Biology subjects

Achterberg, J.

Publications and source records attributed to Achterberg, J..

7 recordsLinked to original sources

bioRxiv2025

Neural dynamics of an extended frontal lobe network in goal-subgoal problem solving

In goal-directed behavior, cognitive control calls for representation of current state, goal state, and current move, often with hierarchical organization into a series of steps or subgoals. In the human brain, a network of multiple-demand or MD regions underpins cognitive control, and core task features are likely encoded and integrated across this distributed network. Here we recorded from four putative homologs to human MD regions in the frontal cortex -- ventrolateral (vlPFC), dorsomedial (dmPFC), dorsal premotor (dPM) and anterior insula/orbitofrontal (I/O) cortex -- as two male monkeys solved a multi-step, on-screen spatial maze. Across regions there was substantial overlap but also wide quantitative variation in encoding task variables. Sensory input and current state were strongly coded in vlPFC, goal most stably in dmPFC, and move most rapidly in vlPFC and dPM. I/O responded during revision of a prepared route. In all regions, an abstract code of problem structure marked progress from problem start to end, with hierarchical separation of progress within and between successive steps. Partial specialisations, we suggest, arise from distinct connectivity, while broad co-recruitment reflects strong information exchange. Our results show how core components of goal-directed behavior are encoded across a distributed network of partial frontal lobe specialists, putatively homologous to frontal components of the human MD system.

neuroscience↗

bioRxiv2024

Synaptic function and sensory processing in ZDHHC9-associated neurodevelopmental disorder: a mechanistic account

Loss-of-function ZDHHC9 variants are associated with X-linked intellectual disability (XLID), rolandic epilepsy (RE) and developmental language difficulties. This study integrates human neurophysiological data with a computational model to identify a potential neural mechanism explaining ZDHHC9-associated differences in cortical function and cognition. Magnetoencephalography (MEG) data was collected during an auditory roving oddball paradigm from eight individuals with a ZDHHC9 loss-of-function variant (ZDHHC9 group) and seven age-matched individuals without neurological or neurodevelopmental difficulties (control group). Evoked responses to auditory stimulation were larger in amplitude and showed a later peak latency in the ZDHHC9 group but demonstrated normal stimulus-specific properties. Magnetic mismatch negativity (mMMN) amplitude was also increased in the ZDHHC9 group, reflected by stronger neural activation during deviant processing relative to the standard. A recurrent neural network (RNN) model was trained to mimic recapitulate group-level auditory evoked responses, and subsequently perturbed to test the hypothesised impact of ZDHHC9-driven synaptic dysfunction on neural dynamics. Results of model perturbations showed that reducing inhibition levels by weakening inhibitory weights recapitulates the observed group differences in evoked responses. Stronger reductions in inhibition levels resulted in increased peak amplitude and peak latency of RNN prediction relative to the pre-perturbation predictions. Control experiments in which excitatory connections were strengthened by the same levels did not result in consistently stable activity or AEF-like RNN predictions. Together, these results suggest that reduced inhibition is a plausible mechanism by which loss of ZDHHC9 function alters cortical dynamics during sensory processing. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=93 SRC="FIGDIR/small/587155v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@1f9bd36org.highwire.dtl.DTLVardef@f54610org.highwire.dtl.DTLVardef@caff66org.highwire.dtl.DTLVardef@145a4d2_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG In the current study, we employed a bottom-up approach to study the impact of synaptic-level alterations associated with ZDHHC9 variants on cortical function in healthy and ZDHHC9-deficient participants. To achieve this, a recurrent neural network model was developed to recapitulate MEG-derived auditory evoked responses and subsequently perturbed in order to determine effects on resulting dynamics. We show that reduced network inhibition recapitulates empirical observations, specifically increased response amplitudes, delayed peak latencies and increased mismatch negativity. These results offered a mechanistic account on the impact of ZDHHC9-associated synaptic alterations on auditory processing.

neuroscience↗

bioRxiv2023

A weighted generative model of the human connectome

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

neuroscience↗

bioRxiv2022

Spatially-embedded recurrent neural networks reveal widespread links between structural and functional neuroscience findings

Brain networks exist within the confines of resource limitations. As a result, a brain network must overcome metabolic costs of growing and sustaining the network within its physical space, while simultaneously implementing its required information processing. To observe the effect of these processes, we introduce the spatially-embedded recurrent neural network (seRNN). seRNNs learn basic task-related inferences while existing within a 3D Euclidean space, where the communication of constituent neurons is constrained by a sparse connectome. We find that seRNNs, similar to primate cerebral cortices, naturally converge on solving inferences using modular small-world networks, in which functionally similar units spatially configure themselves to utilize an energetically-efficient mixed-selective code. As all these features emerge in unison, seRNNs reveal how many common structural and functional brain motifs are strongly intertwined and can be attributed to basic biological optimization processes. seRNNs can serve as model systems to bridge between structural and functional research communities to move neuroscientific understanding forward.

neuroscience↗

bioRxiv2021

Fluid intelligence and naturalistic task impairments after focal brain lesions

Classical executive tasks, such as Wisconsin card-sorting and verbal fluency, are widely used as tests of frontal lobe control functions. Since the pioneering work of Shallice and Burgess (1991), it has been known that complex, naturalistic tasks can capture deficits that are missed in these classical tests. Matching this finding, deficits in several classical tasks are predicted by loss of fluid intelligence, linked to damage in a specific cortical "multiple-demand" (MD) network, while deficits in a more naturalistic task are not. To expand on these previous results, we examined the effect of focal brain lesions on three new tests - a modification of the previously-used Hotel task, a new test of task switching after extended delays, and a test of decision-making in imagined real-life scenarios. As potential predictors of impairment we measured volume of damage to a priori MD and default mode (DMN) networks, as well as cortical damage outside these networks. Deficits in the three new tasks were substantial, but were not explained by loss of fluid intelligence, or by volume of damage to either MD or DMN networks. Instead, deficits were associated with diverse lesions, and not strongly correlated with one another. The results confirm that naturalistic tasks capture cognitive deficits beyond those measured by fluid intelligence. We suggest, however, that these deficits may not arise from specific control operations required by complex behaviour. Instead, like everyday activities, complex tasks combine a rich variety of interacting cognitive components, bringing many opportunities for processing to be disturbed.

neuroscience↗

bioRxiv2020

A one-shot learning signal in monkey prefrontal cortex

Much animal learning is slow, with cumulative changes in behavior driven by reward prediction errors. When the abstract structure of a problem is known, however, both animals and formal learning models can rapidly attach new items to their roles within this structure, sometimes in a single trial. Frontal cortex is likely to play a key role in this process. To examine information seeking and use in a known problem structure, we trained monkeys in a novel explore/exploit task, requiring the animal first to test objects for their association with reward, then, once rewarded objects were found, to re-select them on further trials for further rewards. Many cells in the frontal cortex showed an explore/exploit preference, changing activity in a signal trial to align with one-shot learning in the monkeys behaviour. In contrast to this binary switch, these cells showed little evidence of continuous changes linked to expectancy or prediction error. Explore/exploit preferences were independent for two stages of the trial, object selection and receipt of feedback. Within an established task structure, frontal activity may control the separate operations of explore and exploit, switching in one trial between the two. Significance statementMuch animal learning is slow, with cumulative changes in behavior driven by reward prediction errors. When the abstract structure a problem is known, however, both animals and formal learning models can rapidly attach new items to their roles within this structure. To address transitions in neural activity during one-shot learning, we trained monkeys in an explore/exploit task using familiar objects and a highly familiar task structure. In contrast to continuous changes reflecting expectancy or prediction error, frontal neurons showed a binary, one-shot switch between explore and exploit. Within an established task structure, frontal activity may control the separate operations of exploring alternative objects to establish their current role, then exploiting this knowledge for further reward.

neuroscience↗

bioRxiv2019

Neurocognitive reorganization between crystallized intelligence, fluid intelligence and white matter microstructure in two age-heterogeneous developmental cohorts

Despite the reliability of intelligence measures in predicting important life outcomes such as educational achievement and mortality, the exact configuration and neural correlates of cognitive abilities remain poorly understood, especially in childhood and adolescence. Therefore, we sought to elucidate the factorial structure and neural substrates of child and adolescent intelligence using two cross-sectional, developmental samples (CALM: N=551 (N=165 imaging), age range: 5-18 years, NKI-Rockland: N=337 (N=65 imaging), age range: 6-18 years). In a preregistered analysis, we used structural equation modelling (SEM) to examine the neurocognitive architecture of individual differences in childhood and adolescent cognitive ability. In both samples, we found that cognitive ability in lower and typical-ability cohorts is best understood as two separable constructs, crystallized and fluid intelligence, which became more distinct across development, in line with the age differentiation hypothesis. Further analyses revealed that white matter microstructure, most prominently the superior longitudinal fasciculus, was strongly associated with crystallized (gc) and fluid (gf) abilities. Finally, we used SEM trees to demonstrate evidence for developmental reorganization of gc and gf and their white matter substrates such that the relationships among these factors dropped between 7-8 years before increasing around age 10. Together, our results suggest that shortly before puberty marks a pivotal phase of change in the neurocognitive architecture of intelligence.

neuroscience↗