total, including a discovery (140 cases and 1267 controls) and a validation cohort (335 cases and 185 controls). To characterize CTh heterogeneity, normative ranges were established for 34 cortical regions and the extent of deviation from these ranges was measured for each individual with schizophrenia. CTh deviations were explained by interregional gene expression levels of five out of seven neural cell types examined: (1) astrocytes; (2) endothelial cells; (3) oligodendrocyte progenitor cells (OPCs); (4) excitatory neurons; and (5) inhibitory neurons. Regional alignment between CTh alterations with cell type transcriptional maps distinguished broad patient subtypes, which were validated against genomic data drawn from the same individuals. In a predominantly neuronal/endothelial subtype (22% of patients), CTh deviations covaried with polygenic risk for schizophrenia (sczPRS) calculated specifically from genes marking neuronal and endothelial cells (r = -0.40, p = 0.010). Whereas, in a predominantly glia/OPC subtype (43% of patients), CTh deviations covaried with sczPRS calculated from glia and OPC-linked genes (r = -0.30, p = 0.028). This multi-scale analysis of genomic, transcriptomic, and brain phenotypic data may indicate that CTh heterogeneity in schizophrenia relates to inter-individual variation in cell-type specific functions. Decomposing heterogeneity in relation to cortical cell types enables prioritization of schizophrenia subsets for future disease modeling efforts.

Abnormal Structural Network Communication Reflects Cognitive Deficits in Schizophrenia.

Schizophrenia has long been thought to be a disconnection syndrome and several previous studies have reported widespread abnormalities in white matter tracts in individuals with schizophrenia. Furthermore, reductions in structural connectivity may also impair communication between anatomically unconnected pairs of brain regions, potentially impacting global signal traffic in the brain. Therefore, we used different communication models to examine direct and indirect structural connections (polysynaptic) communication in large-scale brain networks in schizophrenia. Diffusion-weighted magnetic resonance imaging scans were acquired from 62 patients diagnosed with schizophrenia and 35 controls. In this study, we used five network communication models including, shortest paths, navigation, diffusion, search information and communicability to examine polysynaptic communication in large-scale brain networks in schizophrenia. We showed less efficient communication between spatially widespread brain regions particulary encompassing cortico-subcortical basal ganglia network in schizophrenia group relative to controls. Then, we also examined whether reduced communication efficiency was related to clinical symptoms in schizophrenia group. Among different measures of communication efficiency, only navigation efficiency was associated with global cognitive impairment across multiple cognitive domains including verbal learning, processing speed, executive functions and working memory, in individuals with schizophrenia. We did not find any association between communication efficiency measures and positive or negative symptoms within the schizophrenia group. Our findings are important for improving our mechanistic understanding of neurobiological process underlying cognitive symptoms in schizophrenia.

Genetic and Epigenetic Regulation in Lingo-1: Effects on Cognitive Function and White Matter Microstructure in a Case-Control Study for Schizophrenia.

Leucine-rich repeat and immunoglobulin domain-containing protein (Lingo-1) plays a vital role in a large number of neuronal processes underlying learning and memory, which are known to be disrupted in schizophrenia. However, Lingo-1 has never been examined in the context of schizophrenia. The genetic association of a single-nucleotide polymorphism (SNP, rs3144) and methylation (CpG sites) in the Lingo-1 3'-UTR region was examined, with the testing of cognitive dysfunction and white matter (WM) integrity in a schizophrenia case-control cohort (n = 268/group). A large subset of subjects (97 control and 161 schizophrenia subjects) underwent structural magnetic resonance imaging (MRI) brain scans to assess WM integrity. Frequency of the rs3144 minor allele was overrepresented in the schizophrenia population (p = 0.03), with an odds ratio of 1.39 (95% CI 1.016-1.901). CpG sites surrounding rs3144 were hypermethylated in the control population (p = 0.032) compared to the schizophrenia group. rs3144 genotype was predictive of membership to a subclass of schizophrenia subjects with generalized cognitive deficits (p < 0.05), in addition to having associations with WM integrity (p = 0.018). This is the first study reporting a potential implication of genetic and epigenetic risk factors in Lingo-1 in schizophrenia. Both of these genetic and epigenetic alterations may also have associations with cognitive dysfunction and WM integrity in the context of the schizophrenia pathophysiology.

Dopamine Transporter Is a Master Regulator of Dopaminergic Neural Network Connectivity.

Dopaminergic neurons of the substantia nigra pars compacta (SNC) and ventral tegmental area (VTA) exhibit spontaneous firing activity. The dopaminergic neurons in these regions have been shown to exhibit differential sensitivity to neuronal loss and psychostimulants targeting dopamine transporter. However, it remains unclear whether these regional differences scale beyond individual neuronal activity to regional neuronal networks. Here, we used live-cell calcium imaging to show that network connectivity greatly differs between SNC and VTA regions with higher incidence of hub-like neurons in the VTA. Specifically, the frequency of hub-like neurons was significantly lower in SNC than in the adjacent VTA, consistent with the interpretation of a lower network resilience to SNC neuronal loss. We tested this hypothesis, in DAT-cre/loxP-GCaMP6f mice of either sex, when activity of an individual dopaminergic neuron is suppressed, through whole-cell patch clamp electrophysiology, in either SNC or VTA networks. Neuronal loss in the SNC increased network clustering, whereas the larger number of hub-neurons in the VTA overcompensated by decreasing network clustering in the VTA. We further show that network properties are regulatable via a dopamine transporter but not a D2 receptor dependent mechanism. Our results demonstrate novel regulatory mechanisms of functional network topology in dopaminergic brain regions.SIGNIFICANCE STATEMENT In this work, we begin to untangle the differences in complex network properties between the substantia nigra pars compacta (SNC) and VTA, that may underlie differential sensitivity between regions. The methods and analysis employed provide a springboard for investigations of network topology in multiple deep brain structures and disorders.

Network communication models narrow the gap between the modular organization of structural and functional brain networks.

Structural and functional brain networks are modular. Canonical functional systems, such as the default mode network, are well-known modules of the human brain and have been implicated in a large number of cognitive, behavioral and clinical processes. However, modules delineated in structural brain networks inferred from tractography generally do not recapitulate canonical functional systems. Neuroimaging evidence suggests that functional connectivity between regions in the same systems is not always underpinned by anatomical connections. As such, direct structural connectivity alone would be insufficient to characterize the functional modular organization of the brain. Here, we demonstrate that augmenting structural brain networks with models of indirect (polysynaptic) communication unveils a modular network architecture that more closely resembles the brain's established functional systems. We find that diffusion models of polysynaptic connectivity, particularly communicability, narrow the gap between the modular organization of structural and functional brain networks by 20-60%, whereas routing models based on single efficient paths do not improve mesoscopic structure-function correspondence. This suggests that functional modules emerge from the constraints imposed by local network structure that facilitates diffusive neural communication. Our work establishes the importance of modeling polysynaptic communication to understand the structural basis of functional systems.

Connectome spatial smoothing (CSS): Concepts, methods, and evaluation.

Structural connectomes are increasingly mapped at high spatial resolutions comprising many hundreds-if not thousands-of network nodes. However, high-resolution connectomes are particularly susceptible to image registration misalignment, tractography artifacts, and noise, all of which can lead to reductions in connectome accuracy and test-retest reliability. We investigate a network analogue of image smoothing to address these key challenges. Connectome Spatial Smoothing (CSS) involves jointly applying a carefully chosen smoothing kernel to the two endpoints of each tractography streamline, yielding a spatially smoothed connectivity matrix. We develop computationally efficient methods to perform CSS using a matrix congruence transformation and evaluate a range of different smoothing kernel choices on CSS performance. We find that smoothing substantially improves the identifiability, sensitivity, and test-retest reliability of high-resolution connectivity maps, though at a cost of increasing storage burden. For atlas-based connectomes (i.e. low-resolution connectivity maps), we show that CSS marginally improves the statistical power to detect associations between connectivity and cognitive performance, particularly for connectomes mapped using probabilistic tractography. CSS was also found to enable more reliable statistical inference compared to connectomes without any smoothing. We provide recommendations for optimal smoothing kernel parameters for connectomes mapped using both deterministic and probabilistic tractography. We conclude that spatial smoothing is particularly important for the reliability of high-resolution connectomes, but can also provide benefits at lower parcellation resolutions. We hope that our work enables computationally efficient integration of spatial smoothing into established structural connectome mapping pipelines.

Accommodating site variation in neuroimaging data using normative and hierarchical Bayesian models.

The potential of normative modeling to make individualized predictions from neuroimaging data has enabled inferences that go beyond the case-control approach. However, site effects are often confounded with variables of interest in a complex manner and can bias estimates of normative models, which has impeded the application of normative models to large multi-site neuroimaging data sets. In this study, we suggest accommodating for these site effects by including them as random effects in a hierarchical Bayesian model. We compared the performance of a linear and a non-linear hierarchical Bayesian model in modeling the effect of age on cortical thickness. We used data of 570 healthy individuals from the ABIDE (autism brain imaging data exchange) data set in our experiments. In addition, we used data from individuals with autism to test whether our models are able to retain clinically useful information while removing site effects. We compared the proposed single stage hierarchical Bayesian method to several harmonization techniques commonly used to deal with additive and multiplicative site effects using a two stage regression, including regressing out site and harmonizing for site with ComBat, both with and without explicitly preserving variance caused by age and sex as biological variation of interest, and with a non-linear version of ComBat. In addition, we made predictions from raw data, in which site has not been accommodated for. The proposed hierarchical Bayesian method showed the best predictive performance according to multiple metrics. Beyond that, the resulting z-scores showed little to no residual site effects, yet still retained clinically useful information. In contrast, performance was particularly poor for the regression model and the ComBat model in which age and sex were not explicitly modeled. In all two stage harmonization models, predictions were poorly scaled, suffering from a loss of more than 90% of the original variance. Our results show the value of hierarchical Bayesian regression methods for accommodating site variation in neuroimaging data, which provides an alternative to harmonization techniques. While the approach we propose may have broad utility, our approach is particularly well suited to normative modeling where the primary interest is in accurate modeling of inter-subject variation and statistical quantification of deviations from a reference model.

Multi-timepoint pattern analysis: Influence of personality and behavior on decoding context-dependent brain connectivity dynamics.

Behavioral traits are rarely considered in task-evoked functional magnetic resonance imaging (MRI) studies, yet these traits can affect how an individual engages with the task, and thus lead to heterogeneity in task-evoked brain responses. We aimed to investigate whether interindividual variation in behavior associates with the accuracy of predicting task-evoked changes in the dynamics of functional brain connectivity measured with functional MRI. We developed a novel method called multi-timepoint pattern analysis (MTPA), in which binary logistic regression classifiers were trained to distinguish rest from each of 7 tasks (i.e., social cognition, working memory, language, relational, motor, gambling, emotion) based on functional connectivity dynamics measured in 1,000 healthy adults. We found that connectivity dynamics for multiple pairs of large-scale networks enabled individual classification between task and rest with accuracies exceeding 70%, with the most discriminatory connections relatively unique to each task. Crucially, interindividual variation in classification accuracy significantly associated with several behavioral, cognition and task performance measures. Classification between task and rest was generally more accurate for individuals with higher intelligence and task performance. Additionally, for some of the tasks, classification accuracy improved with lower perceived stress, lower aggression, higher alertness, and greater endurance. We conclude that heterogeneous dynamic adaptations of functional brain networks to changing cognitive demands can be reliably captured as linearly separable patterns by MTPA. Future studies should account for interindividual variation in behavior when investigating context-dependent dynamic functional connectivity.

The impact of smoking status on cognition and brain morphology in schizophrenia spectrum disorders.

BACKGROUND: Cigarette smoking is associated with worse cognition and decreased cortical volume and thickness in healthy cohorts. Chronic cigarette smoking is prevalent in schizophrenia spectrum disorders (SSD), but the effects of smoking status on the brain and cognition in SSD are not clear. This study aimed to understand whether cognitive performance and brain morphology differed between smoking and non-smoking individuals with SSD compared to healthy controls. METHODS: Data were obtained from the Australian Schizophrenia Research Bank. Cognitive functioning was measured in 299 controls and 455 SSD patients. Cortical volume, thickness and surface area data were analysed from T1-weighted structural scans obtained in a subset of the sample (n = 82 controls, n = 201 SSD). Associations between smoking status (cigarette smoker/non-smoker), cognition and brain morphology were tested using analyses of covariance, including diagnosis as a moderator. RESULTS: No smoking by diagnosis interactions were evident, and no significant differences were revealed between smokers and non-smokers across any of the variables measured, with the exception of a significantly thinner left posterior cingulate in smokers compared to non-smokers. Several main effects of smoking in the cognitive, volume and thickness analyses were initially significant but did not survive false discovery rate (FDR) correction. CONCLUSIONS: Despite the general absence of significant FDR-corrected findings, trend-level effects suggest the possibility that subtle smoking-related effects exist but were not uncovered due to low statistical power. An investigation of this topic is encouraged to confirm and expand on our findings.