Top PDF Predictive Models in Brain Connectivity Analysis

Predictive Models in Brain Connectivity Analysis

Predictive Models in Brain Connectivity Analysis

Understanding the human brain have been one of the most important topics studied by neuroscience. This field has come up with different imaging theories that are used to quantify the properties of brain networks and their components. The brain has been modeled as a complex network system 1 under the premise that neurons make up an interconnected structure into the nervous system. The mainly components of these networks are the Nodes and their Links. When nodes are seen as the regions of interest, based on a certain parcellation scheme, inter-regional relationships will be defined by three types of connectivity: structural, functional and effective. While structural connectivity approaches are focused on the anatomical parts, which refers to the existence of tracts connecting different brain areas, functional connectivity approaches are focused on the statistical dependence of the signals coming from different areas of the brain. The effective connectivity is similar to the functional but with the peculiarity that it brings the causation elements to the analysis. This type of connectivity allows to determine, when the activation of one area directly causes a change (activation or depression) in another area, or provoke any other special signal. For the effective connectivity, unlike the functional, is possible to evaluate directionality and causality because it provide information about neural interactions.
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Studying effective brain connectivity using multiregression dynamic models

Studying effective brain connectivity using multiregression dynamic models

We propose a strategy of using the MDM-IPA to search initially across a class of simple linear MDMs which are time homogeneous, linear and with no change points. We then check the best model using these new diagnostic methods. In practice, we have found the linear MDMs usually perform well for most nodes receiving inputs from other nodes. However, when diagnostics discover a discrepancy of fit, the MDM class is sufficiently expressive for it to be embellished to accommodate other anomalous features. For example, it is possible to include time-dependent error variances, change points, interaction terms in the regression and so on, to better reflect the underlying model and refine the analysis. Often, even after such embellishment, the model still stays within a conditionally conjugate class. Therefore, if our diagnostics identify serious deviation from the highest scoring simple MDM, we can adapt this model and its high scoring neighbours with features explaining the deviations. The model selection process using Bayes factors can then be reapplied to discover models that describe the process even better. In this way, we can iteratively augment the fitted model and its highest scoring competitors with embellishments until the search class accommodates the main features observed in the dynamic processes well. This is one advantage of adopting a fully Bayesian methodology to perform this analysis. Standard Bayesian diagnostics can be adapted to provide guidance in checking and where necessary to guide the modification of the model class. In Chapter 4, we demonstrate this process with real fMRI datasets.
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An analysis of connectivity

An analysis of connectivity

Recent evidence in biology indicates crossmodal, which is to say informa- tion sharing between the different senses, influences in the brain. This helps to explain such phenomenon as the McGurk effect, where even though a person knows that he is seeing the lip movement “GA” and is hearing the sound “BA”, the person usually can’t help but think that they are hearing the sound “DA”. The McGurk effect is an example of where the visual sense influences the perception of the audio sense. These discoveries transition old feedforward models of the brain to ones that rely on feedback connections and, more recently, crossmodal connections. Although we have many soft- ware systems that rely on some form of intelligence, i.e. person recognition software, speech to text software, etc, very few take advantage of crossmodal influences.
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Adaptive nonlinear multivariate brain connectivity analysis of motor imagery movements using graph theory

Adaptive nonlinear multivariate brain connectivity analysis of motor imagery movements using graph theory

A significant drawback o f conventional M VAR is that the connectivity measures are fixed with time and computed from M VAR models with constant coefficients fitted over the entire time-course, assuming brain as static or stationary process. This shortcoming has been observed in some o f previous studies on MI brain connectivity analysis [28, 90, 97, 100-102, 118, 119, 122]. However, an important property o f brain is its dynamic (time-variant) behavior during any task therefore analyzing brain connectivity within a static (time-invariant) framework or stationarity assumption is incompatible with the well-known dynamical condition- dependent nature o f brain activity and leads to misinterpretation o f the results. A number o f algorithms have been proposed for fitting M VAR models to non- stationary signals, known as adaptive M VAR (AMVAR) or time-varying M VAR (TV-MVAR). In modern neuroscience, the most popular approaches include segmentation (overlapping sliding-window) [123, 124] and state space approaches [125, 126]. Segmentation-based AMVAR models apply a sliding window o f length W from the multivariate dataset with length T , and fit a M VAR model to this data. Then, the window by a quantity Q is incremented and the procedure is repeated until the start o f the window is greater than T - W . This technique has been recently utilized for single-trial connectivity estimation for classification o f two M I tasks in BCI [127]. Although this technique produces M VAR coefficient matrices that describe the evolution o f the M VAR process across time, the local stationarity of each window is still assumed and this may not be able to detect rapid parameter changes o f brain activity. State space models (SSMs) on the other hand are the AMVAR models where the AR coefficients vary instantaneously with time. SSM provides a general framework for analyzing deterministic and stochastic dynamical systems that are measured or observed through a stochastic process. Although this is a powerful technique for dealing with non-stationarity o f neurophysiological signals, there are very limited studies [128, 129] o f applying SSMs for brain connectivity analysis in the literature and there is no study o f using SSMs for brain connectivity analysis during M I movements. The SSM consists o f two components: (1) state
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Fractal analysis of resting state functional connectivity of the brain

Fractal analysis of resting state functional connectivity of the brain

However, there is a controversy about whether the FGN is the most ap- propriate model for resting state neuroimaging signals among a variety of long memory models. While the FGN model is dened just with two parameters under mathematically strict conditions of self-similarity, a neuroimaging signal is produced from a nonlinear biological system which is controlled by numer- ous hidden parameters. In this reason, the fractionally integrated process (FIP) model, based on the concept of fractional dierencing [22], is worth considera- tion as an alternative to the FGN model since it embraces diverse types of long memory. Indeed, the FGN is regarded as a special type of the FIP model which is more extensive than FGN.
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Using computational models to relate structural and
functional brain connectivity

Using computational models to relate structural and functional brain connectivity

The described phenomena leave many open questions. For instance, while we have shown that the predictive power of pair-wise synchrony stability for the structure–function agreement is strong across widely varying topologies, it may be parametrically modulated by the network topology or other parameters of the dynamic model. For instance, this prediction should intuitively be very precise for sparse networks formed by many isolated node doublets, while for denser and more complex connectivity matrices it should provide rather an approximate heuristic estimate (and we should instead analyze the full Jacobian matrix of the system); this dependence may be of interest for the investigation of real data.
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Network analysis of functional brain connectivity in borderline personality disorder using resting-state fMRI

Network analysis of functional brain connectivity in borderline personality disorder using resting-state fMRI

The present study applied graph-theory based complex network analysis and network-based statistic to investigate BPD-related alter- ations of topological organizations and connectivity in resting-state functional brain networks. In the 0.03 – 0.06 Hz functional brain net- works, BPD patients showed increased local cliquishness characterized by increased size of largest connected component, clustering coef fi cient, local ef fi ciency, and small-worldness, particularly at the limbic areas. Patients also showed decreased nodal centrality at several hub nodes, but increased nodal centrality at several non-hub nodes in the network. Furthermore, an interconnected subnetwork in the 0.03 – 0.06 Hz fre- quency band showed signi fi cantly lower connectivity strength in BPD patients, the mean connectivity of which was negatively correlated with the increased topology measures. In addition, the signi fi cant net- work measures were correlated with several clinical symptom scores for BPD diagnosis, and showed high predictive power in patient vs. con- trol classi fi cation using a machine learning classi fi er. The fi ndings of this work may help in gaining new knowledge into the neural under- pinnings of BPD. However, due to limitation of small sample sizes, the re- ported results should be viewed as exploratory and need to be validated on large samples in future works. Future efforts will be directed towards studying functional brain networks constructed with different node sets and connectivity measures, exploring the dynamic network structure across time, and testing the results on a larger sample size. Future work will also be directed towards comparing the topological properties of functional brain networks in different psychiatric disorders, including BPD, obsessive compulsive disorder, and major depressive disorder.
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Brain Connectivity Reflected in Electroencephalogram Coherence in Individuals With Autism: A Meta-analysis

Brain Connectivity Reflected in Electroencephalogram Coherence in Individuals With Autism: A Meta-analysis

A simple measure of connectivity, linear coherence, has been evaluated in most EEG studies. It was first used for representing the connectivity impairments of autism in the 1980s (Cantor et al., 1986). The coherence measure is a function of frequency and explains synchronization between 2 EEG signals of the same frequency. Many pa- pers in the field of autism focus on connectivity issues and report different results. Many reasons justify these differences, such as theoretical models, the measure- ment procedure, and participants’ characteristics. In the following, we refer to the studies used coherence and ob- tained heterogeneous results. In autistic participants, the connectivity between 2 hemispheres during visual tasks is assessed (Isler, Martien, Grieve, Stark, & Herbert, 2010). In this study, coherence measures within the oc- cipital region and between hemispheres were examined; the results indicated that the EEG coherence between 2 hemispheres in individuals with autism was low. In an- other research (Catarino et al., 2013), inter-hemispheric coherence was evaluated, using wavelet coherence, in which children with ASD represented reduced inter- hemispheric coherence.
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The effects of psychosis risk variants on brain connectivity: a review

The effects of psychosis risk variants on brain connectivity: a review

Finally, future studies could benefit from the novel application of recently developed analysis techniques to imaging genetics. For example, DCM hold potential for constructing models of chang- ing brain interactions that also take into account genetic vari- ation (Meyer-Lindenberg, 2009). Other recent advances include the use of parallel ICA to simultaneously analyze independent components derived from fMRI and genetic data (Liu et al., 2009). For example, Meda et al. (2010) used this technique in a pilot study to identify simultaneous independent components of fMRI data and SNP data, derived from a sample of 35 con- trols and 31 SZ patients. The authors found correlations between different neural networks and a number of SNPs, including poly- morphisms involved in altered dopamine transmission. While the authors only included a small number of SNPs and a small sample size, this research suggests a powerful new approach for future studies examining the effects of SZ risk variants on func- tional brain networks. Similarly, more advanced DTI techniques could be implemented that use high angular resolution to account for multiple crossing fibers within a single voxel. Such imag- ing techniques include Q-space approaches and mixture mod- els (Tournier et al., 2011). These models provide mathematical alternatives to the tensor model for the characterization of dif- fusion processes. Furthermore, Jones (2010) recommends the
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Brain functional connectivity and its aberrations in mouse models of autism

Brain functional connectivity and its aberrations in mouse models of autism

”ventral midbrain” module was identified to comprise several ventral brain regions including the amygdala, hypothalamus, and ventral tegmental area (Fig. 2.1A, Mod- ule 5). Finally, thalamic areas emerged as a clearly defined sixth module, although with lower inter-iteration stability (Fig. 2.1A, Module 6). Importantly, the partition- ing of the functional network created from the same rsfMRI dataset upon global sig- nal regression yielded consistent network modules (mean modularity Q = 0.278539, σ = 0.001541), with an increased stability of the thalamic module (Fig. 2.2), cor- roborating the robustness of the methodological approach and overall stability of the identified functional modules. Consistent with human data, the proportion of negative connections in the functional network upon global signal regression was increased from 13 % to 52 % (K. Murphy et al., 2009; Weissenbacher et al., 2009). Correlation analysis of the mean signals from the two cortical modules (DMN and LCN) in global signal regressed rsfMRI timeseries highlighted the presence of robust anticorrelations between these two modules (Fig. 2.3), thus providing additional em- pirical evidence of intrinsic anticorrelations between the two modules, a finding re- cently described in both mice and rats (Schwarz, Natalia Gass, et al., 2013; Sforazzini, Bertero, et al., 2016).
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Time varying brain connectivity with multiregression dynamic models

Time varying brain connectivity with multiregression dynamic models

the Central Limit Theorem, the sum will be more Gaussian of any of its summands, it is possible to infer the correct model by maximising the non-Gaussianity of the residuals (Ramsey et al., 2011; Mumford and Ramsey, 2014). This is the basis of the LiNG Orientation Fixed Structure (LOFS) algorithms of Ramsey et al. (2011, 2014). Of particular relevance are LOFS-R1 and LOFS-R4 (Rule 1 and Rule 4), as these algorithms return graphs which may contain cycles. Given an undirected graph (which may be found with the PC algorithm, or GES or IMaGES), LOFS-R1 considers each node individually, choosing the set of parents (from the adjacent edges in the undirected graph) that maximises a score of non-Gaussianity, e.g. the Anderson-Darling statistic for normality. If an edge cannot be orientated, it can be returned undirected. As can be seen in Figure 1.7d, it is possible for this algorithm to identify cycles and 2-cycles (bidirectional edges). Ramsey et al. (2011) note that a 2-cycle may be due to actual feedback between two nodes, or an unrecorded, latent common cause (or both). LOFS- R4 is a simplified implementation of the LiNG algorithm (see Table 1.1), which does not permit self-loops. Edges which are absent in the undirected graph are replaced by zeros in the coefficient matrix, and the non-Gaussianity is maximised for each row of W ICA (the matrix obtained by independent component analysis) (Lacerda et al., 2012;
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Cytoarchitecure and Connectivity of the Superior Colliculus in Mouse Brain

Cytoarchitecure and Connectivity of the Superior Colliculus in Mouse Brain

changes in the structures that the DHKH expressed when the disease symptoms are manifested [26]. The name of Cpne5 gene is copine V, it is one of the genes which encodes the calcium-dependent protein, a protein that can regulate molecular events and play role in calcium-mediated intracellular processes and the formation of the dendrites (RefSeq, Sep 2015). The gene Cpne5 is expressed in the OP layer and can also be found in other structures (Isocortex, Olfactory areas, Hippocampal formation, cortical subplate, and Pallidum) which have calcium-dependent protein. The name of LOC433258 is attractin-like 1. It is highly expressed in OP and SGR layers, and can also be found in other structures including Isocortex, Olfactory areas, Hippocampal formation, Cortical subplate, Thalamus, Midbrain. For the other four genes (Gabrr1, LOC433228, Mgll and Lats2) that are sparsely expressed in the SC also play an important role in the protein expression. For example, the Gabrr1 (gamma-aminobutyric acid (GABA) C receptor, subunit rho 1), a locus for GABA receptor subunit encoding, has been identified to be slightly expressed in the superficial layers. GABA is the major inhibitory neurotransmitter in the mammalian brain. On the whole, the superior colliculus has a relative higher volume of GABA, while the GABA volume in SGS layer is highest in the central nervous system. GABAc receptors are first found in the retina, but in a few of the brain regions that contain this receptor, SGS is the most strongly labeled [28].
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Connectivity of the Brain from Magnetic Resonance Imaging

Connectivity of the Brain from Magnetic Resonance Imaging

Cortical information processing is highly distributed and the anatomical connectivity pattern shows aspects of small-world and scale-free networks  (Bullmore and Sporns, 2009). In the visual domain, the processing of stimuli proceeds through multiple levels that are characterized by increasing receptive field sizes and by an increase in complexity of the neuronal representations. The organization of feedforward connections is consistent with a partial order of visual areas, whereby higher-level areas, by definition, receive inputs from lower-level areas. Initially, this ordered organization was interpreted as a total hierarchy, whereby each area could be assigned one, globally consistent hierarchy level (Felleman and Van Essen, 1991; Scannell et al., 1995; Hilgetag et al., 2000). However, cortical processing is organized in multiple, parallel streams, suggesting a partial hierarchical rather than a total ordering of areas. Feedback connections play an important and often neglected role in stimulus processing. Feedback connection are, for example, thought to be responsible for the complex shape sensitivity in primary visual cortex (see Hegde and Felleman, 2007).
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The study of brain functional connectivity in Parkinson’s disease

The study of brain functional connectivity in Parkinson’s disease

Studies on functional connectivity have provided import- ant information on PD-related functional and patho- physiological changes. At present, functional connectivity is primarily used to further understand how pathological changes lead to parkinsonian symptoms, and is far from being a method in routine clinical investigations. As shown in the Table 1, the analytic methods of "functional connectivity" studies vary a lot from study to study. Few studies have used the same "functional connectivity" pro- cedure. Therefore, it is hard to perform meta-analysis on these studies. In contrast, voxel-level analysis, like most PET studies and some resting-state fMRI studies focusing the local activity support coordinate-based meta-analysis and, hence, are more helpful to clinical studies. Additional research should focus on increased efforts to develop neural network pattern as a neuroimaging biomarker for early diagnosis of PD; this might well require further tech- nical and methodological improvements. These develop- ments will improve early diagnosis, better evaluate disease progression, differentiate PD from other parkinsonisms on an individual basis, and may guide novel targets for future therapies.
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Whole-brain connectivity analysis and classification of spinocerebellar ataxia type 7 by functional MRI

Whole-brain connectivity analysis and classification of spinocerebellar ataxia type 7 by functional MRI

this study design and the well-defined structural degener- ation in SCA7 we choose to use the univariate approach. Another limitation is the lack of significant correlation with behavioral scores. Only one connection showed a sig- nificant correlation between the functional connectivity and SARA and symptoms onset. There were no significant correlations between these variables and other connec- tions, but there were trends. This outcome can be associ- ated to several variables, for an instance, changes in connectivity appears early in the disease progression and are followed by structural degeneration in a slow fashion [50,51], these difference in the velocity of the progressive degeneration could affect the correlation between vari- ables. Taking into account that SARA score measures the motor impairments as a result of cerebellar dysfunction, and these changes develop slowly compared with the con- nectivity changes, the absence of a good correlation be- tween those variables is not surprising. Something similar could have happened with the CAG expansion and the symptoms onset. In future work we will address those is- sues by analyzing longitudinal data of the same group of patients and by using multivariate approaches as well as behavioral/clinical data, in order to better describe changes of functional connectivity over time.
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Preserved retinotopic brain connectivity in macular degeneration

Preserved retinotopic brain connectivity in macular degeneration

to distinguish connectivity in the polar-angle domain. That said, in our decision to base our analyses on the presented dataset we did not anticipate the possibility that the presence of peripheral stimulation could cause the patient group to exhibit slightly decreased patterns of retinotopic connectivity due to prediction errors generated by instable fixation. This effect would have likely been absent had we based our analysis on pure resting-state scans. However, the fact remains that we could still detect retinotopic connectivity in the patients, and that patients with stable fixation exhibited patterns of connectivity that were indistinguishable from those observed in controls. Thus, our conclusion would have been the same: MD patients still exhibit intact retinotopic connectivity, even after years of visual deprivation.
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Preserved retinotopic brain connectivity in macular degeneration

Preserved retinotopic brain connectivity in macular degeneration

Beside the technical difficulties of restoring the human retina, there are two reasons why visual recovery after pro- longed visual deprivation might be problematic. First, it has been suggested that visual processing in input-deprived visual cortex undergoes large-scale reorganisation in some, but not all patients. 2 That is, in the prolonged absence of visual stimulation, cortical neurons would shift their recep- tive fields toward the portions of the visual field that are still intact, thereby regaining visual sensitivity. Such changes would first need to be reversed before the restored inputs could be processed normally. However, more recent work indicates that large-scale remapping of visual cortex did not occur in a group of 16 MD patients. 3 Second, the long-standing retinal pathology in MD has been associated with reductions in the white- and grey-matter density and volume along the input-deprived visual pathways. 4–6 This suggests that long-term visual deprivation triggers visual cortical degeneration, which may in turn lead to irreversible damage to the visual cortical circuitry (see Prins et al. 7 for a recent review). Thus, while it is largely reassuring that deprived primary visual cortex is generally not remapped, 8–10 the reported anatomical changes in early visual cortex could still have adversely affected the func- tional cortico-cortical connections of the deprived cortex to areas downstream. In turn, this raises the question whether the visual brain would still be able to process appropriately retinal input — were this to be restored.
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The Predictive Brain: A Modular View of Brain and Cognitive Function?

The Predictive Brain: A Modular View of Brain and Cognitive Function?

Each argument for modularity has its own unique and compelling features. Yet we ultimately argue they come up short, albeit for different reasons. If this is on the right track, the result is important because dominating theories of brain and cognitive function speak against what many take to be an established truth; namely, that modularity is at the explanatory basis of our best understanding of brain and cognitive function. Positively put, the results arrived at in this paper present hierarchical message passing in the brain as integrated and recurrent over multiple spatial-temporal scales. From systems neuroscience, we now know that global network dynamics arise from local dynamics, where global dynamics constrain local activity such that the entire brain becomes a self-organising system (Deco et al. 2012). Showing that there are no grounds for thinking that predictive processing yields a modular view of brain and cognitive function provides support for the hypothesis that global network dynamics enslaves activity at lower scales of brain activity. Furthermore, our results highlight that when models of brain and cognitive function is premised on an acyclic Markov decision scheme, utilised by most Bayesian models of brain and cognitive function, it is
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Are animal models predictive for humans?

Are animal models predictive for humans?

Should the above drugs that caused cancer in some ani- mal have been banned? If the null hypothesis is that there is no association between animal carcinogens and human carcinogens strong enough so the animal model can be said to be predictive, then we see much evidence to sup- port the null hypothesis but very little if any to refute it. The point to be made here is that there are scientists (rather more than we have space to mention) who ques- tion the predictive and/or clinical value of animal-based research and history is on their side. As noted above, the opinions of scientists prove nothing in and of itself. Fur- ther, some of what we have presented could be dismissed as anecdotes but this would be a mistake. First, the studies referenced in the previous section are just that, scientific studies not anecdotes. Second, the examples presented are referenced, anecdotes are not (unless they are case reports and we must remember that thalidomide's downfall started as a series of case reports). But again we must ask where the burden of proof lies? We believe the second law of thermodynamics because there has never been an example of where it was wrong. Just one such example would falsify the law. If the animal model community claims the animal model is predictive, then they must explain the examples and studies that reveal it was not. Examples such as case reports count when disproving an assertion, especially when they are then supported with studies, but cannot be used by those making the claim as proof for their overarching hypothesis. That is how sci- ence works. We did not make the rules. In summary there is ample evidence to question, if not disprove entirely, the overarching hypothesis that animal models are predictive for humans.
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Disease association and inter-connectivity analysis of human brain specific co-expressed functional modules

Disease association and inter-connectivity analysis of human brain specific co-expressed functional modules

We identified the HS-COMODs with the largest nor- malized connectivity to the neurobiological modules. More than half of the HS-COMODs with normalized connectivity over than 0.5 were involved in protein modification which was overrepresented functional category in our results (Table  1); especially, they were associated with protein ubiquitination. The concerted activity of the individual players such as the E3 ubiq- uitin ligase and proteasome upon the intra and extra- cellular stimuli might be crucial, as it has been shown that ubiquitin-dependent protein degradation delicately controls the life cycle of synaptic proteins for synapse regulation and organization, and in turn controls learn- ing and memory [5, 6]. Implications of ubiquitin sys- tem have been also reported in previous analyses [7, 8] but their unique characteristics in terms of functional interplay have not been noted. Several E3s, such as AMFR, RNF5, and PARK2, involved in ‘protein ubiq- uitination’, were shown to specifically target the synap- tic proteins involved in, for example, the postsynaptic density (GO:0014069) and nerve–nerve synaptic trans- mission (GO:0007270). The other modules, those that are highly connected to the neurobiological modules such as ‘HIV Infection’, and ‘generation of precursor Fig. 1 Overlap between species‑specific modules and brain disease
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