Skip to main content
NCBI home page
Springer logoLink to Springer
. 2026 Feb 25;20(2):22. doi: 10.1007/s11682-026-01110-4

Associations between white matter micro- and macro-structure and attention in 6-7-year-old children with low to moderate prenatal alcohol exposure

Philippa Pyman 1,2, Claire E Kelly 1,2, Thijs Dhollander 2, Garance Delagneau 1,2, Elizabeth J Elliott 3,4, Anthony J Penington 2,5,6, Stephen Hearps 2, Simonne E Collins 1,2, Sharon Lewis 2,6, Xiaoyun Liang 2, Alicia Spittle 2,7, Renee Testa 5, Jane Halliday 2,6, Evelyne Muggli 2,6, Deanne K Thompson 1,2,6, Peter J Anderson 1,2,8,9,✉
PMCID: PMC12935816  PMID: 41739341

Abstract

Heavy prenatal alcohol exposure is associated with changes to the brain’s white matter, which may underpin attention problems observed in this population. Less is known about the effect of low to moderate exposure. This study investigated white matter correlates of attention in children with low to moderate levels of prenatal alcohol exposure and controls whose mothers abstained from alcohol. We also examined whether associations differed between alcohol exposure in trimester one only or throughout pregnancy. A total of 129 children aged 6-7-years from the prospective Asking Questions about Alcohol in pregnancy (AQUA) cohort (trimester one alcohol exposure n = 47; exposure throughout pregnancy n = 45; controls n = 37) participated in a brain magnetic resonance imaging study. Fixel-based analysis was performed on diffusion weighted images to investigate associations between whole brain white matter micro- and macro-structure and attention (focus, shift, sustain, hyperactivity and inattention). In controls, better focused attention and less hyperactivity were associated with greater macrostructural cross-sectional area of the corticospinal tract and cerebellar white matter, respectively. Greater microstructural fiber density in the splenium of the corpus callosum was associated with more inattention in the children with trimester one alcohol exposure, which persisted when adjusted for any binge drinking episodes prior to pregnancy recognition. Subtle differences in the association between white matter and attention were found between controls and prenatal exposure groups, where associations were stronger in controls. While further exploration is needed, preliminary findings in 6-7-year-old children suggest that low to moderate prenatal alcohol exposure affects white matter fibers that subserve attention skills.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11682-026-01110-4.

Keywords: Prenatal alcohol exposure, Attention, Brain microstructure, Diffusion-weighted MRI, Fixel based analysis

Introduction

Despite a high prevalence of low-moderate alcohol intake during pregnancy, the long-term consequences of prenatal alcohol exposure (PAE) remain poorly understood (Muggli et al., 2016; Young et al., 2022). Risks associated with PAE are predominantly informed by children who have Fetal Alcohol Spectrum Disorder (FASD). FASD encompasses cognitive, behavioral, brain and growth deficits that are generally associated with heavy and chronic PAE (Jacobson et al., 2021; Kodituwakku et al., 2011). Attention problems are a core feature and a key diagnostic criterion for FASD diagnosis (Bower & Elliott, 2016). It has been hypothesized that low to moderate PAE have similar, albeit more subtle, effects on neurodevelopment (Wozniak et al., 2019). Findings are variable and few studies have found an association between low to moderate PAE and developmental deficits (Flak et al., 2014; Muggli et al., 2024; Ogunjimi et al., 2025).

Disruption to the brain’s white matter underpins the adverse neurodevelopmental outcomes observed in FASD (Ghazi Sherbaf et al., 2019). A review of diffusion tensor imaging findings identified diffuse white matter changes in FASD, with the most affected regions being the corpus callosum, inferior and superior longitudinal fasciculi, cerebellar peduncles and cingulum (Ghazi Sherbaf et al., 2019). Radiological findings on clinical MRI in FASD patients are inconsistent and dependent on timing dose and duration of PAE (Treit et al., 2020). A dose-response relationship has been identified between PAE and abnormalities of white matter microstructure in the corpus callosum, inviting hypotheses that low levels of PAE have adverse effects (Fan et al., 2016; Fryer et al., 2009; Li et al., 2009). One study found an association between moderate and occasional binge PAE and altered white matter microstructure in the superior longitudinal fasciculus and inferior cerebellar peduncles in infants with PAE compared to unexposed controls (Donald et al., 2015). Given that many of the regions associated with PAE are important for attention (Lebel et al., 2010), findings may have broad clinical implications for cognitive functioning.

Different neural networks are important for focused, shifting and sustained attention (Ware et al., 2021). For example, altered microstructure of the cingulum, corpus callosum and superior longitudinal fasciculus have been associated with sustained (maintenance of focus over time) (Chiang et al., 2015; Klarborg et al., 2013; Santhanam et al., 2011), focused (filtering out distractors) (Ge et al., 2013) and shifting (disengaging and refocusing) (Ursache et al., 2016) attention, as well as behavioral hyperactivity and impulsivity (Chiang et al., 2020; Nagel et al., 2011). The brain regions associated with attention among typically developing children and children with Attention Deficit and Hyperactivity Disorder (ADHD), are regions that are vulnerable to the neurotoxicity of PAE. Altered white matter microstructure in varying brain regions may therefore underpin different dimensions of attention problems in those with PAE. Preliminary evidence identified a link between focused attention and white matter microstructure in the splenium and isthmus of the corpus callosum in children with FASD (Fan et al., 2016), but this is not a universal finding (Wozniak et al., 2009).

Past investigations of white matter microstructure most commonly use the diffusion tensor modelling technique. Diffusion tensor imaging-derived measures such as fractional anisotropy are non-specific to white matter properties (e.g., axon density) and are affected by extra-axonal signal contamination (Jones et al., 2013). Further, they cannot detect complex fiber geometries (e.g., crossing, bending or fanning) of the multiple fiber populations within image voxels. Relating diffusion tensor imaging measures to underlying biology is therefore not ideal (Jones et al., 2013). A more novel technique, Fixel-Based Analysis (FBA), enables the analysis of individual fiber populations within each voxel (fixel), allowing for a finer unit of analysis and more biologically interpretable measures (Dhollander et al. 2021a, b; Raffelt et al. 2015). FBA analyses fiber-specific measures including the microstructural density (fiber density, FD), macrostructural cross-sectional area (fiber cross-section, FC) and the combined fiber density and cross section (FDC) of white matter. FDC reflects the total intra-axonal volume of fibers and their capacity to transmit information (Raffelt et al., 2017). Studies in populations with neurodegenerative and demyelinating disorders or those born preterm demonstrate that greater cross section and density of fibers support cognition (Dhollander et al. 2021a, b). We have recently reported data from the Asking Questions About Alcohol in Pregnancy (AQUA) cohort confirming that low-moderate PAE throughout pregnancy leads to widespread changes in structural brain connectivity (Liang et al., 2024) and reduced FBA-derived cross-sectional area of the cingulum bundle compared with controls (Thompson et al., 2024). However, the implications of these subtle changes in white matter micro- and macro-structure for attention functioning warrant further investigation.

We aimed to apply FBA to examine associations between attention (focus, sustain, shift, behavioral hyperactivity and inattentiveness) and white matter micro- and macro- structure (FD, FC, and FDC) in a cohort of children exposed to different levels of PAE: No exposure; low-moderate PAE only in trimester 1 (T1); low-moderate PAE exposure throughout pregnancy (T1-T3). Based on past literature, including FASD research, it was hypothesized that higher FD, FC and FDC in the cingulum, corpus callosum and superior longitudinal fasciculus would be associated with better attention in control and PAE groups. We secondly aimed to examine whether associations between white matter abnormalities and attention varied across different PAE exposure groups, and hypothesized that the effects of PAE would be dependent on both dose and timing (Guerri, 1998).

Methods

Participants

Pregnant women were recruited into the AQUA cohort at their first antenatal appointment from seven hospitals around Melbourne between 2011 and 2012.

Inclusion criteria at recruitment

Eligibility criteria included being < 19 weeks pregnant, singleton pregnancy, maternal age ≥16, and English language proficiency sufficient to obtain informed consent and complete questionnaires. Women were required to be attending low-risk public hospital antenatal clinics at the time of recruitment. “Low-risk” status was determined by the attending clinic classification and excluded women with substance dependence, major psychiatric conditions, or pre-existing complex medical conditions requiring specialized obstetric care at baseline. However, women who subsequently developed pregnancy complications (such as preeclampsia) during the course of their pregnancy remained in the cohort. The sample comprised generally well-educated mothers, with 57% having completed a university degree at baseline. Comprehensive data were collected on maternal medical conditions, substance use (including detailed alcohol consumption patterns), diet and supplement intake, as well as demographic and family factors (Muggli et al., 2014).

Exclusion criteria at 6–7 year follow-up

For the present analysis conducted at the 6–7 year follow-up, we excluded lifelong alcohol abstainers because our target population was children of mothers who normally drink some alcohol.

At baseline, the AQUA cohort consisted of 1570 children (lifetime abstainers n = 112; no PAE n = 494; PAE T1-T3 n = 461; PAE T1 n = 430; and irregular PAE n = 73 (Muggli et al., 2016). The cohort was reviewed at ages one (Muggli et al., 2017), two (Halliday et al., 2017), and 6-to-9 years (Muggli et al. 2022a) for craniofacial analysis and/or neuropsychological assessment (n = 698).

During the 6–7-year follow-up, 146 children had an MRI, of which 129 children had diffusion imaging data for FBA. Selection was stratified to ensure approximately equivalent numbers with no PAE (controls n = 37), PAE T1 (n = 47), and PAE T1-T3 (n = 45). Approval was granted by the Human Research Ethics Committee at the Royal Children’s Hospital Melbourne (approval #38025) and written parental consent was obtained.

Procedure

Children completed a neuropsychological assessment at the Murdoch Children’s Research Institute, Melbourne. Assessors were registered or student psychologists blinded to PAE. Children from each subgroup (controls, PAE T1, PAE T1-T3) were invited for MRI at the Royal Children’s Hospital, Melbourne. Mock MRI training was undertaken prior to their MRI to familiarize children with the procedure and MRI was performed on non-sedated children.

Assessment of PAE

PAE was prospectively measured using questionnaires administered at three timepoints in pregnancy (Muggli et al., 2010, 2014). Information about the type, volume, and frequency of drinking was collected, and converted to grams of absolute alcohol (gAA). The PAE T1 group had 24.1 gAA per week prior to pregnancy recognition, with 0.1 gAA per week on average post pregnancy recognition and included nine with binge-level PAE prior to pregnancy recognition. The PAE T1-T3 group had 39.2 gAA per week on average prior to pregnancy recognition and < 11 gAA per week throughout the remainder of pregnancy and included 13 with binge-level PAE prior to pregnancy recognition. A binge drinking episode was defined as ≥ 50 gAA per occasion (Muggli et al. 2022a, b). Children whose mothers were abstinent from alcohol throughout pregnancy comprised the control group.

Attention measures and covariates

Attention measures are summarized in Table 1. We evaluated four domains of attention: focus, sustain, shift and behavioral hyperactivity, impulsivity and inattentiveness using validated tools (Table 1).

Table 1.

Attention measures

Attention domain Scale/subtest
Focus Balloon Hunt 5 from the Test of Everyday Attention (TEA-Ch2) (Manly et al., 2016) estimated focused attention. This is a speeded cancellation task. Raw scores, equal to the number of crossed targets divided by the time, were converted to scaled scores (M = 10, SD = 3) based on the child’s age and sex, with higher scores reflecting superior focused attention.
Sustain Barking from the TEA-Ch2 (Manly et al., 2016) required children to count tones over 10 trials. Correct responses were converted to scaled scores based on the child’s age and sex (M = 10, SD = 3), with higher scores reflecting better sustained attention.
Shift The Contingency Naming Test, trial three measured shifting attention (Taylor et al., 1987). An array of shapes (circle, triangle and square) with smaller internal shapes of differing colors (pink, green and blue) were presented and were named according to a rule. Efficiency scores were used in analyses: efficiency= (Inline graphic) x 100 (Anderson et al., 2000).
Behavioral hyperactivity, impulsivity and inattentiveness The Attention Deficit Hyperactivity Disorder Rating Scale (ADHD-RS-5) (DuPaul et al., 2016) is an 18-item questionnaire. Parents rated their child’s home behavior on a 4-point scale (0, never/rarely to 3, very often). Scores were summed and converted to percentile ranks based on the child’s age and sex, with higher scores indicative of behavioral attention problems.
Open in a new tab

Social risk, child age and sex, intracranial volume and binge PAE were included as covariates (Ghazi Sherbaf et al., 2019; Suades-Gonzalez et al., 2017; Ursache et al., 2016). Social risk was estimated by the Social Risk Index which measured: family structure, education of the primary caregiver, occupation and employment of the primary income earner, and maternal language and age at the child’s birth (Roberts et al., 2008). Raw scores were dichotomized around the median (lower social risk 0–1; higher social risk ≥2–12).

MRI acquisition

MRI data were acquired on a Siemens Magnetom Prisma 3 T scanner. Diffusion-weighted MRI data were acquired with 60 gradient directions, b-value = 2800 s/mm2, 10 b = 0 s/mm2 images, 2 mm isotropic voxels, multi-band acceleration factor = 2, repetition time = 3500 ms, echo time = 67 ms. An additional pair of b = 0 s/mm2 images with opposite phase encodings were acquired to correct for echo planar imaging-induced geometric distortions in the diffusion MRI data. T1-weighted images were also acquired (3D magnetization-prepared gradient-echo (MP-RAGE) sequences with prospective motion correction, echo times = 2.14, 3.94, 5.77 and 7.5ms, repetition time = 2550 ms, Field of View = 256 × 256 mm2, matrix = 288 × 288, 208 sagittal slices, slice thickness = 0.9 mm, interpolated voxel size = 0.9 × 0.4 × 0.4 mm3) and were used to generate a measure of intracranial volume for statistical analyses, using the standard FreeSurfer version 7.1.1 recon-all pipeline (Fischl, 2012).

Diffusion MRI processing

Diffusion MRI data were processed predominantly using MRtrix3 version 3.0.1 (Tournier et al., 2012), MRtrix3Tissue (https://3Tissue.github.io, a fork of MRtrix3; version 5.2.8), and Functional MRI of the Brain Software Library (FSL 6.0.3) (Jenkinson et al., 2012), following all steps in the typical FBA pipeline (Dhollander et al. 2021a, b). Pre-processing included correction for Gibbs-ringing artefact (Kellner et al., 2016), participant movement, eddy current-induced distortions, susceptibility-induced distortions and susceptibility-by-movement interactions (Andersson et al., 2016, 2017, 2018; Andersson & Sotiropoulos, 2016). Image quality control was performed by visual inspection and automatically using the FSL QUAD and SQUAD tools (Bastiani et al., 2019).

The following steps were performed: estimation and averaging of 3-tissue response functions (Dhollander et al., 2019), upsampling to 1.5 mm isotropic voxels, Single-Shell 3-Tissue Constrained Spherical Deconvolution (SS3T-CSD) (Dhollander & Connelly, 2016), and bias field correction and global intensity normalization (Thijs Dhollander et al. 2021a, b). These steps resulted in the white matter fiber orientation distribution, grey matter, and cerebrospinal fluid compartment images for each participant. A study-specific white matter fiber orientation distribution template was created by iterative registration and averaging of a random selection of 30 participants’ fiber orientation distribution images from each group, following which all participants’ images were registered and warped to the template (Raffelt et al., 2011). For each participant in template space, fixels were segmented and reorientated, and correspondence with common template fixels was established. Finally, the fixel-wise FD, FC (in log form) and FDC metrics were computed (Raffelt et al., 2017).

Statistical analyses

Demographic and attention variables were analyzed using two-sample t-tests for continuous variables and chi squared tests for categorical variables in Stata Statistical Software Release 16 (StataCorp., 2019).

General Linear Models assessed associations between attention and FD, FC and FDC per subgroup (Control, PAE T1, PAE T1-T3) at the fixel-wise level. Analyses were adjusted for age at MRI, sex, social risk, and intracranial volume, which was log-transformed and only included in FC and FDC models (Smith et al., 2019). Connectivity based smoothing and statistical inference were performed using connectivity-based fixel enhancement (Raffelt et al., 2015). Non-parametric permutation testing with 5000 permutations were used to generate family-wise error rate (FWE) corrected p-values per fixel.

Interaction analyses investigated whether the association of FD, FC and FDC metrics with attention differed between groups (controls versus PAE T1 versus PAE T1-T3), using General Linear Models adjusted for age at MRI, sex, social risk, and intracranial volume for FC and FDC models. Connectivity-based fixel enhancement, and non-parametric permutation testing were performed. For interpretation of significant group-by-attention interactions, FD, FC and FDC were extracted from fiber pathways (Wasserthal et al., 2018) and associated with attention in each group.

Finally, we conducted sensitivity analyses that adjusted for binge PAE. These analyses followed the same approach as above.

Results

Of the 146 children who had an MRI, 14 were excluded due to incomplete acquisition of diffusion MRI and three were excluded due to movement artefacts. The final sample comprised 129 children (66 female, 63 male) of mean age 7.2 (SD 0.2) years (controls, n = 37; PAE T1 n = 47; PAE T1-T3 n = 45). Table 2 displays their demographic characteristics. Mothers were predominantly aged 30 years or older at birth (76%), highly educated (69% tertiary), and of normal or underweight BMI pre-pregnancy (60%). Controls had a higher social risk background than the PAE T1 group. Prenatal alcohol exposure patterns varied across trimesters. In the PAE T1 group, exposure to alcohol dropped to almost zero once mothers became aware of their pregnancy. Prior to pregnancy recognition, mean weekly exposure was 24.1 g of absolute alcohol (gAA) (SD = 73.7) with a mean maximum exposure of 32.1 gAA per occasion (SD = 35.3). One standard drink in Australia equates to 10 gAA; these exposure levels translate to approximately 2.4 standard drinks per week with a maximum of 3.2 standard drinks per occasion. To contextualise, 3.2 standard drinks is equivalent to approximately 2.2 glasses (150 ml) of 12% wine. Almost one in five children (18%) in the PAE T1 group were exposed to at least one binge-level episode (≥ 5 standard drinks per occasion) during this period.

Table 2.

Participant characteristics

Controls PAE T1 PAE T1-T3
N (%) 37 (28.7) 47 (36.4) 45 (34.9)
Maternal characteristics
Maternal age at birth N (%)
< 25 years 2 (5.4) 1 (2.1) 0 (0)
25–29 years 7 (18.9 13 (27.7) 8 (17.8)
30–34 years 14 (37.8) 14 (29.8) 20 (44.4)
≥ 35 years 14 (37.8) 19 (40.4) 17 (37.8)
Maternal education N (%)
High school 2 (5.4) 6 (12.8) 1 (2.2)
Trade/diploma 14 (37.8) 8 (17.0) 9 (20.0)
University 21 (56.8) 33 (70.2) 35 (77.8)
Pre-pregnancy BMI N (%)
Normal/underweight (< 25) 19 (54.3) 28 (62.2) 27 (61.4)
Overweight (25–29.9.9) 6 (17.1) 8 (17.8) 11 (25.0)
Obese (≥ 30) 10 (28.6) 9 (20.0) 6 (13.6
Higher social risk background N (%) 17 (46.0)a 11 (23.4)a 15 (33.3)
Prenatal Alcohol exposure
Trimester 1, pre-awareness
Mean gAA/week (SD) 24.1 (73.7) 39.2 (51.5)
Mean gAA/Occasion (SD) 32.1 (35.3) 36.1 (37.2)
Binge episode N (%) 9 (19.1) 13 (28.9)
Trimester 1, post-awareness
Mean gAA/week (SD) 0.1 (0.5) 7.4 (20.8)
Mean gAA/Occasion (SD) 0.9 (2.7) 7.2 (9.1)
Binge episode N (%) Nil
Trimester 2
Mean gAA/week (SD) 10.0 (20.2)
Mean gAA/Occasion (SD) 9.7 (7.1)
Binge episode N (%) Nil
Trimester 3
Mean gAA/week (SD) 6.2 (10.5)
Mean gAA/Occasion (SD) 8.4 (6.7)
Binge episode N (%) Nil
Child characteristics
Child age (years) at MRI, M (SD) 7.1 (0.2) 7.2 (0.2) 7.2 (0.2)
Child sex (female), N (%) 18 (48.6) 29 (61.7) 19 (42.2)
Total intracranial volume (cm3), M (SD)b 1503 (106) 1539 (143) 1598 (122)
Total brain tissue volume (cm3), M (SD)b 1181 (98) 1199 (108) 1243 (99)
Total cortical volume (cm3), M (SD)b 586 (52) 593 (54) 618 (47)
Total white matter volume (cm3), M (SD)b 399 (42) 405 (47) 420 (45)
Focused attention standard score, M (SD) 9.2 (3.0) 8.5 (2.9) 9.6 (2.8)
Shifting attention efficiency score, M (SD)c 1.2 (0.43) 1.13 (0.37) 1.20 (0.44)
Sustained attention standard score, M (SD)c 9.7 (3.1) 9.4 (2.8) 10.2 (3.0)
Hyperactive subscale percentile rank, M (SD) 46.8 (35.1) 39.2 (26.4) 44.5 (30.8)
Inattentive subscale percentile rank, M (SD) 45.2 (31.8) 41.7 (26.8) 50.6 (28.2)
Open in a new tab

GAA grams of absolute alcohol; SD standard deviation; M mean

ap<0.05 for difference between controls and PAE T1 group, bVolumes were derived from FreeSurfer software, as previously described(Thompson et al., 2024), csample size for controls n = 36 for the analysis due to missing data

The PAE T1-T3 group showed continued exposure throughout gestation. Prior to pregnancy recognition, mean weekly exposure was 39.2 gAA (SD = 51.5) with a mean maximum exposure of 36.1 gAA per occasion (SD = 37.2), equivalent to approximately 2.5 glasses of wine per occasion. Approximately 3 in 10 children (29%) were exposed to at least one binge-level episode during this period. Following pregnancy recognition, exposure levels dropped substantially, with mean weekly levels ranging from 6.2 to 10.0 gAA and mean maximum levels per occasion ranging from 6.2 to 9.7 gAA across trimesters two and three. Maximum exposure did not reach 50 gAA per occasion in trimesters two and three (i.e., no binge-level exposure). There were no differences in age, sex or attention between groups. Controls had a higher social risk background than the PAE T1 group.

Associations between attention and white matter within each group

In controls, greater FC in the cerebellar white matter and the corticospinal tract were associated with less hyperactivity and better focused attention, respectively (Fig. 1A and B). In the PAE T1 group, greater FD in the splenium of the corpus callosum and lower FC in the external capsule were associated with higher inattentiveness (Fig. 1C and D).

Fig. 1.

Fig. 1

Open in a new tab

Associations between attention domains and fixel-based metrics. Figures were generated by highlighting streamline segments from the whole-brain tractogram that passed through fixels that were significantly associated with attention performance (pFWE<0.05). Streamlines were colored by pFWE value. Figures illustrate the relationship between (A) fewer hyperactive symptoms with higher FC for Control group; (B) higher focused attention with higher FC for the Control group; (C) higher inattentiveness with higher FD for the PAE T1 group; (D) higher inattentiveness with lower FC for the PAE T1 group. All significant results were obtained from models that adjusted for intracranial volume (for FC and FDC), age, sex and social risk

Differences in white matter-attention associations between groups

There was a significant group interaction effect (controls vs. PAE T1 group) for the association between FDC in the left corticospinal tract and focused attention (Fig. 2A). There was also a significant group interaction effect (controls vs. PAE T1-T3 group) for the association between FC in the cerebellar white matter and hyperactivity (Fig. 2B). Both significant interaction effects highlighted the strong relationships between white matter metrics and attention metrics in the control group, whereas the relationships between white matter metrics and attention metrics were weaker in the PAE groups (Fig. 3).

Fig. 2.

Fig. 2

Open in a new tab

Group-by-attention interactions in fixel-based metrics. Figures were generated by highlighting streamline segments from the whole brain tractogram that passed through fixels that exhibited a significant group-by-attention interaction (pFWE<0.05). Streamlines were colored by pFWE value. Figures depict significant group-by-attention interaction effects for, (A) FDC and focused attention for the Control versus the PAE T1 group; and (B) for FC and hyperactive attention for the Control versus the PAE T1-T3 group. All significant results were obtained from models that adjusted for intracranial volume (for FC and FDC), age, sex and social risk

Fig. 3.

Fig. 3

Open in a new tab

Scatterplot of significant interaction effects. Associations between (A) FDC of the left corticospinal tract (CST) and focused attention separately for the control and PAE T1 groups; (B) FC of the middle cerebellar peduncle (MCP) and hyperactivity separately for the control and PAE T1-T3 groups. Plots demonstrate that for the control group (in blue), higher FBA metrics (FDC and FC) were strongly associated with greater focused attention and less hyperactivity (p = 0.004 and p = 0.003). Alternatively, in the PAE groups (in orange) weaker opposite direction associations were seen (p = 0.7 for focused attention and p = 0.01 for hyperactivity)

Sensitivity analyses adjusted for binge PAE

After adjustment for binge PAE, the association between greater FD in the splenium and higher inattentiveness in the PAE T1 group remained (Supplementary Fig. 1), but otherwise there were no significant associations between attention and white matter within the PAE groups. Interactions were consistent with the above findings but spanned a larger spatial extent (Supplementary Fig. 2).

Discussion

To understand the biological and clinical implications of low-moderate alcohol consumption patterns during pregnancy, we investigated the relationship between whole-brain white matter micro- and macro-structure and attention in children. There was evidence that specific pathways in white matter were associated with attention. In controls, greater white matter FC in the cerebellum was associated with less hyperactivity and in the corticospinal tract with better focused attention. For the PAE T1 group, greater FD in the splenium of the corpus callosum was associated with greater inattentiveness, while reduced FC in the external capsule was associated with higher inattentiveness. However, the latter finding did not persist after controlling for binge PAE. There was some evidence that associations differed between PAE and control groups. Relationships were stronger in the control than PAE T1group for associations between higher FDC in the left corticospinal tract and focused attention. Relationships were also stronger for controls than the PAE T1-T3 group for associations between higher FC in the cerebellar white matter with less hyperactivity. These interactions persisted after controlling for binge PAE.

Associations between the structure of the cerebellum and hyperactivity suggest that a smaller cross-sectional area of these fibers underlies a reduced capacity for typically developing children to regulate overactivity. This is consistent with past literature on the role of the cerebellum in movement (Ramnani, 2006), behavioral regulation (Baillieux et al., 2008), executive function (Salman & Tsai, 2016) and attention (Ge et al., 2013).

The association between lower FDC in the corticospinal tract and focused attention in controls suggests a slower capacity of corticospinal tract fibers to relay information, leading to poorer performance. Although the corticospinal tract is not typically implicated in attentional processes, its involvement in this study may reflect the fine motor and response time aspects of the task given evidence of this somatosensory tract’s role in bimanual coordination (Fuelscher et al., 2021) and sensorimotor speed (Karahan et al., 2019). Lateralization to the left is consistent with previous findings that manual dexterity is associated with FC on the contralateral side to the predominant hand (Fuelscher et al., 2021). Biologically, lower FDC reflects a slower capacity of corticospinal tract fibers to relay information, leading to poorer performance.

Neuropathology in the posterior regions of the corpus callosum, particularly the splenium, has been identified in individuals with FASD and associated with cognitive deficits including in verbal learning and executive dysfunction (Ghazi Sherbaf et al., 2019). Thinning, hypoplasia and volume loss of the splenium in children with FASD (Spadoni et al., 2007), as well as dose-response associations between diffusion tensor imaging metrics and FASD severity (Fryer et al., 2009; Li et al., 2009), have led to suggestions that this region is particularly vulnerable to PAE (Ghazi Sherbaf et al., 2019). Our results extend findings by implicating this structure in attention difficulties for children with PAE in T1 only. Unexpectedly, higher FD was associated with increased inattentiveness. While counterintuitive, this may align with previous reports linking executive dysfunction to corpus callosum thickening in FASD populations (Bookstein et al., 2002). The biological underpinnings of this association remain unclear, as several mechanisms could contribute to the observed effect such as axonal atrophy, increased intra-cellular volume, or age-related differences in white matter maturation. Accordingly, these findings should be interpreted with caution (Dhollander et al. 2021a, b).

The cingulum and superior longitudinal fasciculus have previously been associated with attention (Chiang et al., 2015; Donald et al., 2015), which was contradictory to our current findings and hypotheses. Associations may be less widespread because FBA is more biologically specific and robust to fiber geometry than other techniques previously used (Raffelt et al., 2015). The superior longitudinal fasciculus has many fiber populations of differing orientations which likely confounded previous relationships (Schilling et al., 2017). FBA investigations have similarly found constricted associations, even in populations with more extensive white matter injury (Bauer et al., 2020; Wallace et al., 2020).

Other than the differences in FDC in the left corticospinal tract and FC in the cerebellar white matter, there was limited evidence that associations differed based on PAE timing. Moreover, data across groups showed substantial overlap when examining interaction effects, suggesting minimal divergence in white matter measures. Large variance in cognition is needed to detect differences related to the timing of PAE (Lebel et al., 2011). Limited variance in our sample may suggest a weak effect of low-moderate PAE patterns on the white matter that subserves attention. Additionally, PAE groups were classified based on exposure timing (T1 only versus throughout T1-T3). Future research would benefit from larger cohorts with the capacity to utilize more objective statistical classification, such as group-based trajectory modelling (Nagin & Odgers, 2010).

Technical limitations of our study are important to consider when interpreting the findings. Notably, we observed no group differences in attention scores, and the range of scores across all attention measures was narrow. This restricted variability may have limited the sensitivity of these measures to detect subtle effects of PAE, particularly in sustained attention where group performance was above average. Furthermore, the sustained attention task was auditory in nature, which is reportedly less affected by PAE (Ware et al., 2021). Future research should therefore investigate visual sustained attention. Several neurobiological and epigenetic factors that may have influenced the results were not assessed such as maternal substance use and nutritional characteristics. Additionally, our exposure measures did not account for individual differences in maternal alcohol metabolism (influenced by factors such as age, weight, and genetic variations in metabolizing enzymes), which may have introduced measurement error and attenuated the strength of observed associations. Maternal education levels were higher in the PAE groups compared with controls, consistent with the larger AQUA cohort demographics. As higher maternal education is typically associated with better child neurodevelopmental outcomes, this pattern would be expected to bias results toward the null, making our detected associations between PAE and white matter microstructure conservative estimates of true effects. Nonetheless, these limitations suggest that our findings should be considered preliminary and interpreted with caution.

Conclusion

This study provides insights into the neurobehavioral sequelae of low-moderate PAE patterns by delineating white matter correlates of attention skills in children with and without PAE. In those without PAE, greater cross-sectional area of cerebellum fibers was associated with less hyperactivity and in the corticospinal tract with better focused attention. In those with PAE in T1 only, higher density of the splenium fibers was associated with more inattentiveness, while reduced cross-sectional area of external capsule fibers was associated with more inattentiveness. There was evidence of subtle group differences in the associations between white matter structure and attention, where relationships were stronger in control than PAE groups. Further research is required to determine the reproducibility of these preliminary findings.

Supplementary Information

Below is the link to the electronic supplementary material.

ESM 1 (547KB, docx)

(DOCX 546 KB)

Acknowledgements

We thank the families of the AQUA cohort who have participated in this research and contributed greatly to our understanding of alcohol during pregnancy. We also acknowledge the developmental imaging team at the Murdoch Children’s Research Institute and Michael Kean and Radiographers at the Royal Children’s Hospital, Melbourne, for their expertise that has made this research possible.

Abbreviations

ADHD

Attention Deficit and Hyperactivity Disorder

AQUA

Asking Questions About Alcohol in Pregnancy cohort

FASD

Fetal Alcohol Spectrum Disorder

FBA

Fixel–Based Analysis

FC

Fiber Cross Section

FD

Fiber Density

FDC

Fiber Density and Cross Section

FSL

Functional MRI of the Brain Software Library

FWE

family–wise error rate

PAE

Prenatal Alcohol Exposure

T1

Trimester 1 only

T1-T3

Trimester 1 through to Trimester 3 (throughout pregnancy)

Author contributions

Author contributions included conception and study design (PP, CK, EE, AP, SH, SL, AS, RT, JH, EM, DT, PA), funding acquisition (EE, AP, SH, SL, AS, JH, EM, DT, PA), data collection or acquisition (PP, CK, TD, GD, SC, JH, EM), statistical analysis (PP, CK, TD), interpretation of results (PP, CK, TD, RT, JH, DT, PA), drafting the manuscript work or revising it critically for important intellectual content (All authors) and approval of final version to be published and agreement to be accountable for the integrity and accuracy of all aspects of the work (All authors).

Funding

Open Access funding enabled and organized by CAUL and its Member Institutions. This research was supported by the National Health and Medical Research Council of Australia (NHMRC) [Project Grant 1446635 to PA, JH, EE, AP, DT, EM, AS, SL and SH; Centre of Research Excellence in Newborn Medicine 1153176 to AS, DT and PA; Career Development Fellowship APP1160003 to DT and APP1159533 to AS; Investigator Grants APP1176077 to PA and APP2026176 to EE; Practitioner Fellowship GNT1021480 to EE]; a Medical Research Futures Fund Next Generation Fellowship [MRF1135959 to EE]; Australian Government Research Training Program (RTP) scholarships to PP, SC and CK; Monash University Graduate Excellence Scholarship to CK; the Murdoch Children’s Research Institute; the Royal Children’s Hospital Foundation; the Department of Paediatrics at The University of Melbourne; and the Victorian Government’s Operational Infrastructure Support Program.

Data availability

As the study is ongoing, data will not be publicly available.

Code availability

As the study is ongoing, code will not be publicly available.

Declarations

Ethics approval

Ethics approval was provided by the Human Research Ethics Committee of the Royal Children’s Hospital, Melbourne, Australia (approval #38025).

Consent for publication

All authors approved the final manuscript for publication.

Consent to participate

All caregivers provided written informed consent for study participation.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  1. Anderson, P. J., Anderson, V., Northam, E., & Taylor, H. G. (2000). Standardization of the contingency naming test (CNT) for school-aged children: a measure of reactive flexibility. In Standardization of the Contingency Naming Test (CNT) for school-aged children: A measure of reactive flexibility (Vol. 1, pp. 247–273).
  2. Andersson, J. L. R., Graham, M. S., Drobnjak, I., Zhang, H., & Campbell, J. (2018). Susceptibility-induced distortion that varies due to motion: Correction in diffusion MR without acquiring additional data. NeuroImage,171, 277–295. 10.1016/j.neuroimage.2017.12.040 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Andersson, J. L. R., Graham, M. S., Drobnjak, I., Zhang, H., Filippini, N., & Bastiani, M. (2017). Towards a comprehensive framework for movement and distortion correction of diffusion MR images: Within volume movement. NeuroImage,152, 450–466. 10.1016/j.neuroimage.2017.02.085 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Andersson, J. L. R., Graham, M. S., Zsoldos, E., & Sotiropoulos, S. N. (2016). Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion correction of diffusion MR images. NeuroImage,141, 556–572. 10.1016/j.neuroimage.2016.06.058 [DOI] [PubMed] [Google Scholar]
  5. Andersson, J. L. R., & Sotiropoulos, S. N. (2016). An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. NeuroImage,125, 1063–1078. 10.1016/j.neuroimage.2015.10.019 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Baillieux, H., De Smet, H. J., Paquier, P. F., De Deyn, P. P., & Marien, P. (2008). Cerebellar neurocognition: Insights into the bottom of the brain. Clinical Neurology and Neurosurgery,110(8), 763–773. 10.1016/j.clineuro.2008.05.013 [DOI] [PubMed] [Google Scholar]
  7. Bastiani, M., Cottaar, M., Fitzgibbon, S. P., Suri, S., Alfaro-Almagro, F., Sotiropoulos, S. N., Jbabdi, S., & Andersson, J. L. R. (2019). Automated quality control for within and between studies diffusion MRI data using a non-parametric framework for movement and distortion correction. NeuroImage,184, 801–812. 10.1016/j.neuroimage.2018.09.073 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Bauer, T., Ernst, L., David, B., Becker, A. J., Wagner, J., Witt, J. A., Helmstaedter, C., Weber, B., Hattingen, E., Elger, C. E., Surges, R., & Ruber, T. (2020). Fixel-based analysis links white matter characteristics, serostatus and clinical features in limbic encephalitis. NeuroImage: Clinical,27, Article 102289. 10.1016/j.nicl.2020.102289 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Bookstein, F. L., Sampson, P. D., Connor, P. D., & Streissguth, A. P. (2002). Midline corpus callosum is a neuroanatomical focus of fetal alcohol damage. Anatomical Record, 269(3), 162–174. ://MEDLINE:12124903. [DOI] [PubMed] [Google Scholar]
  10. Bower, C., & Elliott, E. J. (2016). Australian Guide to the diagnosis of Fetal Alcohol Spectrum Disorder (FASD). https://fasdhub.org.au/fasd-information/australian-guidelines-for-assessment-and-diagnosis-of-fasd/ [DOI] [PMC free article] [PubMed]
  11. Chiang, H. L., Chen, Y. J., Lo, Y. C., Tseng, W. Y., & Gau, S. S. (2015). Altered white matter tract property related to impaired focused attention, sustained attention, cognitive impulsivity and vigilance in attention-deficit/ hyperactivity disorder. Journal of Psychiatry & Neuroscience,40(5), 325–335. 10.1503/jpn.140106 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Chiang, H. L., Hsu, Y. C., Shang, C. Y., Tseng, W. I., & Gau, S. S. (2020). White matter endophenotype candidates for ADHD: A diffusion imaging tractography study with sibling design. Psychological Medicine,50(7), 1203–1213. 10.1017/S0033291719001120 [DOI] [PubMed] [Google Scholar]
  13. Dhollander, T., Clemente, A., Singh, M., Boonstra, F., Civier, O., Duque, J. D., Egorova, N., Enticott, P., Fuelscher, I., Gajamange, S., Genc, S., Gottlieb, E., Hyde, C., Imms, P., Kelly, C., Kirkovski, M., Kolbe, S., Liang, X., Malhotra, A., & Caeyenberghs, K. (2021a). Fixel-based analysis of diffusion MRI: Methods, applications, challenges and opportunities. NeuroImage,241, Article 118417. 10.1016/j.neuroimage.2021.118417 [DOI] [PubMed] [Google Scholar]
  14. Dhollander, T., & Connelly, A. (2016). A novel iterative approach to reap the benefits of multi-tissue CSD from just single-shell (+ b = 0) diffusion MRI data (24.). International Society of Magnetic Resonance in Medicine. [Google Scholar]
  15. Dhollander, T., Mito, R., Raffelt, D., & Connelly, A. (2019). Improved white matter response function estimation for 3-tissue constrained spherical deconvolution (27.). International Society of Magnetic Resonance in Medicine. [Google Scholar]
  16. Dhollander, T., Tabbara, R., Rosnarho-Tornstrand, J., Tournier, J. D., Raffelt, D., & Connelly, A. (2021b). Multi-tissue log-domain intensity and inhomogeneity normalisation for quantitative apparent fibre density. 29th International Society of Magnetic Resonance in Medicine, Online.
  17. Donald, K. A., Roos, A., Fouche, J. P., Koen, N., Howells, F. M., Woods, R. P., Zar, H. J., Narr, K. L., & Stein, D. J. (2015). A study of the effects of prenatal alcohol exposure on white matter microstructural integrity at birth. Acta Neuropsychiatrica,27(4), 197–205. 10.1017/neu.2015.35 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. DuPaul, G. J., Power, T. J., Anastopoulos, A. D., & Reid, R. (2016). ADHD Rating Scale-5 for children and adolescents: Checklists, norms, and clinical interpretation. Guilford Press. [Google Scholar]
  19. Fan, J., Jacobson, S. W., Taylor, P. A., Molteno, C. D., Dodge, N. C., Stanton, M. E., Jacobson, J. L., & Meintjes, E. M. (2016). White matter deficits mediate effects of prenatal alcohol exposure on cognitive development in childhood. Human Brain Mapping,37(8), 2943–2958. 10.1002/hbm.23218 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Fischl, B. (2012). FreeSurfer. NeuroImage,62(2), 774–781. 10.1016/j.neuroimage.2012.01.021 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Flak, A. L., Su, S., Bertrand, J., Denny, C. H., Kesmodel, U. S., & Cogswell, M. E. (2014). The association of mild, moderate, and binge prenatal alcohol exposure and child neuropsychological outcomes: A meta-analysis. Alcoholism, Clinical and Experimental Research,38(1), 214–226. 10.1111/acer.12214 [DOI] [PubMed] [Google Scholar]
  22. Fryer, S. L., Schweinsburg, B. C., Bjorkquist, O. A., Frank, L. R., Mattson, S. N., Spadoni, A. D., & Riley, E. P. (2009). Characterization of white matter microstructure in fetal alcohol spectrum disorders. Alcoholism, Clinical and Experimental Research,33(3), 514–521. 10.1111/j.1530-0277.2008.00864.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Fuelscher, I., Hyde, C., Anderson, V., & Silk, T. J. (2021). White matter tract signatures of fiber density and morphology in ADHD. Cortex; a Journal Devoted to the Study of the Nervous System and Behavior,138, 329–340. 10.1016/j.cortex.2021.02.015 [DOI] [PubMed] [Google Scholar]
  24. Ge, H., Yin, X., Xu, J., Tang, Y., Han, Y., Xu, W., Pang, Z., Meng, H., & Liu, S. (2013). Fiber pathways of attention subnetworks revealed with tract-based spatial statistics (TBSS) and probabilistic tractography. PLoS One,8(11), Article e78831. 10.1371/journal.pone.0078831 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Ghazi Sherbaf, F., Aarabi, M. H., Hosein Yazdi, M., & Haghshomar, M. (2019). White matter microstructure in fetal alcohol spectrum disorders: A systematic review of diffusion tensor imaging studies. Human Brain Mapping,40(3), 1017–1036. 10.1002/hbm.24409 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Guerri, C. (1998). Neuroanatomical and neurophysiological mechanisms involved in central nervous system dysfunctions induced by prenatal alcohol exposure. Alcoholism, Clinical and Experimental Research, 22(2), 304–312. 10.1111/j.1530-0277.1998.tb03653.x [DOI] [PubMed] [Google Scholar]
  27. Halliday, J. L., Muggli, E., Lewis, S., Elliott, E. J., Amor, D. J., O’Leary, C., Donath, S., Forster, D., Nagle, C., Craig, J. M., & Anderson, P. J. (2017). Alcohol consumption in a general antenatal population and child neurodevelopment at 2 years. Journal of Epidemiology and Community Health,71(10), 990–998. 10.1136/jech-2017-209165 [DOI] [PubMed] [Google Scholar]
  28. Jacobson, J. L., Akkaya-Hocagil, T., Ryan, L. M., Dodge, N. C., Richardson, G. A., Olson, H. C., Coles, C. D., Day, N. L., Cook, R. J., & Jacobson, S. W. (2021). Effects of prenatal alcohol exposure on cognitive and behavioral development: Findings from a hierarchical meta-analysis of data from six prospective longitudinal U.S. cohorts. Alcoholism, Clinical and Experimental Research,45(10), 2040–2058. 10.1111/acer.14686 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W., & Smith, S. M. (2012). Fsl. NeuroImage,62(2), 782–790. 10.1016/j.neuroimage.2011.09.015 [DOI] [PubMed] [Google Scholar]
  30. Jones, D. K., Knosche, T. R., & Turner, R. (2013). White matter integrity, fiber count, and other fallacies: The do’s and don’ts of diffusion MRI. NeuroImage,73, 239–254. 10.1016/j.neuroimage.2012.06.081 [DOI] [PubMed] [Google Scholar]
  31. Karahan, E., Costigan, A. G., Graham, K. S., Lawrence, A. D., & Zhang, J. (2019). Cognitive and white-matter compartment models reveal selective relations between corticospinal tract microstructure and simple reaction time. The Journal of Neuroscience,39(30), 5910–5921. 10.1523/JNEUROSCI.2954-18.2019 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Kellner, E., Dhital, B., Kiselev, V. G., & Reisert, M. (2016). Gibbs-ringing artifact removal based on local subvoxel-shifts. Magnetic Resonance in Medicine,76(5), 1574–1581. 10.1002/mrm.26054 [DOI] [PubMed] [Google Scholar]
  33. Klarborg, B., Skak Madsen, K., Vestergaard, M., Skimminge, A., Jernigan, T. L., & Baare, W. F. (2013). Sustained attention is associated with right superior longitudinal fasciculus and superior parietal white matter microstructure in children. Human Brain Mapping, 34(12), 3216–3232. 10.1002/hbm.22139 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Kodituwakku, P. W., Segall, J. M., & Beatty, G. K. (2011). Cognitive and behavioral effects of prenatal alcohol exposure. Future Neurology,6(2), 237–259. 10.2217/fnl.11.4 [Google Scholar]
  35. Lebel, C., Rasmussen, C., Wyper, K., Andrew, G., & Beaulieu, C. (2010). Brain microstructure is related to math ability in children with fetal alcohol spectrum disorder. Alcoholism, Clinical and Experimental Research, 34(2), 354–363. 10.1111/j.1530-0277.2009.01097.x [DOI] [PubMed] [Google Scholar]
  36. Lebel, C., Roussotte, F., & Sowell, E. R. (2011). Imaging the impact of prenatal alcohol exposure on the structure of the developing human brain. Neuropsychology Review, 21(2), 102–118. 10.1007/s11065-011-9163-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Liang, X., Kelly, C. E., Yeh, C. H., Dhollander, T., Hearps, S., Anderson, P. J., & Thompson, D. K. (2024). Structural brain network organization in children with prenatal alcohol exposure. NeuroImage: Clinical,44, Article 103690. 10.1016/j.nicl.2024.103690 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Li, L., Coles, C. D., Lynch, M. E., & Hu, X. (2009). Voxelwise and skeleton-based region of interest analysis of fetal alcohol syndrome and fetal alcohol spectrum disorders in young adults. Human Brain Mapping,30(10), 3265–3274. 10.1002/hbm.20747 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Manly, T., Anderson, V., Crawford, J., George, M., & Robertson, I. H. (2016). Test of everyday attention for children ((2nd ed.)). Pearson Education. [Google Scholar]
  40. Muggli, E., Cook, B., O’Leary, C., Forster, D., & Halliday, J. (2010). Alcohol in pregnancy: What questions should we be asking? Report to the Commonwealth department of health and ageing. Murdoch Children’s Research Institute.
  41. Muggli, E., Halliday, J., Elliott, E. J., Penington, A., Thompson, D., Spittle, A. J., Forster, D., Lewis, S., Hearps, S., & Anderson, P. J. (2022a). Cohort profile: Early school years follow-up of the Asking Questions about Alcohol in Pregnancy Longitudinal Study in Melbourne, Australia (AQUA at 6). BMJ Open,12(1), Article e054706. 10.1136/bmjopen-2021-054706 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Muggli, E., Halliday, J., Hearps, S., Nguyen, T. N., Penington, A., Thompson, D. K., Spittle, A., Forster, D. A., Lewis, S., Elliott, E. J., & Anderson, P. J. (2024). Low to moderate prenatal alcohol exposure and neurodevelopment in a prospective cohort of early school aged children. Scientific Reports,14(1), Article 7302. 10.1038/s41598-024-57938-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Muggli, E., Hearps, S., Halliday, J., Elliott, E. J., Penington, A., Thompson, D. K., Spittle, A., Forster, D. A., Lewis, S., & Anderson, P. J. (2022b). A data driven approach to identify trajectories of prenatal alcohol consumption in an Australian population-based cohort of pregnant women. Scientific Reports,12(1), Article 4353. 10.1038/s41598-022-08190-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Muggli, E., Matthews, H., Penington, A., Claes, P., O’Leary, C., Forster, D., Donath, S., Anderson, P. J., Lewis, S., Nagle, C., Craig, J. M., White, S. M., Elliott, E. J., & Halliday, J. (2017). Association between prenatal alcohol exposure and craniofacial shape of children at 12 months of age. JAMA Pediatrics,171(8), 771–780. 10.1001/jamapediatrics.2017.0778 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Muggli, E., O’Leary, C., Donath, S., Orsini, F., Forster, D., Anderson, P. J., Lewis, S., Nagle, C., Craig, J. M., Elliott, E., & Halliday, J. (2016). Did you ever drink more? A detailed description of pregnant women’s drinking patterns. BMC Public Health,16, 683. 10.1186/s12889-016-3354-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Muggli, E., O’Leary, C., Forster, D., Anderson, P., Lewis, S., Nagle, C., Craig, J. M., Donath, S., Elliott, E., & Halliday, J. (2014). Study protocol: Asking QUestions about Alcohol in pregnancy (AQUA): A longitudinal cohort study of fetal effects of low to moderate alcohol exposure. BMC Pregnancy and Childbirth,14, Article 302. 10.1186/1471-2393-14-302 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Nagel, B. J., Bathula, D., Herting, M., Schmitt, C., Kroenke, C. D., Fair, D., & Nigg, J. T. (2011). Altered white matter microstructure in children with attention-deficit/hyperactivity disorder. Journal of the American Academy of Child and Adolescent Psychiatry,50(3), 283–292. 10.1016/j.jaac.2010.12.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Nagin, D. S., & Odgers, C. L. (2010). Group-based trajectory modeling in clinical research. Annual Review of Clinical Psychology,6, 109–138. 10.1146/annurev.clinpsy.121208.131413 [DOI] [PubMed] [Google Scholar]
  49. Ogunjimi, E., Guebert, A. F., Anderson, T., Derkson, J., Okonji, P. E., & Mela, M. (2025). The Long-Term effects of prenatal alcohol exposure on offspring: Insights from the ALSPAC cohort. Child Psychiatry and Human Development. 10.1007/s10578-025-01855-x [DOI] [PubMed] [Google Scholar]
  50. Raffelt, D. A., Smith, R. E., Ridgway, G. R., Tournier, J. D., Vaughan, D. N., Rose, S., Henderson, R., & Connelly, A. (2015). Connectivity-based fixel enhancement: Whole-brain statistical analysis of diffusion MRI measures in the presence of crossing fibres. NeuroImage,117, 40–55. 10.1016/j.neuroimage.2015.05.039 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Raffelt, D. A., Tournier, J. D., Smith, R. E., Vaughan, D. N., Jackson, G., Ridgway, G. R., & Connelly, A. (2017). Investigating white matter fibre density and morphology using fixel-based analysis. NeuroImage,144(Pt A), 58–73. 10.1016/j.neuroimage.2016.09.029 [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Raffelt, D., Tournier, J. D., Fripp, J., Crozier, S., Connelly, A., & Salvado, O. (2011). Symmetric diffeomorphic registration of fibre orientation distributions. NeuroImage,56(3), 1171–1180. 10.1016/j.neuroimage.2011.02.014 [DOI] [PubMed] [Google Scholar]
  53. Ramnani, N. (2006). The primate cortico-cerebellar system: Anatomy and function. Nature Reviews Neuroscience, 7(7), 511–522. 10.1038/nrn1953 [DOI] [PubMed] [Google Scholar]
  54. Roberts, G., Howard, K., Spittle, A. J., Brown, N. C., Anderson, P. J., & Doyle, L. W. (2008). Rates of early intervention services in very preterm children with developmental disabilities at age 2 years. Journal of Paediatrics and Child Health, 44(5), 276–280. 10.1111/j.1440-1754.2007.01251.x [DOI] [PubMed] [Google Scholar]
  55. Salman, M. S., & Tsai, P. (2016). The role of the pediatric cerebellum in motor functions, cognition, and behavior: A clinical perspective. Neuroimaging Clinics of North America,26(3), 317–329. 10.1016/j.nic.2016.03.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Santhanam, P., Coles, C. D., Li, Z., Li, L., Lynch, M. E., & Hu, X. (2011). Default mode network dysfunction in adults with prenatal alcohol exposure. Psychiatry Research, 194(3), 354–362. 10.1016/j.pscychresns.2011.05.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Schilling, K., Gao, Y., Janve, V., Stepniewska, I., Landman, B. A., & Anderson, A. W. (2017). Can increased spatial resolution solve the crossing fiber problem for diffusion MRI? NMR in Biomedicine. 10.1002/nbm.3787 [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Smith, R., Dhollander, T., & Connelly, A. (2019). On the regression of intracranial volume in fixel-based analysis.
  59. Spadoni, A. D., McGee, C. L., Fryer, S. L., & Riley, E. P. (2007). Neuroimaging and fetal alcohol spectrum disorders. Neuroscience and Biobehavioral Reviews,31(2), 239–245. 10.1016/j.neubiorev.2006.09.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. StataCorp. (2019). Stata Statistical Software: Release 16. College Station. StataCorp LLC. [Google Scholar]
  61. Suades-Gonzalez, E., Forns, J., Garcia-Esteban, R., Lopez-Vicente, M., Esnaola, M., Alvarez-Pedrerol, M., Julvez, J., Caceres, A., Basagana, X., Lopez-Sala, A., & Sunyer, J. (2017). A longitudinal study on attention development in primary school children with and without teacher-reported symptoms of ADHD. Frontiers in Psychology,8, 655. 10.3389/fpsyg.2017.00655 [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Taylor, H. G., Albo, V. C., Phebus, C. K., Sachs, B. R., & Bierl, P. G. (1987). Postirradiation treatment outcomes for children with acute lymphocytic leukemia: Clarification of risks. Journal of Pediatric Psychology,12(3), 395–411. 10.1093/jpepsy/12.3.395 [DOI] [PubMed] [Google Scholar]
  63. Thompson, D. K., Kelly, C. E., Dhollander, T., Muggli, E., Hearps, S., Lewis, S., Nguyen, T. N., Spittle, A., Elliott, E. J., Penington, A., Halliday, J., & Anderson, P. J. (2024). Associations between low-moderate prenatal alcohol exposure and brain development in childhood. NeuroImage: Clinical,42, Article 103595. 10.1016/j.nicl.2024.103595 [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Tournier, J. D., Calamante, F., & Connelly, A. (2012). MRtrix: Diffusion tractography in crossing fiber regions. International Journal of Imaging Systems and Technology,22(1), 53–66. [Google Scholar]
  65. Treit, S., Jeffery, D., Beaulieu, C., & Emery, D. (2020). Radiological findings on structural magnetic resonance imaging in fetal alcohol spectrum disorders and healthy controls. Alcoholism, Clinical and Experimental Research, 44(2), 455–462. 10.1111/acer.14263 [DOI] [PubMed] [Google Scholar]
  66. Ursache, A., Noble, K. G., Pediatric Imaging, N., Genetics, S. (2016). Socioeconomic status, white matter, and executive function in children. Brain and Behavior,6(10), Article e00531. 10.1002/brb3.531 [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Wallace, E. J., Mathias, J. L., Ward, L., Fripp, J., Rose, S., & Pannek, K. (2020). A fixel-based analysis of micro- and macro-structural changes to white matter following adult traumatic brain injury. Human Brain Mapping,41(8), 2187–2197. 10.1002/hbm.24939 [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Ware, A. L., Long, X., & Lebel, C. (2021). Functional connectivity of the attention networks is altered and relates to neuropsychological outcomes in children with prenatal alcohol exposure. Developmental Cognitive Neuroscience,48, Article 100951. 10.1016/j.dcn.2021.100951 [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Wasserthal, J., Neher, P., & Maier-Hein, K. H. (2018). Tractseg - Fast and accurate white matter tract segmentation. NeuroImage,183, 239–253. 10.1016/j.neuroimage.2018.07.070 [DOI] [PubMed] [Google Scholar]
  70. Wozniak, J. R., Muetzel, R. L., Mueller, B. A., McGee, C. L., Freerks, M. A., Ward, E. E., Nelson, M. L., Chang, P. N., & Lim, K. O. (2009). Microstructural corpus callosum anomalies in children with prenatal alcohol exposure: An extension of previous diffusion tensor imaging findings. Alcoholism, Clinical and Experimental Research,33(10), 1825–1835. 10.1111/j.1530-0277.2009.01021.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Wozniak, J. R., Riley, E. P., & Charness, M. E. (2019). Clinical presentation, diagnosis, and management of fetal alcohol spectrum disorder. Lancet Neurology,18(8), 760–770. 10.1016/s1474-4422(19)30150-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Young, S. L., Steane, S. E., Kent, N. L., Reid, N., Gallo, L. A., & Moritz, K. M. (2022). Prevalence and patterns of prenatal alcohol exposure in Australian cohort and cross-sectional studies: A systematic review of data collection approaches. International Journal of Environmental Research and Public Health. 10.3390/ijerph192013144 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

ESM 1 (547KB, docx)

(DOCX 546 KB)

Data Availability Statement

As the study is ongoing, data will not be publicly available.

As the study is ongoing, code will not be publicly available.

Articles from Brain Imaging and Behavior are provided here courtesy of Springer

RESOURCES