Publications by authors named "Danielle S Bassett"

260 Publications

Environmental influences on the pace of brain development.

Nat Rev Neurosci 2021 Apr 28. Epub 2021 Apr 28.

Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA.

Childhood socio-economic status (SES), a measure of the availability of material and social resources, is one of the strongest predictors of lifelong well-being. Here we review evidence that experiences associated with childhood SES affect not only the outcome but also the pace of brain development. We argue that higher childhood SES is associated with protracted structural brain development and a prolonged trajectory of functional network segregation, ultimately leading to more efficient cortical networks in adulthood. We hypothesize that greater exposure to chronic stress accelerates brain maturation, whereas greater access to novel positive experiences decelerates maturation. We discuss the impact of variation in the pace of brain development on plasticity and learning. We provide a generative theoretical framework to catalyse future basic science and translational research on environmental influences on brain development.
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http://dx.doi.org/10.1038/s41583-021-00457-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8081006PMC
April 2021

Transdiagnostic dimensions of psychopathology explain individuals' unique deviations from normative neurodevelopment in brain structure.

Transl Psychiatry 2021 Apr 20;11(1):232. Epub 2021 Apr 20.

Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA, 19104, USA.

Psychopathology is rooted in neurodevelopment. However, clinical and biological heterogeneity, together with a focus on case-control approaches, have made it difficult to link dimensions of psychopathology to abnormalities of neurodevelopment. Here, using the Philadelphia Neurodevelopmental Cohort, we built normative models of cortical volume and tested whether deviations from these models better predicted psychiatric symptoms compared to raw cortical volume. Specifically, drawing on the p-factor hypothesis, we distilled 117 clinical symptom measures into six orthogonal psychopathology dimensions: overall psychopathology, anxious-misery, externalizing disorders, fear, positive psychosis symptoms, and negative psychosis symptoms. We found that multivariate patterns of deviations yielded improved out-of-sample prediction of psychopathology dimensions compared to multivariate patterns of raw cortical volume. We also found that correlations between overall psychopathology and deviations in ventromedial prefrontal, inferior temporal, and dorsal anterior cingulate cortices were stronger than those observed for specific dimensions of psychopathology (e.g., anxious-misery). Notably, these same regions are consistently implicated in a range of putatively distinct disorders. Finally, we performed conventional case-control comparisons of deviations in a group of individuals with depression and a group with attention-deficit hyperactivity disorder (ADHD). We observed spatially overlapping effects between these groups that diminished when controlling for overall psychopathology. Together, our results suggest that modeling cortical brain features as deviations from normative neurodevelopment improves prediction of psychiatric symptoms in out-of-sample testing, and that p-factor models of psychopathology may assist in separating biomarkers that are disorder-general from those that are disorder-specific.
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http://dx.doi.org/10.1038/s41398-021-01342-6DOI Listing
April 2021

Atypical neural topographies underpin dysfunctional pattern separation in temporal lobe epilepsy.

Brain 2021 Mar 17. Epub 2021 Mar 17.

Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.

Episodic memory is the ability to accurately remember events from our past. The process of pattern separation is hypothesized to underpin this ability and is defined as the ability to orthogonalize memory traces, to maximize the features that make them unique. Contemporary cognitive neuroscience suggests that pattern separation entails complex interactions between the hippocampus and the neocortex, where specific hippocampal subregions shape neural reinstatement in the neocortex. To test this hypothesis, the current work studied both healthy controls and patients with temporal lobe epilepsy (TLE) who present with hippocampal structural anomalies. In all participants, we measured neural activity using functional magnetic resonance imaging (fMRI) while they retrieved memorized items compared to lure items which share features with the target. Behaviorally, TLE patients were less able to exclude lures than controls, and showed a reduction in pattern separation. To assess the hypothesized relationship between neural patterns in the hippocampus and the neocortex, we identified topographic gradients of intrinsic connectivity along neocortical and hippocampal subfield surfaces and identified the topographic profile of the neural activity accompanying pattern separation. In healthy controls, pattern separation followed a graded pattern of neural activity, both along the hippocampal long axis (and peaked in anterior segments that are more heavily engaged in transmodal processing) and along the neocortical hierarchy running from unimodal to transmodal regions (peaking in transmodal default mode regions). In TLE patients, however, this concordance between task-based functional activations and topographic gradients was markedly reduced. Furthermore, person specific measures of concordance between task-related activity and connectivity gradients in patients and controls related to inter-individual differences in behavioral measures of pattern separation and episodic memory, highlighting the functional relevance of the observed topographic motifs. Our work is consistent with an emerging understanding that successful discrimination between memories with similar features entails a shift in the locus of neural activity away from sensory systems, a pattern that is mirrored along the hippocampal long axis and with respect to neocortical hierarchies. More broadly, our study establishes topographic profiling using intrinsic connectivity gradients captures the functional underpinnings of episodic memory processes in manner that is sensitive to their reorganization in pathology.
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http://dx.doi.org/10.1093/brain/awab121DOI Listing
March 2021

BCI learning induces core-periphery reorganization in M/EEG multiplex brain networks.

J Neural Eng 2021 Mar 16. Epub 2021 Mar 16.

Inria, 47, boulevard de l'Hôpital, Paris, 75013, FRANCE.

Brain-computer interfaces (BCIs) constitute a promising tool for communication and control. However, mastering non-invasive closed-loop systems remains a learned skill that is difficult to develop for a non-negligible proportion of users. The involved learning process induces neural changes associated with a brain network reorganization that remains poorly understood. To address this inter-subject variability, we adopted a multilayer approach to integrate brain network properties from electroencephalographic (EEG) and magnetoencephalographic (MEG) data resulting from a four-session BCI training program followed by a group of healthy subjects. Our method gives access to the contribution of each layer to multilayer network that tends to be equal with time. We show that regardless the chosen modality, a progressive increase in the integration of somatosensory areas in the α band was paralleled by a decrease of the integration of visual processing and working memory areas in the β band. Notably, only brain network properties in multilayer network correlated with future BCI scores in the α2 band: positively in somatosensory and decision-making related areas and negatively in associative areas. Our findings cast new light on neural processes underlying BCI training. Integrating multimodal brain network properties provides new information that correlates with behavioral performance and could be considered as a potential marker of BCI learning.
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http://dx.doi.org/10.1088/1741-2552/abef39DOI Listing
March 2021

Data-driven control of complex networks.

Nat Commun 2021 03 3;12(1):1429. Epub 2021 Mar 3.

Department of Mechanical Engineering, University of California at Riverside, Riverside, CA, USA.

Our ability to manipulate the behavior of complex networks depends on the design of efficient control algorithms and, critically, on the availability of an accurate and tractable model of the network dynamics. While the design of control algorithms for network systems has seen notable advances in the past few years, knowledge of the network dynamics is a ubiquitous assumption that is difficult to satisfy in practice. In this paper we overcome this limitation, and develop a data-driven framework to control a complex network optimally and without any knowledge of the network dynamics. Our optimal controls are constructed using a finite set of data, where the unknown network is stimulated with arbitrary and possibly random inputs. Although our controls are provably correct for networks with linear dynamics, we also characterize their performance against noisy data and in the presence of nonlinear dynamics, as they arise in power grid and brain networks.
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http://dx.doi.org/10.1038/s41467-021-21554-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7930026PMC
March 2021

Pairwise maximum entropy model explains the role of white matter structure in shaping emergent co-activation states.

Commun Biol 2021 Feb 16;4(1):210. Epub 2021 Feb 16.

Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA.

A major challenge in neuroscience is determining a quantitative relationship between the brain's white matter structural connectivity and emergent activity. We seek to uncover the intrinsic relationship among brain regions fundamental to their functional activity by constructing a pairwise maximum entropy model (MEM) of the inter-ictal activation patterns of five patients with medically refractory epilepsy over an average of ~14 hours of band-passed intracranial EEG (iEEG) recordings per patient. We find that the pairwise MEM accurately predicts iEEG electrodes' activation patterns' probability and their pairwise correlations. We demonstrate that the estimated pairwise MEM's interaction weights predict structural connectivity and its strength over several frequencies significantly beyond what is expected based solely on sampled regions' distance in most patients. Together, the pairwise MEM offers a framework for explaining iEEG functional connectivity and provides insight into how the brain's structural connectome gives rise to large-scale activation patterns by promoting co-activation between connected structures.
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http://dx.doi.org/10.1038/s42003-021-01700-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7887247PMC
February 2021

Structural control energy of resting-state functional brain states reveals less cost-effective brain dynamics in psychosis vulnerability.

Hum Brain Mapp 2021 May 10;42(7):2181-2200. Epub 2021 Feb 10.

Medical Image Processing Laboratory, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.

How the brain's white-matter anatomy constrains brain activity is an open question that might give insights into the mechanisms that underlie mental disorders such as schizophrenia. Chromosome 22q11.2 deletion syndrome (22q11DS) is a neurodevelopmental disorder with an extremely high risk for psychosis providing a test case to study developmental aspects of schizophrenia. In this study, we used principles from network control theory to probe the implications of aberrant structural connectivity for the brain's functional dynamics in 22q11DS. We retrieved brain states from resting-state functional magnetic resonance images of 78 patients with 22q11DS and 85 healthy controls. Then, we compared them in terms of persistence control energy; that is, the control energy that would be required to persist in each of these states based on individual structural connectivity and a dynamic model. Persistence control energy was altered in a broad pattern of brain states including both energetically more demanding and less demanding brain states in 22q11DS. Further, we found a negative relationship between persistence control energy and resting-state activation time, which suggests that the brain reduces energy by spending less time in energetically demanding brain states. In patients with 22q11DS, this behavior was less pronounced, suggesting a deficiency in the ability to reduce energy through brain activation. In summary, our results provide initial insights into the functional implications of altered structural connectivity in 22q11DS, which might improve our understanding of the mechanisms underlying the disease.
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http://dx.doi.org/10.1002/hbm.25358DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8046160PMC
May 2021

Multimodal in vivo recording using transparent graphene microelectrodes illuminates spatiotemporal seizure dynamics at the microscale.

Commun Biol 2021 Jan 29;4(1):136. Epub 2021 Jan 29.

Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA.

Neurological disorders such as epilepsy arise from disrupted brain networks. Our capacity to treat these disorders is limited by our inability to map these networks at sufficient temporal and spatial scales to target interventions. Current best techniques either sample broad areas at low temporal resolution (e.g. calcium imaging) or record from discrete regions at high temporal resolution (e.g. electrophysiology). This limitation hampers our ability to understand and intervene in aberrations of network dynamics. Here we present a technique to map the onset and spatiotemporal spread of acute epileptic seizures in vivo by simultaneously recording high bandwidth microelectrocorticography and calcium fluorescence using transparent graphene microelectrode arrays. We integrate dynamic data features from both modalities using non-negative matrix factorization to identify sequential spatiotemporal patterns of seizure onset and evolution, revealing how the temporal progression of ictal electrophysiology is linked to the spatial evolution of the recruited seizure core. This integrated analysis of multimodal data reveals otherwise hidden state transitions in the spatial and temporal progression of acute seizures. The techniques demonstrated here may enable future targeted therapeutic interventions and novel spatially embedded models of local circuit dynamics during seizure onset and evolution.
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http://dx.doi.org/10.1038/s42003-021-01670-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7846732PMC
January 2021

The feasibility of an in-scanner smoking lapse paradigm to examine the neural correlates of lapses.

Addict Biol 2021 Jan 28:e13001. Epub 2021 Jan 28.

Department of Psychology, The Pennsylvania State University, State College, Pennsylvania, USA.

Quitting smoking is notoriously difficult. Models of nicotine dependence posit that strength of cognitive control contributes to maintaining smoking abstinence during smoking cessation attempts. We examine the role for large-scale functional brain systems associated with cognitive control in smoking lapse using a novel adaption of a well-validated behavioral paradigm. We use data from 17 daily smokers (five females) after 12 h of smoking abstinence. Participants completed up to 10 sequential 5-min functional magnetic resonance imaging (fMRI) runs, within a single scanning session. After each run, participants decided whether to stay in the scanner in order to earn additional money or to terminate the session in order to smoke a cigarette (i.e., lapse) and forego additional monetary reward. Cox regression results indicate that decreased segregation of the default mode system from the frontoparietal system undermines the ability to resist smoking. This study demonstrates the feasibility of modifying an established behavioral model of smoking lapse behavior for use in the neuro imaging environment, and it provides initial evidence that this approach yields valuable information regarding fine-grained, time-varying changes in patterns of neural activity in the moments leading up to a decision to smoke. Specifically, results lend support to the hypothesis that the time-varying interplay between large-scale functional brain systems associated with cognitive control is implicated in smoking lapse behavior.
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http://dx.doi.org/10.1111/adb.13001DOI Listing
January 2021

Time-evolving controllability of effective connectivity networks during seizure progression.

Proc Natl Acad Sci U S A 2021 Feb;118(5)

Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104;

Over one third of the estimated 3 million people with epilepsy in the United States are medication resistant. Responsive neurostimulation from chronically implanted electrodes provides a promising treatment alternative to resective surgery. However, determining optimal personalized stimulation parameters, including when and where to intervene to guarantee a positive patient outcome, is a major open challenge. Network neuroscience and control theory offer useful tools that may guide improvements in parameter selection for control of anomalous neural activity. Here we use a method to characterize dynamic controllability across consecutive effective connectivity (EC) networks based on regularized partial correlations between implanted electrodes during the onset, propagation, and termination regimes of 34 seizures. We estimate regularized partial correlation adjacency matrices from 1-s time windows of intracranial electrocorticography recordings using the Graphical Least Absolute Shrinkage and Selection Operator (GLASSO). Average and modal controllability metrics calculated from each resulting EC network track the time-varying controllability of the brain on an evolving landscape of conditionally dependent network interactions. We show that average controllability increases throughout a seizure and is negatively correlated with modal controllability throughout. Our results support the hypothesis that the energy required to drive the brain to a seizure-free state from an ictal state is smallest during seizure onset, yet we find that applying control energy at electrodes in the seizure onset zone may not always be energetically favorable. Our work suggests that a low-complexity model of time-evolving controllability may offer insights for developing and improving control strategies targeting seizure suppression.
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http://dx.doi.org/10.1073/pnas.2006436118DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7865160PMC
February 2021

Hunters, busybodies and the knowledge network building associated with deprivation curiosity.

Nat Hum Behav 2021 03 30;5(3):327-336. Epub 2020 Nov 30.

Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA, USA.

The open-ended and internally driven nature of curiosity makes characterizing the information seeking that accompanies it a daunting endeavour. We use a historico-philosophical taxonomy of information seeking coupled with a knowledge network building framework to capture styles of information-seeking in 149 participants as they explore Wikipedia for over 5 hours spanning 21 days. We create knowledge networks in which nodes represent distinct concepts and edges represent the similarity between concepts. We quantify the tightness of knowledge networks using graph theoretical indices and use a generative model of network growth to explore mechanisms underlying information-seeking. Deprivation curiosity (the tendency to seek information that eliminates knowledge gaps) is associated with the creation of relatively tight networks and a relatively greater tendency to return to previously visited concepts. With this framework in hand, future research can readily quantify the information seeking associated with curiosity.
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http://dx.doi.org/10.1038/s41562-020-00985-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8082236PMC
March 2021

Temporal networks of tobacco withdrawal symptoms during smoking cessation treatment.

J Abnorm Psychol 2021 Jan 30;130(1):89-101. Epub 2020 Nov 30.

Department of Bioengineering.

A recently developed network perspective on tobacco withdrawal posits that withdrawal symptoms causally influence one another across time, rather than simply being indicators of a latent syndrome. Evidence supporting a network perspective would shift the focus of tobacco withdrawal research and intervention toward studying and treating individual withdrawal symptoms and intersymptom associations. Here we construct and examine temporal tobacco withdrawal networks that describe the interplay among withdrawal symptoms across time using experience-sampling data from 1,210 participants (58.35% female, 86.24% White) undergoing smoking cessation treatment. We also construct person-specific withdrawal networks and capture individual differences in the extent to which withdrawal symptom networks promote the spread of symptom activity through the network across time using impulse response analysis. Results indicate substantial moment-to-moment associations among withdrawal symptoms, substantial between-person differences in withdrawal network structure, and reductions in the interplay among withdrawal symptoms during combination smoking cessation treatment. Overall, findings suggest the utility of a network perspective and also highlight challenges associated with the network approach stemming from vast between-person differences in symptom networks. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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http://dx.doi.org/10.1037/abn0000650DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7818515PMC
January 2021

Towards precise resting-state fMRI biomarkers in psychiatry: synthesizing developments in transdiagnostic research, dimensional models of psychopathology, and normative neurodevelopment.

Curr Opin Neurobiol 2020 12 23;65:120-128. Epub 2020 Nov 23.

Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Electrical & Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA; Santa Fe Institute, Santa Fe, NM 87501, USA. Electronic address:

Searching for biomarkers has been a chief pursuit of the field of psychiatry. Toward this end, studies have catalogued candidate resting-state biomarkers in nearly all forms of mental disorder. However, it is becoming increasingly clear that these biomarkers lack specificity, limiting their capacity to yield clinical impact. We discuss three avenues of research that are overcoming this limitation: (i) the adoption of transdiagnostic research designs, which involve studying and explicitly comparing multiple disorders from distinct diagnostic axes of psychiatry; (ii) dimensional models of psychopathology that map the full spectrum of symptomatology and that cut across traditional disorder boundaries; and (iii) modeling individuals' unique functional connectomes throughout development. We provide a framework for tying these subfields together that draws on tools from machine learning and network science.
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http://dx.doi.org/10.1016/j.conb.2020.10.016DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7770086PMC
December 2020

How humans learn and represent networks.

Proc Natl Acad Sci U S A 2020 11;117(47):29407-29415

Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA 19104;

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http://dx.doi.org/10.1073/pnas.1912328117DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7703562PMC
November 2020

The brain produces mind by modeling.

Proc Natl Acad Sci U S A 2020 11;117(47):29299-29301

Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139-4307.

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http://dx.doi.org/10.1073/pnas.1912340117DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7703556PMC
November 2020

Models of communication and control for brain networks: distinctions, convergence, and future outlook.

Netw Neurosci 2020 1;4(4):1122-1159. Epub 2020 Nov 1.

Department of Bioengineering, University of Pennsylvania, Philadelphia, PA USA.

Recent advances in computational models of signal propagation and routing in the human brain have underscored the critical role of white-matter structure. A complementary approach has utilized the framework of network control theory to better understand how white matter constrains the manner in which a region or set of regions can direct or control the activity of other regions. Despite the potential for both of these approaches to enhance our understanding of the role of network structure in brain function, little work has sought to understand the relations between them. Here, we seek to explicitly bridge computational models of communication and principles of network control in a conceptual review of the current literature. By drawing comparisons between communication and control models in terms of the level of abstraction, the dynamical complexity, the dependence on network attributes, and the interplay of multiple spatiotemporal scales, we highlight the convergence of and distinctions between the two frameworks. Based on the understanding of the intertwined nature of communication and control in human brain networks, this work provides an integrative perspective for the field and outlines exciting directions for future work.
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http://dx.doi.org/10.1162/netn_a_00158DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7655113PMC
November 2020

Path-dependent connectivity, not modularity, consistently predicts controllability of structural brain networks.

Netw Neurosci 2020 1;4(4):1091-1121. Epub 2020 Nov 1.

Department of Bioengineering, University of Pennsylvania, Philadelphia, PA USA.

The human brain displays rich communication dynamics that are thought to be particularly well-reflected in its marked community structure. Yet, the precise relationship between community structure in structural brain networks and the communication dynamics that can emerge therefrom is not well understood. In addition to offering insight into the structure-function relationship of networked systems, such an understanding is a critical step toward the ability to manipulate the brain's large-scale dynamical activity in a targeted manner. We investigate the role of community structure in the controllability of structural brain networks. At the region level, we find that certain network measures of community structure are sometimes statistically correlated with measures of linear controllability. However, we then demonstrate that this relationship depends on the distribution of network edge weights. We highlight the complexity of the relationship between community structure and controllability by performing numerical simulations using canonical graph models with varying mesoscale architectures and edge weight distributions. Finally, we demonstrate that , a measure rooted in the graph spectrum, and which captures higher order graph architecture, is a stronger and more consistent predictor of controllability. Our study contributes to an understanding of how the brain's diverse mesoscale structure supports transient communication dynamics.
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http://dx.doi.org/10.1162/netn_a_00157DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7655114PMC
November 2020

Generative network models of altered structural brain connectivity in schizophrenia.

Neuroimage 2021 01 5;225:117510. Epub 2020 Nov 5.

Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, J5 68159 Mannheim, Germany.

Alterations in the structural connectome of schizophrenia patients have been widely characterized, but the mechanisms remain largely unknown. Generative network models have recently been introduced as a tool to test the biological underpinnings of altered brain network formation. We evaluated different generative network models in healthy controls (n=152), schizophrenia patients (n=66), and their unaffected first-degree relatives (n=32), and we identified spatial and topological factors contributing to network formation. We further investigated how these factors relate to cognition and to polygenic risk for schizophrenia. Our data show that among the four tested classes of generative network models, structural brain networks were optimally accounted for by a two-factor model combining spatial constraints and topological neighborhood structure. The same wiring model explained brain network formation across study groups. However, relatives and schizophrenia patients exhibited significantly lower spatial constraints and lower topological facilitation compared to healthy controls. Further exploratory analyses point to potential associations of the model parameter reflecting spatial constraints with the polygenic risk for schizophrenia and cognitive performance. Our results identify spatial constraints and local topological structure as two interrelated mechanisms contributing to regular brain network formation as well as altered connectomes in schizophrenia and healthy individuals at familial risk for schizophrenia. On an exploratory level, our data further point to the potential relevance of spatial constraints for the genetic risk for schizophrenia and general cognitive functioning, thereby encouraging future studies in following up on these observations to gain further insights into the biological basis and behavioral relevance of model parameters.
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http://dx.doi.org/10.1016/j.neuroimage.2020.117510DOI Listing
January 2021

Network structure of cascading neural systems predicts stimulus propagation and recovery.

J Neural Eng 2020 11 4;17(5):056045. Epub 2020 Nov 4.

Neuroscience Graduate Group, University of Pennsylvania, Philadelphia, PA 19104, United States of America.

Objective: Many neural systems display spontaneous, spatiotemporal patterns of neural activity that are crucial for information processing. While these cascading patterns presumably arise from the underlying network of synaptic connections between neurons, the precise contribution of the network's local and global connectivity to these patterns and information processing remains largely unknown.

Approach: Here, we demonstrate how network structure supports information processing through network dynamics in empirical and simulated spiking neurons using mathematical tools from linear systems theory, network control theory, and information theory.

Main Results: In particular, we show that activity, and the information that it contains, travels through cycles in real and simulated networks.

Significance: Broadly, our results demonstrate how cascading neural networks could contribute to cognitive faculties that require lasting activation of neuronal patterns, such as working memory or attention.
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http://dx.doi.org/10.1088/1741-2552/abbff1DOI Listing
November 2020

Neurocognitive and functional heterogeneity in depressed youth.

Neuropsychopharmacology 2021 03 2;46(4):783-790. Epub 2020 Oct 2.

Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.

Depression is a common psychiatric illness that often begins in youth, and is sometimes associated with cognitive deficits. However, there is significant variability in cognitive dysfunction, likely reflecting biological heterogeneity. We sought to identify neurocognitive subtypes and their neurofunctional signatures in a large cross-sectional sample of depressed youth. Participants were drawn from the Philadelphia Neurodevelopmental Cohort, including 712 youth with a lifetime history of a major depressive episode and 712 typically developing (TD) youth matched on age and sex. A subset (MDD n = 368, TD n = 200) also completed neuroimaging. Cognition was assessed with the Penn Computerized Neurocognitive Battery. A recently developed semi-supervised machine learning algorithm was used to delineate neurocognitive subtypes. Subtypes were evaluated for differences in both clinical psychopathology and brain activation during an n-back working memory fMRI task. We identified three neurocognitive subtypes in the depressed group. Subtype 1 was high-performing (high accuracy, moderate speed), Subtype 2 was cognitively impaired (low accuracy, slow speed), and Subtype 3 was impulsive (low accuracy, fast speed). While subtypes did not differ in clinical psychopathology, they diverged in their activation profiles in regions critical for executive function, which mirrored differences in cognition. Taken together, these data suggest disparate mechanisms of cognitive vulnerability and resilience in depressed youth, which may inform the identification of biomarkers for prognosis and treatment response.
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http://dx.doi.org/10.1038/s41386-020-00871-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8027806PMC
March 2021

Relations between large-scale brain connectivity and effects of regional stimulation depend on collective dynamical state.

PLoS Comput Biol 2020 09 4;16(9):e1008144. Epub 2020 Sep 4.

Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.

At the macroscale, the brain operates as a network of interconnected neuronal populations, which display coordinated rhythmic dynamics that support interareal communication. Understanding how stimulation of different brain areas impacts such activity is important for gaining basic insights into brain function and for further developing therapeutic neurmodulation. However, the complexity of brain structure and dynamics hinders predictions regarding the downstream effects of focal stimulation. More specifically, little is known about how the collective oscillatory regime of brain network activity-in concert with network structure-affects the outcomes of perturbations. Here, we combine human connectome data and biophysical modeling to begin filling these gaps. By tuning parameters that control collective system dynamics, we identify distinct states of simulated brain activity and investigate how the distributed effects of stimulation manifest at different dynamical working points. When baseline oscillations are weak, the stimulated area exhibits enhanced power and frequency, and due to network interactions, activity in this excited frequency band propagates to nearby regions. Notably, beyond these linear effects, we further find that focal stimulation causes more distributed modifications to interareal coherence in a band containing regions' baseline oscillation frequencies. Importantly, depending on the dynamical state of the system, these broadband effects can be better predicted by functional rather than structural connectivity, emphasizing a complex interplay between anatomical organization, dynamics, and response to perturbation. In contrast, when the network operates in a regime of strong regional oscillations, stimulation causes only slight shifts in power and frequency, and structural connectivity becomes most predictive of stimulation-induced changes in network activity patterns. In sum, this work builds upon and extends previous computational studies investigating the impacts of stimulation, and underscores the fact that both the stimulation site, and, crucially, the regime of brain network dynamics, can influence the network-wide responses to local perturbations.
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http://dx.doi.org/10.1371/journal.pcbi.1008144DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7537889PMC
September 2020

Reconfigurations within resonating communities of brain regions following TMS reveal different scales of processing.

Netw Neurosci 2020 1;4(3):611-636. Epub 2020 Jul 1.

U.S. Army CCDC Army Research Laboratory, Aberdeen Proving Ground, MD, USA.

An overarching goal of neuroscience research is to understand how heterogeneous neuronal ensembles cohere into networks of coordinated activity to support cognition. To investigate how local activity harmonizes with global signals, we measured electroencephalography (EEG) while single pulses of transcranial magnetic stimulation (TMS) perturbed occipital and parietal cortices. We estimate the rapid network reconfigurations in dynamic network communities within specific frequency bands of the EEG, and characterize two distinct features of network reconfiguration, flexibility and allegiance, among spatially distributed neural sources following TMS. Using distance from the stimulation site to infer local and global effects, we find that alpha activity (8-12 Hz) reflects concurrent local and global effects on network dynamics. Pairwise allegiance of brain regions to communities on average increased near the stimulation site, whereas TMS-induced changes to flexibility were generally invariant to distance and stimulation site. In contrast, communities within the beta (13-20 Hz) band demonstrated a high level of spatial specificity, particularly within a cluster comprising paracentral areas. Together, these results suggest that focal magnetic neurostimulation to distinct cortical sites can help identify both local and global effects on brain network dynamics, and highlight fundamental differences in the manifestation of network reconfigurations within alpha and beta frequency bands.
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http://dx.doi.org/10.1162/netn_a_00139DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7462427PMC
July 2020

Reflections on the past two decades of neuroscience.

Nat Rev Neurosci 2020 10 2;21(10):524-534. Epub 2020 Sep 2.

Department of Systems Pharmacology, The University of Tokyo, Tokyo, Japan.

The first issue of Nature Reviews Neuroscience was published 20 years ago, in 2000. To mark this anniversary, in this Viewpoint article we asked a selection of researchers from across the field who have authored pieces published in the journal in recent years for their thoughts on notable and interesting developments in neuroscience, and particularly in their areas of the field, over the past two decades. They also provide some thoughts on current lines of research and questions that excite them.
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http://dx.doi.org/10.1038/s41583-020-0363-6DOI Listing
October 2020

Modeling brain, symptom, and behavior in the winds of change.

Neuropsychopharmacology 2021 01 28;46(1):20-32. Epub 2020 Aug 28.

Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.

Neuropsychopharmacology addresses pressing questions in the study of three intertwined complex systems: the brain, human behavior, and symptoms of illness. The field seeks to understand the perturbations that impinge upon those systems, either driving greater health or illness. In the pursuit of this aim, investigators often perform analyses that make certain assumptions about the nature of the systems that are being perturbed. Those assumptions can be encoded in powerful computational models that serve to bridge the wide gulf between a descriptive analysis and a formal theory of a system's response. Here we review a set of three such models along a continuum of complexity, moving from a local treatment to a network treatment: one commonly applied form of the general linear model, impulse response models, and network control models. For each, we describe the model's basic form, review its use in the field, and provide a frank assessment of its relative strengths and weaknesses. The discussion naturally motivates future efforts to interlink data analysis, computational modeling, and formal theory. Our goal is to inspire practitioners to consider the assumptions implicit in their analytical approach, align those assumptions to the complexity of the systems under study, and take advantage of exciting recent advances in modeling the relations between perturbations and system function.
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http://dx.doi.org/10.1038/s41386-020-00805-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7689481PMC
January 2021

Thalamus and focal to bilateral seizures: A multiscale cognitive imaging study.

Neurology 2020 10 26;95(17):e2427-e2441. Epub 2020 Aug 26.

From the Department of Clinical and Experimental Epilepsy (L.C., L.A.A., K.T., S.B.V., M.C., M.G., M.K.S., P.J.T., G.P.W., J.S.D., M.J.K.) and Neuroradiological Academic Unit (S.B.V.), UCL Queen Square Institute of Neurology, London; MRI Unit (L.C., L.A.A., K.T., S.B.V., M.C., M.G., M.K.S., P.J.T., G.P.W., J.S.D., M.J.K.), Epilepsy Society, Chalfont St Peter, Buckinghamshire, UK; Departments of Bioengineering (L.C., X.H., D.S.B.), Physics and Astronomy (D.S.B.), Electrical and Systems Engineering (D.S.B.), Neurology (D.S.B.), and Psychiatry (D.S.B.), University of Pennsylvania, Philadelphia; Department of Neurology (K.T.), Medical University of Vienna, Austria; Centre for Medical Image Computing (S.B.V.), University College London, UK; Department of Neurology (M.G.), University Hospital Zurich, Switzerland; Santa Fe Institute (D.S.B.), NM; Department of Medicine, Division of Neurology (G.P.W.), Queen's University, Kingston, Canada; and Department of Neurology (M.R.S.), Thomas Jefferson University, Philadelphia, PA.

Objective: To investigate the functional correlates of recurrent secondarily generalized seizures in temporal lobe epilepsy (TLE) using task-based fMRI as a framework to test for epilepsy-specific network rearrangements. Because the thalamus modulates propagation of temporal lobe onset seizures and promotes cortical synchronization during cognition, we hypothesized that occurrence of secondarily generalized seizures, i.e., focal to bilateral tonic-clonic seizures (FBTCS), would relate to thalamic dysfunction, altered connectivity, and whole-brain network centrality.

Methods: FBTCS occur in a third of patients with TLE and are a major determinant of disease severity. In this cross-sectional study, we analyzed 113 patients with drug-resistant TLE (55 left/58 right), who performed a verbal fluency fMRI task that elicited robust thalamic activation. Thirty-three patients (29%) had experienced at least one FBTCS in the year preceding the investigation. We compared patients with TLE-FBTCS to those without FBTCS via a multiscale approach, entailing analysis of statistical parametric mapping (SPM) 12-derived measures of activation, task-modulated thalamic functional connectivity (psychophysiologic interaction), and graph-theoretical metrics of centrality.

Results: Individuals with TLE-FBTCS had less task-related activation of bilateral thalamus, with left-sided emphasis, and left hippocampus than those without FBTCS. In TLE-FBTCS, we also found greater task-related thalamotemporal and thalamomotor connectivity, and higher thalamic degree and betweenness centrality. Receiver operating characteristic curves, based on a combined thalamic functional marker, accurately discriminated individuals with and without FBTCS.

Conclusions: In TLE-FBTCS, impaired task-related thalamic recruitment coexists with enhanced thalamotemporal connectivity and whole-brain thalamic network embedding. Altered thalamic functional profiles are proposed as imaging biomarkers of active secondary generalization.
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http://dx.doi.org/10.1212/WNL.0000000000010645DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7682917PMC
October 2020

The architecture of co-morbidity networks of physical and mental health conditions in military veterans.

Proc Math Phys Eng Sci 2020 Jul 1;476(2239):20190790. Epub 2020 Jul 1.

US Department of Veterans Affairs (VA) Connecticut Healthcare System, West Haven, CT, USA.

Co-morbidity between medical and psychiatric conditions is commonly considered between individual pairs of conditions. However, an important alternative is to consider all conditions as part of a co-morbidity network, which encompasses all interactions between patients and a healthcare system. Analysis of co-morbidity networks could detect and quantify general tendencies not observed by smaller-scale studies. Here, we investigate the co-morbidity network derived from longitudinal healthcare records from approximately 1 million United States military veterans, a population disproportionately impacted by psychiatric morbidity and psychological trauma. Network analyses revealed marked and heterogenous patterns of co-morbidity, including a multi-scale community structure composed of groups of commonly co-morbid conditions. Psychiatric conditions including posttraumatic stress disorder were strong predictors of future medical morbidity. Neurological conditions and conditions associated with chronic pain were particularly highly co-morbid with psychiatric conditions. Across conditions, the degree of co-morbidity was positively associated with mortality. Co-morbidity was modified by biological sex and could be used to predict future diagnostic status, with out-of-sample prediction accuracy of 90-92%. Understanding complex patterns of disease co-morbidity has the potential to lead to improved designs of systems of care and the development of targeted interventions that consider the broader context of mental and physical health.
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http://dx.doi.org/10.1098/rspa.2019.0790DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7426059PMC
July 2020

Architecture and evolution of semantic networks in mathematics texts.

Proc Math Phys Eng Sci 2020 Jul 29;476(2239):20190741. Epub 2020 Jul 29.

Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA.

Knowledge is a network of interconnected concepts. Yet, precisely how the topological structure of knowledge constrains its acquisition remains unknown, hampering the development of learning enhancement strategies. Here, we study the topological structure of semantic networks reflecting mathematical concepts and their relations in college-level linear algebra texts. We hypothesize that these networks will exhibit structural order, reflecting the logical sequence of topics that ensures accessibility. We find that the networks exhibit strong core-periphery architecture, where a dense core of concepts presented early is complemented with a sparse periphery presented evenly throughout the exposition; the latter is composed of many small modules each reflecting more narrow domains. Using tools from applied topology, we find that the expositional evolution of the semantic networks produces and subsequently fills knowledge gaps, and that the density of these gaps tracks negatively with community ratings of each textbook. Broadly, our study lays the groundwork for future efforts developing optimal design principles for textbook exposition and teaching in a classroom setting.
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http://dx.doi.org/10.1098/rspa.2019.0741DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7426037PMC
July 2020

The Citation Diversity Statement: A Practice of Transparency, A Way of Life.

Trends Cogn Sci 2020 09;24(9):669-672

Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA. Electronic address:

Appending a Citation Diversity Statement to a paper is a simple and effective way to increase awareness about citation bias and help mitigate it. Here, we describe why reducing citation bias is important and how to include a Citation Diversity Statement in your next publication.
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http://dx.doi.org/10.1016/j.tics.2020.06.009DOI Listing
September 2020

Response inhibition in adolescents is moderated by brain connectivity and social network structure.

Soc Cogn Affect Neurosci 2020 10;15(8):827-837

Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.

The social environment an individual is embedded in influences their ability and motivation to engage self-control processes, but little is known about the neural mechanisms underlying this effect. Many individuals successfully regulate their behavior even when they do not show strong activation in canonical self-control brain regions. Thus, individuals may rely on other resources to compensate, including daily experiences navigating and managing complex social relationships that likely bolster self-control processes. Here, we employed a network neuroscience approach to investigate the role of social context and social brain systems in facilitating self-control in adolescents. We measured brain activation using functional magnetic resonance imaging (fMRI) as 62 adolescents completed a Go/No-Go response inhibition task. We found that self-referential brain systems compensate for weaker activation in executive function brain systems, especially for adolescents with more friends and more communities in their social networks. Collectively, our results indicate a critical role for self-referential brain systems during the developmental trajectory of self-control throughout adolescence.
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http://dx.doi.org/10.1093/scan/nsaa109DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7543938PMC
October 2020

Defining and predicting transdiagnostic categories of neurodegenerative disease.

Nat Biomed Eng 2020 08 3;4(8):787-800. Epub 2020 Aug 3.

Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA.

The prevalence of concomitant proteinopathies and heterogeneous clinical symptoms in neurodegenerative diseases hinders the identification of individuals who might be candidates for a particular intervention. Here, by applying an unsupervised clustering algorithm to post-mortem histopathological data from 895 patients with degeneration in the central nervous system, we show that six non-overlapping disease clusters can simultaneously account for tau neurofibrillary tangles, α-synuclein inclusions, neuritic plaques, inclusions of the transcriptional repressor TDP-43, angiopathy, neuron loss and gliosis. We also show that membership to the six transdiagnostic disease clusters, which explains more variance in cognitive phenotypes than can be explained by individual diagnoses, can be accurately predicted from scores of the Mini-Mental Status Exam, protein levels in cerebrospinal fluid, and genotype at the APOE and MAPT loci, via cross-validated multiple logistic regression. This combination of unsupervised and supervised data-driven tools provides a framework that could be used to identify latent disease subtypes in other areas of medicine.
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http://dx.doi.org/10.1038/s41551-020-0593-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7946378PMC
August 2020