Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.40839

Expression of White and Gray Matter Autism Functional Network Connectivity Signatures in Infancy

Qiang Li·Jingyu Liu·Zening Fu·Armin Iraji·Sarah Shultz·Vince D. Calhoun
View original source ↗Read source document ↗Export metadata ↓

Abstract

Functional connectivity studies have provided valuable insights into altered brain organization in autism spectrum disorder (ASD). However, most functional connectivity studies focus exclusively on either gray matter (GM) regions or white matter (WM) tracts and examine specific age groups, rather than investigating their functional interactions across brain development. This limits our understanding of how GM-WM functional interactions evolve developmentally and may obscure important aspects of brain network organization in ASD. We combined integrated WM+GM functional network connectivity (FNC) with brain-wide risk scores (BRSs) to quantify ASD-associated pattern expression in ABIDE I/II and an independent infant cohort. We employed a NeuroMark framework with predefined WM and GM intrinsic connectivity networks templates, to estimate subject-level FNC. First, we identified two distinct patterns of altered WM+GM FNC in ASD: GM-related hypoconnectivity centered on the paralimbic network and WM-GM hyperconnectivity centered on the frontal network. These findings highlight the involvement of higher-order cognitive networks and the importance of WM-GM functional interactions in characterizing ASD-related connectivity alterations. Second, infants with a higher familial likelihood of autism exhibited greater mean expression of ASD-associated FNC patterns than those with a lower familial likelihood, as quantified by BRS. These group-level findings do not establish developmental persistence or predict diagnostic outcomes at the individual level.

Explore related subjects

q-bio.NC

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Qiang Li, Jingyu Liu, Zening Fu, Armin Iraji, Sarah Shultz, Vince D. Calhoun. 2026-10-29. Expression of White and Gray Matter Autism Functional Network Connectivity Signatures in Infancy. https://arxiv.org/abs/2610.40839

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Do agents need to sleep?

LLM-based agents, which increasingly operate persistently across sessions and accumulate memory through interaction, already exhibit the familiar symptoms of unchecked plasticity: semantic drift, degraded retrieval precision, propagation of stored errors, and saturation of memory capacity. Drawing on the selectionist "ringing, resonance and renormalization" (RRR) account of sleep, we propose that each of its three phases maps onto an operation feasible in digital memory architectures. Engineers building stateful agents, meanwhile, are independently rediscovering the problems sleep evolved to solve: what to forget, and when to do the forgetting. From this we derive the falsifiable prediction that selectionist consolidation will outperform instructionist alternatives as memory stores grow large. The core claim concerns the nature of continuous learning itself: plasticity has a price, and for any system that learns continuously under finite capacity while coupled to its environment, that price is sleep.

q-bio.NC↗
arXiv2026

Sketch of a novel approach to a neural model

We present an account of neuroplasticity with respect to cell-internal processing pathways in relation to membrane and synaptic plasticity. We think traditional synapse-centric, weight-based models of memorization are not sufficient or adequate to capture the complexity of neuroplasticity. In these accounts, the model is a network of neurons connected by adaptive transmission links. The adaptation of the transmission links relies on weight changes according to use of the transmission link (short-term and long-term potentiation/depression). In contrast, we propose a paradigm switch from a synapse-centric model (each synapse learns independently, based on its history of use) to a neuron-centric model (each neuron uses signal selection for intracellular pathways to express plasticity at the membrane). A neural model consists of (a) expression of parameters at the membrane, in particular dendritic synapses or spines, and axonal boutons (b) internal parameters in the sub-membrane zone and the cytoplasm with its protein signaling network and (c) core parameters in the nucleus for genetic and epigenetic information. In a neuron-centric model, each node (=neuron) in the network has its own internal memory. Neural transmission and information storage are separated, not automatically combined by coupling strength. There is filtering and selection of signals for storage. Not every transmission event leaves a trace. This represents an important conceptual advance over synaptic weight models. We present the neuron as a self-programming device, rather than as passively determined by ongoing input. We believe a new approach to neural modeling is necessary, because the experimental evidence is not well captured by traditional synapse-centric models. Ultimately, we are interested in the possibilities of a flexible memory system that processes external signals according to its inherent structure.

q-bio.NC↗
arXiv2026

Analyzing Memory Effects in Large Language Models through the lens of Cognitive Psychology

Memory, a fundamental component of human cognition, exhibits adaptive yet fallible characteristics as illustrated by Schacter's memory "sins".These cognitive phenomena have been studied extensively in psychology and neuroscience, but the extent to which artificial systems, specifically Large Language Models (LLMs), emulate these cognitive phenomena remains underexplored. This study uses human memory research as a lens for understanding LLMs and systematically investigates human memory effects in state-of-the-art LLMs using paradigms drawn from psychological research. We evaluate seven key memory phenomena, comparing human behavior to LLM performance. Both people and models remember less when overloaded with information (list length effect) and remember better with repeated exposure (list strength effect). They also show similar difficulties when retrieving overlapping information, where storing too many similar facts leads to confusion (fan effect). Like humans, LLMs are susceptible to falsely "remembering" words that were never shown but are related to others (false memories), and they can apply prior learning to new, related situations (cross-domain generalization). However, LLMs differ in two key ways: they are less influenced by the order in which information is presented (positional bias) and more robust when processing random or meaningless material (nonsense effect). These results reveal both alignments and divergences in how LLMs and humans reconstruct memory. The findings help clarify how memory-like behavior in LLMs echoes core features of human cognition, while also highlighting the architectural differences that lead to distinct patterns of error and success.

q-bio.NC↗