Dani S. Bassett’s research while affiliated with McGill University and other places

The brain is immensely complex, with diverse components and dynamic interactions building upon one another to orchestrate a wide range of behaviors. Understanding the patterns of these complex interactions and how they are coordinated to support collective neural function is critical for parsing human and animal behavior, treating mental illness, and developing artificial intelligence. Rapid experimental advances in imaging, recording, and perturbing neural systems across various species now provide opportunities to distill underlying principles of brain organization and function. Here, we take stock of recent progress and review methods used in the statistical analysis of brain networks, drawing from fields of statistical physics, network theory, and information theory. Our discussion is organized by scale, starting with models of individual neurons and extending to large-scale networks mapped across brain regions. We then examine organizing principles and constraints that shape the biological structure and function of neural circuits. We conclude with an overview of several critical frontiers, including expanding current models, fostering tighter feedback between theory and experiment, and leveraging perturbative approaches to understand neural systems. Alongside these efforts, we highlight the importance of contextualizing their contributions by linking them to formal accounts of explanation and causation.

From sequences of discrete events, humans build mental models of their world. Referred to as graph learning, the process produces a model encoding the graph of event-to-event transition probabilities. Recent evidence suggests that some networks are easier to learn than others, but the neural underpinnings of this effect remain unknown. Here we use fMRI to show that even over short timescales the network structure of a temporal sequence of stimuli determines the fidelity of event representations as well as the dimensionality of the space in which those representations are encoded: when the graph was modular as opposed to lattice-like, BOLD representations in visual areas better predicted trial identity and displayed higher intrinsic dimensionality. Broadly, our study shows that network context influences the strength of learned neural representations, motivating future work in the design, optimization, and adaptation of network contexts for distinct types of learning.

Background: Psychiatric symptoms are typically highly inter-correlated at the group level. Collectively, these correlations define the architecture of psychopathology—informing taxonomic and mechanistic models in psychiatry. However, to date, it remains unclear if this architecture differs between etiologically distinct subgroups, despite the core relevance of this understanding for personalized medicine. Here, we introduce a new analytic pipeline to probe group differences in the psychopathology architecture—demonstrated through comparison of two distinct neurogenetic disorders. Methods: We use a large questionnaire battery in 300 individuals aged 5-25 years (n = 102 XXY/KS, n = 64 XYY, n = 134 age-matched XY) to characterize the structure of correlations among 53 diverse measures of psychopathology in XXY/KS and XYY syndrome—enabling us to compare the effects of X- vs. Y-chromosome dosage on the architecture of psychopathology at multiple, distinctly informative levels. Results: Behavior correlation matrices describe the architecture of psychopathology in each syndrome. Comparison of matrix row averages reveals that autism-related features and externalizing symptoms are most differentially coupled to other aspects of psychopathology in XXY/KS vs. XYY. Clustering the difference between matrices captures coordinated group differences in pairwise coupling between measures of psychopathology: XXY/KS increases coherence among externalizing, internalizing, and autism-related features, while XYY syndrome shows greater coherence in dissociality and early neurodevelopmental impairment. Conclusions. These methods offer new insights into X- and Y-chromosome dosage effects on behavior, and our shared code can now be applied to other clinical groups of interest—helping to hone mechanistic models and inform the tailoring of care.

Multivariate information theory provides a general and principled framework for understanding how the components of a complex system are connected. Existing analyses are coarse in nature -- built up from characterizations of discrete subsystems -- and can be computationally prohibitive. In this work, we propose to study the continuous space of possible descriptions of a composite system as a window into its organizational structure. A description consists of specific information conveyed about each of the components, and the space of possible descriptions is equivalent to the space of lossy compression schemes of the components. We introduce a machine learning framework to optimize descriptions that extremize key information theoretic quantities used to characterize organization, such as total correlation and O-information. Through case studies on spin systems, Sudoku boards, and letter sequences from natural language, we identify extremal descriptions that reveal how system-wide variation emerges from individual components. By integrating machine learning into a fine-grained information theoretic analysis of composite random variables, our framework opens a new avenues for probing the structure of real-world complex systems.

Modern science is formally structured around scholarly publication, where scientific knowledge is canonized through citation. Precisely how citations are given and accrued can provide information about the value of discovery, the history of scientific ideas, the structure of fields, and the space or scope of inquiry. Yet parsing this information has been challenging because citations are not simply present or absent; rather, they differ in purpose, function, and sentiment. In this paper, we investigate how critical and favorable sentiments are distributed across citations, and demonstrate that citation sentiment tracks sociocultural norms across scales of collaboration, discipline, and country. At the smallest scale of individuals, we find that researchers cite scholars they have collaborated with more favorably (and less critically) than scholars they have not collaborated with. Outside collaborative relationships, higher h-index scholars cite lower h-index scholars more critically. At the mesoscale of disciplines, we find that wetlab disciplines tend to be less critical than drylab disciplines, and disciplines that engage in more synthesis through publishing more review articles tend to be less critical. At the largest scale of countries, we find that greater individualism (and lesser acceptance of the unequal distribution of power) is associated with more critical sentiment. Collectively, our results demonstrate how sociocultural factors can explain variations in sentiment in scientific communication. As such, our study contributes to the broader understanding of how human factors influence the practice of science, and underscore the importance of considering the larger sociocultural contexts in which science progresses.

Whether the system under study is a shoal of fish, a collection of neurons, or a set of interacting atmospheric and oceanic processes, transfer entropy measures the flow of information between time series and can detect possible causal relationships. Much like mutual information, transfer entropy is generally reported as a single value summarizing an amount of shared variation, yet a more fine-grained accounting might illuminate much about the processes under study. Here we propose to decompose transfer entropy and localize the bits of variation on both sides of information flow: that of the originating process's past and that of the receiving process's future. We employ the information bottleneck (IB) to compress the time series and identify the transferred entropy. We apply our method to decompose the transfer entropy in several synthetic recurrent processes and an experimental mouse dataset of concurrent behavioral and neural activity. Our approach highlights the nuanced dynamics within information flow, laying a foundation for future explorations into the intricate interplay of temporal processes in complex systems.

Intrinsically motivated information seeking is an expression of curiosity believed to be central to human nature. However, most curiosity research relies on small, Western convenience samples. Here, we analyze a naturalistic population of 482,760 readers using Wikipedia’s mobile app in 14 languages from 50 countries or territories. By measuring the structure of knowledge networks constructed by readers weaving a thread through articles in Wikipedia, we replicate two styles of curiosity previously identified in laboratory studies: the nomadic “busybody” and the targeted “hunter.” Further, we find evidence for another style—the “dancer”—which was previously predicted by a historico-philosophical examination of texts over two millennia and is characterized by creative modes of knowledge production. We identify associations, globally, between the structure of knowledge networks and population-level indicators of spatial navigation, education, mood, well-being, and inequality. These results advance our understanding of Wikipedia’s global readership and demonstrate how cultural and geographical properties of the digital environment relate to different styles of curiosity.

Synuclein misfolding and progressive accumulation drives a pathogenic process in Parkinson’s disease. To understand cellular and network vulnerability to α-synuclein pathology, we developed a framework to quantify network-level vulnerability and identify new therapeutic targets at the cellular level. Full brain α-synuclein pathology was mapped in mice over 9 months. Empirical pathology data was compared to theoretical pathology estimates from a diffusion model of pathology progression along anatomical connections. Unexplained variance in the model enabled us to derive regional vulnerability that we compared to regional gene expression. We identified gene expression patterns that relate to regional vulnerability, including 12 kinases that were enriched in vulnerable regions. Among these, an inhibitor of group II PAKs demonstrated protection from neuron death and α-synuclein pathology, even after delayed compound treatment. This study provides a framework for the derivation of cellular vulnerability from network-based studies and identifies a promising therapeutic pathway for Parkinson’s disease. Abstract Figure HIGHLIGHTS Longitudinal α-synuclein pathology assessment in 1046 brain regions over 9 months Linear diffusion modeling derivation of network vulnerability to α-synuclein pathology PANGEA: assessment of over 19,000 genes in 302 brain regions Group II PAK inhibitor prevents α-synuclein pathology and neuron death

Understanding the neurophysiological changes that occur during loss and recovery of consciousness is a fundamental aim in neuroscience and has marked clinical relevance. Here, we utilize multimodal magnetic resonance neuroimaging to investigate changes in regional network connectivity and neurovascular dynamics as the brain transitions from wakefulness to dexmedetomidine-induced unconsciousness, and finally into early-stage recovery of consciousness. We observed widespread decreases in functional connectivity strength across the whole brain, and targeted increases in structure-function coupling (SFC) across select networks— especially the cerebellum—as individuals transitioned from wakefulness to hypnosis. We also observed robust decreases in cerebral blood flow (CBF) across the whole brain—especially within the brainstem, thalamus, and cerebellum. Moreover, hypnosis was characterized by significant increases in the amplitude of low-frequency fluctuations (ALFF) of the resting-state blood oxygen level-dependent signal, localized within visual and somatomotor regions. Critically, when transitioning from hypnosis to the early stages of recovery, functional connectivity strength and SFC—but not CBF—started reverting towards their awake levels, even before behavioral arousal. By further testing for a relationship between connectivity and neurovascular alterations, we observed that during wakefulness, brain regions with higher ALFF displayed lower functional connectivity with the rest of the brain. During hypnosis, brain regions with higher ALFF displayed weaker coupling between structural and functional connectivity. Correspondingly, brain regions with stronger functional connectivity strength during wakefulness showed greater reductions in CBF with the onset of hypnosis. Earlier recovery of consciousness was associated with higher baseline (awake) levels of functional connectivity strength, CBF, and ALFF, as well as female sex. Across our findings, we also highlight the role of the cerebellum as a recurrent marker of connectivity and neurovascular changes between states of consciousness. Collectively, these results demonstrate that induction of, and emergence from dexmedetomidine-induced unconsciousness are characterized by widespread changes in connectivity and neurovascular dynamics. SIGNIFICANCE STATEMENT Elucidating the neurophysiological changes underlying loss and recovery of consciousness is a fundamental question in neuroscience. Here, we analyze magnetic resonance imaging data collected across multiple time-points to characterize how the human brain’s connectivity and neurovascular dynamics change as it transitions from wakefulness to dexmedetomidine-induced unconsciousness, and early-stage recovery of consciousness. During hypnosis, brain regions become less functionally synchronized to each other; they attain a smaller number of functional configurations compared to wakefulness, and display functional connectivity patterns that are more similar to the underlying structural connectivity. Furthermore, cerebral blood flow significantly decreases across the whole brain, and less metabolically demanding low frequency fluctuations in the hemodynamic signal become more prominent. Collectively, loss of consciousness is accompanied by widespread connectivity and neurovascular changes in the brain, characteristic of less metabolically demanding dynamics.