Distance is not everything in imaging genomics of functional networks: reply to a commentary on Correlated gene expression supports synchronous activity in brain networks

AbstractOur 2015 paper (Richiardi et al., 2015), showed that transcriptional similarity of gene expression level is higher than expected by chance within functional brain networks (defined by functional magnetic resonance imaging), a relationship that is driven by around 140 genes. These results were replicated in vivo in adolescents, where we showed that SNPs of these genes where associated above chance with in-vivo fMRI connectivity, and in the mouse, where mouse orthologs of our genes showed above-chance association with meso-scale axonal connectivity. This paper has received a commentary on biorXiv (Pantazatos and Li, 2016), making several claims about our results and methods, mainly pointing out that Euclidean distance explains our results (“…high within-network SF is entirely attributable to proximity and is unrelated to functional brain networks…”). Here we address these claims and their weaknesses, and show that our original results stand, contrary to the claims made in the commentary.

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Commentary: BRAIN NETWORKS. Correlated gene expression supports synchronous activity in brain networks. Science 348, 1241-4

10.1101/079202 ◽

2016 ◽

Cited By ~ 1

Author(s):

Spiro P. Pantazatos ◽

Xinyi Li

Keyword(s):

Gene Expression ◽

Resting State ◽

Euclidean Distance ◽

Brain Networks ◽

Resting State Fmri ◽

Spatial Proximity ◽

Functional Magnetic Resonance ◽

Future Directions ◽

Functional Brain ◽

Functional Brain Networks

SummaryA recent report claims that functional brain networks defined with resting-state functional magnetic resonance imaging (fMRI) can be recapitulated with correlated gene expression (i.e. high within-network tissue-tissue “strength fraction”, SF) (Richiardi et al., 2015). However, the authors do not adequately control for spatial proximity. We replicated their main analysis, performed a more effective adjustment for spatial proximity, and tested whether “null networks” (i.e. clusters with center coordinates randomly placed throughout cortex) also exhibit high SF. Removing proximal tissue-tissue correlations by Euclidean distance, as opposed to removing correlations within arbitrary tissue labels as in (Richiardi et al., 2015), reduces within-network SF to no greater than null. Moreover, randomly placed clusters also have significantly high SF, indicating that high within-network SF is entirely attributable to proximity and is unrelated to functional brain networks defined by resting-state fMRI. We discuss why additional validations in the original article are invalid and/or misleading and suggest future directions.

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Modelling hierarchical structure in functional brain networks

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2010.0279 ◽

2010 ◽

Vol 368 (1933) ◽

pp. 5633-5644 ◽

Cited By ~ 14

Author(s):

Pablo M. Gleiser ◽

Victor I. Spoormaker

Keyword(s):

Complex Networks ◽

Hierarchical Structure ◽

Brain Networks ◽

Network Approach ◽

Functional Magnetic Resonance ◽

Resonance Imaging ◽

Functional Brain ◽

Functional Brain Networks ◽

Using Data ◽

The Brain

In this work, we focus on a complex-network approach for the study of the brain. In particular, we consider functional brain networks, where the vertices represent different anatomical regions and the links their functional connectivity. First, we build these networks using data obtained with functional magnetic resonance imaging. Then, we analyse the main characteristics of these complex networks, including degree distribution, the presence of modules and hierarchical structure. Finally, we present a network model with dynamical nodes and adaptive links. We show that the model allows for the emergence of complex networks with characteristics similar to those observed in functional brain networks.

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Gene expression and functional brain networks

Nature Reviews Genetics ◽

10.1038/nrg3986 ◽

2015 ◽

Vol 16 (8) ◽

pp. 439-439

Author(s):

Denise Waldron

Keyword(s):

Gene Expression ◽

Brain Networks ◽

Functional Brain ◽

Functional Brain Networks

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Discriminative Analysis of Schizophrenia Patients Using Topological Properties of Structural and Functional Brain Networks: A Multimodal Magnetic Resonance Imaging Study

Frontiers in Neuroscience ◽

10.3389/fnins.2021.785595 ◽

2022 ◽

Vol 15 ◽

Author(s):

Jing Wang ◽

Pengfei Ke ◽

Jinyu Zang ◽

Fengchun Wu ◽

Kai Wu

Keyword(s):

Magnetic Resonance Imaging ◽

Brain Networks ◽

Brain Network ◽

Svm Classifier ◽

Resonance Imaging ◽

Functional Brain ◽

Clinical Stages ◽

Functional Brain Networks ◽

Discriminative Analysis ◽

Nodal Properties

Interest in the application of machine learning (ML) techniques to multimodal magnetic resonance imaging (MRI) data for the diagnosis of schizophrenia (SZ) at the individual level is growing. However, a few studies have applied the features of structural and functional brain networks derived from multimodal MRI data to the discriminative analysis of SZ patients at different clinical stages. In this study, 205 normal controls (NCs), 61 first-episode drug-naive SZ (FESZ) patients, and 79 chronic SZ (CSZ) patients were recruited. We acquired their structural MRI, diffusion tensor imaging, and resting-state functional MRI data and constructed brain networks for each participant, including the gray matter network (GMN), white matter network (WMN), and functional brain network (FBN). We then calculated 3 nodal properties for each brain network, including degree centrality, nodal efficiency, and betweenness centrality. Two classifications (SZ vs. NC and FESZ vs. CSZ) were performed using five ML algorithms. We found that the SVM classifier with the input features of the combination of nodal properties of both the GMN and FBN achieved the best performance to discriminate SZ patients from NCs [accuracy, 81.2%; area under the receiver operating characteristic curve (AUC), 85.2%; p < 0.05]. Moreover, the SVM classifier with the input features of the combination of the nodal properties of both the GMN and WMN achieved the best performance to discriminate FESZ from CSZ patients (accuracy, 86.2%; AUC, 92.3%; p < 0.05). Furthermore, the brain areas in the subcortical/cerebellum network and the frontoparietal network showed significant importance in both classifications. Together, our findings provide new insights to understand the neuropathology of SZ and further highlight the potential advantages of multimodal network properties for identifying SZ patients at different clinical stages.

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Common and Distinct Functional Brain Networks for Intuitive and Deliberate Decision Making

Brain Sciences ◽

10.3390/brainsci9070174 ◽

2019 ◽

Vol 9 (7) ◽

pp. 174 ◽

Cited By ~ 1

Author(s):

Burak Erdeniz ◽

John Done

Keyword(s):

Reinforcement Learning ◽

Dorsolateral Prefrontal Cortex ◽

Learning Task ◽

Anterior Cingulate ◽

Functional Coupling ◽

Functional Magnetic Resonance ◽

Resonance Imaging ◽

Functional Brain ◽

Dorsolateral Prefrontal ◽

Functional Brain Networks

Reinforcement learning studies in rodents and primates demonstrate that goal-directed and habitual choice behaviors are mediated through different fronto-striatal systems, but the evidence is less clear in humans. In this study, functional magnetic resonance imaging (fMRI) data were collected whilst participants (n = 20) performed a conditional associative learning task in which blocks of novel conditional stimuli (CS) required a deliberate choice, and blocks of familiar CS required an intuitive choice. Using standard subtraction analysis for fMRI event-related designs, activation shifted from the dorso-fronto-parietal network, which involves dorsolateral prefrontal cortex (DLPFC) for deliberate choice of novel CS, to ventro-medial frontal (VMPFC) and anterior cingulate cortex for intuitive choice of familiar CS. Supporting this finding, psycho-physiological interaction (PPI) analysis, using the peak active areas within the PFC for novel and familiar CS as seed regions, showed functional coupling between caudate and DLPFC when processing novel CS and VMPFC when processing familiar CS. These findings demonstrate separable systems for deliberate and intuitive processing, which is in keeping with rodent and primate reinforcement learning studies, although in humans they operate in a dynamic, possibly synergistic, manner particularly at the level of the striatum.

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Cortico-Cortical Evoked Potential Mapping

Invasive Studies of the Human Epileptic Brain ◽

10.1093/med/9780198714668.003.0032 ◽

2018 ◽

pp. 431-452 ◽

Cited By ~ 2

Author(s):

Riki Matsumoto ◽

Takeharu Kunieda

Keyword(s):

Human Brain ◽

Cortical Excitability ◽

Brain Connectivity ◽

Brain Networks ◽

Single Pulse ◽

Functional Brain ◽

Cortical Connections ◽

Functional Brain Networks ◽

Cortical Regions

The utility of single-pulse electrical stimulation (SPES) for epilepsy surgery has been highlighted in the last decade. When applied at a frequency of about 1 Hz, it can probe cortico-cortical connections by averaging electrocorticographic signal time-locked to stimuli to record cortico-cortical evoked potentials (CCEPs) emanating from adjacent and remote cortices. Although limited to patients undergoing invasive presurgical evaluations, CCEPs provide a novel way to explore inter-regional connectivity in vivo in the living human brain to probe functional brain networks such as language and cognitive motor networks. In addition to its impact on basic systems neuroscience, this method, in combination with 50 Hz electrical cortical stimulation, can contribute clinically to the mapping of functional brain systems by tracking cortico-cortical connections among functional cortical regions in individual patients. This approach may help identify normal cortico-cortical networks in pathological brain, or plasticity of brain systems in conjunction with pathology. Because of its high practical value, it has been applied to intraoperative monitoring of functional brain networks in patients with brain tumours. With regard to epilepsy, SPES has been used to probe cortical excitability of the focus (epileptogenicity) and seizure networks. Both early (i.e. CCEP) and delayed responses are regarded as surrogate markers of epileptogenicity. With regard to its potential impact on human brain connectivity maps, worldwide collaboration is warranted to establish standardized CCEP connectivity maps as a solid reference for non-invasive connectome research.

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S88: In vivo investigation of functional brain networks by CCEPs

Clinical Neurophysiology ◽

10.1016/s1388-2457(14)50087-9 ◽

2014 ◽

Vol 125 ◽

pp. S20-S21

Author(s):

R. Matsumoto ◽

T. Kunieda ◽

A. Ikeda

Keyword(s):

Brain Networks ◽

Functional Brain ◽

Functional Brain Networks

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Alterations of connectivity patterns in functional brain networks in patients with mild traumatic brain injury: A longitudinal resting-state functional magnetic resonance imaging study

The Neuroradiology Journal ◽

10.1177/1971400920901706 ◽

2020 ◽

Vol 33 (2) ◽

pp. 186-197 ◽

Cited By ~ 1

Author(s):

Maria M D’Souza ◽

Mukesh Kumar ◽

Ajay Choudhary ◽

Prabhjot Kaur ◽

Pawan Kumar ◽

...

Keyword(s):

Magnetic Resonance Imaging ◽

Traumatic Brain Injury ◽

Magnetic Resonance ◽

Functional Connectivity ◽

Resting State ◽

Brain Networks ◽

Network Connectivity ◽

Resonance Imaging ◽

Functional Brain ◽

Functional Brain Networks

Aim In the present study, we aimed to characterise changes in functional brain networks in individuals who had sustained uncomplicated mild traumatic brain injury (mTBI). We assessed the progression of these changes into the chronic phase. We also attempted to explore how these changes influenced the severity of post-concussion symptoms as well as the cognitive profile of the patients. Methods A total of 65 patients were prospectively recruited for an advanced magnetic resonance imaging (MRI) scan within 7 days of sustaining mTBI. Of these, 25 were reassessed at 6 months post injury. Differences in functional brain networks were analysed between cases and age- and sex-matched healthy controls using independent component analysis of resting-state functional MRI. Results Our study revealed reduced functional connectivity in multiple networks, including the anterior default mode network, central executive network, somato-motor and auditory network in patients who had sustained mTBI. A negative correlation between network connectivity and severity of post-concussive symptoms was observed. Follow-up studies performed 6 months after injury revealed an increase in network connectivity, along with an improvement in the severity of post-concussion symptoms. Neurocognitive tests performed at this time point revealed a positive correlation between the functional connectivity and the test scores, along with a persistence of negative correlation between network connectivity and post-concussive symptom severity. Conclusion Our results suggest that uncomplicated mTBI is associated with specific abnormalities in functional brain networks that evolve over time and may contribute to the severity of post-concussive symptoms and cognitive deficits.

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