Understanding divergence: Placing developmental neuroscience in its dynamic context

Neuroscience & Biobehavioral Reviews, Journal Year: 2024, Volume and Issue: 157, P. 105539 - 105539

Published: Jan. 9, 2024

Neurodevelopment is not merely a process of brain maturation, but an adaptation to constraints unique each individual and the environments we co-create. However, our theoretical methodological toolkits often ignore this reality. There growing awareness that shift needed allows us study divergence behaviour across conventional categorical boundaries. argue in future must also incorporate developmental dynamics capture emergence those neurodevelopmental differences. This crucial step will require adjustments design methodology. If ultimate aim how, ultimately when, takes place then need analytic toolkit equal these ambitions. We over reliance on group averages has been conceptual dead-end with regard part because any differences are inevitably lost within average. Instead, approaches which themselves new, or simply newly applied context, may allow frameworks from groups individuals. Likewise, methods capable modelling complex dynamic systems understand emergent only possible at level interacting neural system.

Language: Английский

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Toward computational neuroconstructivism: a framework for developmental systems neuroscience

Trends in Cognitive Sciences, Journal Year: 2023, Volume and Issue: 27(8), P. 726 - 744

Published: May 31, 2023

Brain development is underpinned by complex interactions between neural assemblies, driving structural and functional change. This neuroconstructivism (the notion that functions are shaped these interactions) core to some developmental theories. However, due their complexity, understanding underlying mechanisms challenging. Elsewhere in neurobiology, a computational revolution has shown mathematical models of hidden biological can bridge observations with theory building. Can we build similar framework yielding mechanistic insights for brain development? Here, outline the conceptual technical challenges addressing this gap, demonstrate there great potential specifying as mathematically defined processes operating within physical constraints. We provide examples, alongside broader ingredients needed, field explores explanations system-wide development.

Language: Английский

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Individual variation in the functional lateralization of human ventral temporal cortex: Local competition and long-range coupling

Imaging Neuroscience, Journal Year: 2025, Volume and Issue: 3

Published: Jan. 1, 2025

The ventral temporal cortex (VTC) of the human cerebrum is critically engaged in high-level vision. One intriguing aspect this region its functional lateralization, with neural responses to words being stronger left hemisphere, and faces right hemisphere; such patterns can be summarized a signed laterality index (LI), positive for leftward laterality. Converging evidence has suggested that word emerges couple efficiently left-lateralized frontotemporal language regions, but more mixed regarding sources lateralization face perception. Here, we use individual differences as tool test three theories VTC organization arising from (1) local competition between driven by long-range coupling processes, (2) other categories, (3) areas exhibiting social processing. First, an in-house MRI experiment, did not obtain negative correlation LIs selectivity relative object responses, find when using fixation baseline, challenging ideas driving rightward lateralization. We next examined broader LI interactions large-scale Human Connectome Project (HCP) dataset. Face were significantly anti-correlated, while body positively correlated, consistent idea generic representational cooperation may shape Last, assessed role development Within our substantial was evident text several nodes distributed text-processing circuit. In HCP data, both negatively correlated processing different subregions posterior lobe (PSL STSp, respectively). summary, no face-word VTC; instead, multiple lateralities within VTC, including Moreover, also influenced lobe, where become lateralized due language.

Language: Английский

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A weighted generative model of the human connectome

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: June 25, 2023

Abstract Probabilistic generative network models have offered an exciting window into the constraints governing human connectome’s organization. In particular, they highlighted economic context of formation and special roles that physical geometry self-similarity likely play in determining topology. However, a critical limitation these is do not consider strength anatomical connectivity between regions. This significantly limits their scope to answer neurobiological questions. The current work draws inspiration from principle redundancy reduction develop novel weighted model. model significant advance because it only incorporates theoretical advancements previous models, but also has ability capture dynamic strengthening or weakening connections over time. Using state-of-the-art Convex Optimization Modelling for Microstructure-Informed Tractography (COMMIT) approach, sample children adolescents ( n = 88, aged 8 18 years), we show this can accurately approximate simultaneously topology edge-weights connectome (specifically, MRI signal fraction attributed axonal projections). We achieve at both sparse dense densities. Generative fits are comparable to, many cases better than, published findings simulating absence weights. Our implications future research by providing new avenues exploring normative developmental trends, neural computation wider conceptual economics connectomics supporting functioning.

Language: Английский

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Individual variation in the functional lateralization of human ventral temporal cortex: Local competition and long-range coupling

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 16, 2024

Abstract The ventral temporal cortex (VTC) of the human cerebrum is critically engaged in computations related to high-level vision. One intriguing aspect this region its asymmetric organization and functional lateralization. Notably, VTC, neural responses words are stronger left hemisphere, whereas faces right hemisphere. Converging evidence has suggested that left-lateralized word emerge couple efficiently with frontotemporal language regions, but more mixed regarding sources right-lateralization for face perception. Here, we use individual differences as a tool adjudicate between three theories VTC arising from: 1) local competition faces, 2) other categories, 3) long-range coupling areas subject their own competition. First, an in-house MRI experiment, demonstrated laterality both substantial reliable within right-handed population young adults. We found no (anti-)correlation selectivity relative object responses, positive correlation when using fixation baseline, challenging ideas faces. next examined broader large-scale Human Connectome Project (HCP) dataset. Face were significantly anti-correlated, while body positively correlated, consistent idea generic representational cooperation may shape Last, assessed role development laterality. Within our was evident text several nodes distributed text-processing circuit. In HCP data, negatively correlated laterality, social perception same areas, effect processing representations, driven by processing. conclude interactions heterogeneous hemispheric specializations visual cortex.

Language: Английский

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conn2res: A toolbox for connectome-based reservoir computing

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: June 4, 2023

The connection patterns of neural circuits form a complex network. How signaling in these manifests as cognition and adaptive behaviour remains the central question neuroscience. Concomitant advances connectomics artificial intelligence open fundamentally new opportunities to understand how shape computational capacity biological brain networks. Reservoir computing is versatile paradigm that uses nonlinear dynamics high-dimensional dynamical systems perform computations approximate cognitive functions. Here we present conn2res : an open-source Python toolbox for implementing networks modular, allowing arbitrary architectures be imposed. allows researchers input connectomes reconstructed using multiple techniques, from tract tracing noninvasive diffusion imaging, impose systems, simple spiking neurons memristive dynamics. versatility us ask questions at confluence neuroscience intelligence. By reconceptualizing function computation, sets stage more mechanistic understanding structure-function relationships

Language: Английский

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Alterations in topology, cost and dynamics of gamma-band EEG functional networks in a preclinical model of traumatic brain injury

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 12, 2024

Traumatic brain injury is a major cause of disability leading to multiple sequelae in cognitive, sensory, and physical domains, including post-traumatic epilepsy. Despite extensive research, our understanding its impact on macroscopic circuitry remains incomplete. We analyzed electrophysiological functional connectomes the gamma band using preclinical model blast-induced traumatic over time points after injury. revealed differences small-world propensity rich-club structure compared age-matched controls, indicating reorganization following further investigated cost-efficiency trade-offs, propose computationally efficient normalization procedure for quantifying cost spatially embedded networks that controls connectivity strength differences, suggest metabolic drivers as candidate observed differences. Furthermore, we employed brain-wide computational seizure dynamics attribute homeostatic mechanism activity regulation with potential unintended consequence driving generalized seizures. Finally, demonstrated post-injury hyperexcitability manifests an increase sound-evoked response amplitudes at cortical level. Our work characterizes first gamma-band network proposes causes these changes, thus identifying targets future therapeutic interventions.

Language: Английский

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conn2res: A toolbox for connectome-based reservoir computing

Research Square (Research Square), Journal Year: 2023, Volume and Issue: unknown

Published: June 28, 2023

Abstract The connection patterns of neural circuits form a complex network. How signaling in these manifests as cognition and adaptive behaviour remains the central question neuroscience. Concomitant advances connectomics artificial intelligence open fundamentally new opportunities to understand how shape computational capacity biological brain networks. Reservoir computing is versatile paradigm that uses nonlinear dynamics high-dimensional dynamical systems perform computations approximate cognitive functions. Here we present conn2res: an open-source Python toolbox for implementing networks conn2res modular, allowing arbitrary architectures be imposed. allows researchers input connectomes reconstructed using multiple techniques, from tract tracing noninvasive diffusion imaging, impose systems, simple spiking neurons memristive dynamics. versatility us ask questions at confluence neuroscience intelligence. By reconceptualizing function computation, sets stage more mechanistic understanding structure-function relationships

Language: Английский

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Winning the Lottery With Neural Connectivity Constraints: Faster Learning Across Cognitive Tasks With Spatially Constrained Sparse RNNs

Neural Computation, Journal Year: 2023, Volume and Issue: 35(11), P. 1850 - 1869

Published: Sept. 19, 2023

Recurrent neural networks (RNNs) are often used to model circuits in the brain and can solve a variety of difficult computational problems requiring memory, error correction, or selection (Hopfield, 1982; Maass et al., 2002; Maass, 2011). However, fully connected RNNs contrast structurally with their biological counterparts, which extremely sparse (about 0.1%). Motivated by neocortex, where connectivity is constrained physical distance along cortical sheets other synaptic wiring costs, we introduce locality masked (LM-RNNs) that use task-agnostic predetermined graphs sparsity as low 4%. We study LM-RNNs multitask learning setting relevant cognitive systems neuroscience commonly set tasks, 20-Cog-tasks (Yang 2019). show through reductio ad absurdum be solved small pool separated autapses mechanistically analyze understand. Thus, these tasks fall short goal inducing complex recurrent dynamics modular structure RNNs. next contribute new battery, Mod-Cog, consisting up 132 expands about seven-fold number task complexity 20-Cog-tasks. Importantly, while simple 20-Cog-tasks, expanded requires richer architectures continuous attractor dynamics. On an optimal result faster training better data efficiency than networks.

Language: Английский

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Inductive biases of neural specialization in spatial navigation

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2022, Volume and Issue: unknown

Published: Dec. 7, 2022

Abstract The brain may have evolved a modular architecture for reward-based learning in daily tasks, with circuits featuring functionally specialized modules that match the task structure. We propose this enables better and generalization than architectures less modules. To test hypothesis, we trained reinforcement agents various neural on naturalistic navigation task. found largely segregates computations of state representation, value, action into more efficient generalization. Behaviors also resemble macaque behaviors closely. Investigating latent these agents, discovered learned representation combines prediction observation, weighted by their relative uncertainty, akin to Kalman filter. These results shed light possible rationale brain’s specializations suggest artificial systems can use insight from neuroscience improve natural tasks.

Language: Английский

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