A collaborative research effort led by Professor Rihui Li from the Centre for Cognitive and Brain Sciences and Professor Qiong Xu of the Children’s Hospital of Fudan University has identified distinct and shared patterns of brain structure development in young children with Autism Spectrum Disorder (ASD) and Fragile X Syndrome (FXS). The study, published in the prestigious international journal Molecular Psychiatry (5-Year IF=11.8), provides critical new insights into how these neurodevelopmental disorders diverge in their underlying neurobiology during a crucial early window of development.
ASD is a highly heterogeneous disorder characterized by social communication deficits and repetitive behaviors, affecting 1-2% of children globally. This study confronts this complexity by directly comparing ASD to FXS—the most common monogenic cause of ASD—which offers a more genetically homogeneous model with overlapping behavioral symptoms.
Utilizing T1-weighted structural MRI data from 190 children (90 with idiopathic ASD, 46 with FXS, and 54 typically developing controls), the team conducted advanced voxel-based morphometry (VBM) analysis to map gray matter volume (GMV) differences. A key innovation was the application of age-varying analysis to track developmental trajectories between ages 2 and 8.
The research revealed a unique and pronounced neuroanatomical signature for FXS in early childhood. As shown in Figure 1, children with FXS exhibited a complex pattern of regional volume alterations, characterized by a significant increase in GMV within subcortical structures—most notably the caudate nucleus—and the cerebellar Crus I. Simultaneously, these children showed substantial volume reductions in key cortical and cerebellar regions, including the frontal insular cortex and the vermis of the cerebellum (lobules VIII/IX), when compared to both the ASD and typically developing control groups. This distinctive combination of overgrowth and undergrowth provides a clear structural fingerprint for FXS.
This study also demonstrated that these structural differences are not static but follow dynamic, age-dependent trajectories. Figure 2 illustrates that the GMV differences observed in FXS remained remarkably consistent across multiple narrow age windows between 2 and 8 years. This persistence suggests that the underlying neurobiological mechanisms driving these changes are active throughout early childhood.
A pivotal discovery, detailed in Figure 3, was the significant divergence in growth rates of GMV between the two disorders. While both conditions involve atypical development, children with ASD demonstrated a pattern of significantly accelerated GMV growth across the entire brain and in nearly all specific regions of interest compared to both the FXS and control groups. In contrast, the FXS group showed growth trajectories that were either similar to typically developing children or significantly slower than the ASD group in certain cerebellar regions. These finding positions aberrantly accelerated growth as a key feature of idiopathic ASD in early childhood, while highlighting a different developmental path for FXS.
This research establishes a clear structural brain signature for FXS and identifies aberrant growth trajectories as a key feature of ASD. The finding that these differences are dynamic and age-dependent is a major step toward understanding the distinct neurobiological underpinnings of these conditions, despite their overlapping symptoms. The findings advocate for moving beyond behavior-based diagnoses to develop mechanistically targeted therapies tailored to the specific neurodevelopmental trajectory of each condition. Future work will focus on longitudinal studies to confirm these developmental trajectories and explore how these early brain differences predict long-term outcomes.
Original article link: https://www.nature.com/articles/s41380-025-03350-0
Prof. Rihui Li from the CCBS of the University of Macau and Prof. Qiong Xu from the Children’s Hospital of Fudan University are the co-corresponding authors of the article. PhD student Danyong Feng from the CCBS of the University of Macau and Dr. Dongyun Li from the Children’s Hospital of Fudan University are the co-first authors of the article. This work is supported by the National Natural Science Foundation of China (82301743), the Science and Technology Development Fund of the Macao SAR (0010/2023/ITP1, 0016/2024/RIB1), the University of Macau (SRG2023-00015-ICI, MYRG-GRG2024-00296-ICI, and MYRG-CRG2024-00022-ICI), the Natural Science Foundation of Anhui Province (No. 2308085MH255) and the academic leaders development program (EKXDPY202306) and Med+X cross-disciplinary team project of the Children’s Hospital of Fudan University.

Figure 1. Comparisons of gray matter volume (GMV) between three groups

Figure 2. Comparisons of GMV in different age ranges between three groups.

Figure 3. Comparisons of GMV growth rates in different age ranges between three groups.