Sensory Processing Dysfunction (SPD) refers to a clinical deficit in the ability to modulate, discriminate, or create an organized response to sensory information, affecting up to 16% of children (Ben-Sasson et al., 2009). Due to the disruptions in sensory processing, children with SPD may demonstrate atypical or delayed intellectual, language, or motor milestones (May-Benson et al., 2009).

Of children with SPD, approximately 40% will also meet research criteria for attention-deficit/hyperactivity disorder (ADHD) (Koziol and Budding, 2012). ADHD is characterized by pervasive and disabling symptoms of inattention, hyperactivity, and impulsivity, as defined by the Diagnostic and Statistical Manual of Mental Diseases (DSM-5), and affects 5–8% of school children (Faraone et al., 2003; American Psychiatric Association, 2013). However, the rate of ADHD in SPD is almost ten-fold higher. While there are overlapping features of ADHD and SPD, they may represent two distinct dimensions that can coexist with unique neurobiological properties (Miller et al., 2012). SPD in the context of autism and ADHD has been associated with mental health and behavioral challenges including anxiety, depression, academic difficulties, and disruptive behaviors (Mangeot et al., 2001; Sanz-Cervera et al., 2017; MacLennan et al., 2021; Rossow et al., 2021, 2022). Since SPD is a global cluster of sensory discrimination, modulation and sensorimotor challenges that often co-occurs with attention challenges. It is likely that these information processing functions have both shared and unique aspects of their underlying neural mechanisms. Teasing apart these neural underpinnings will shed light on how these two dimensions thought to be distinct might interrelate (Miller et al., 2012).

Quantitative neuroimaging studies found no differences in gray or white matter (WM) volumes between children with SPD and the typically developing childhood cohort, indicating that the pathology should be sought in microstructural changes (Owen et al., 2013; Brandes-Aitken et al., 2018b; Narayan et al., 2021). Diffusion magnetic resonance imaging (dMRI) research has shown that diffusion tensor imaging (DTI) -derived fractional anisotropy (FA) in the superior corona radiata uniquely correlates with cognitive control performance in subjects with SPD (Brandes-Aitken et al., 2018a). However, no prior work has examined the WM microstructure in SPD with ADHD (SPD+ADHD) compared to SPD without ADHD (SPD-ADHD). SPD+ADHD children have been shown to have reduced midline frontal theta activity on electroencephalography (EEG), a marker of attention abilities captured in real-time (Anguera et al., 2017). The midline frontal theta difference is thought to emanate from the dorsal anterior cingulate and adjacent medial prefrontal cortex, a region that would be expected to be implicated a priori due to its known importance for impulse control (Ishii et al., 2014).

Although DTI is a useful tool for studying brain development, it represents only a basic statistical description of water diffusion within a voxel. DTI is typically acquired at a single relatively low diffusion-weighting factor (b value) representing only a single spherical shell in q-space. The assumption of Gaussian diffusion that underpins the DTI model breaks down at b values in excess of 1,000 s/mm², whereas the investigation of restricted and strongly hindered diffusion, such as within the intracellular space, requires higher diffusion-weighting factors. Therefore, common DTI measures, such as fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) are not specific to the underlying microstructural features of axons and dendrites (collectively called neurites) and often lack the specificity to differentiate between intracellular and extracellular disruption (Pierpaoli and Basser, 1996; Wheeler-Kingshott and Cercignani, 2009; De Santis et al., 2014). In contrast to DTI, Neurite Orientation and Dispersion Density Imaging (NODDI) is a multi-compartment biophysical model of brain microstructure that computes the non-collinear properties of neurite Orientation Dispersion Index (ODI) and Neurite Density Index (NDI), corresponding to the degree of incoherence in fiber orientations and to the intracellular volume fraction within each imaging voxel, respectively. NODDI employs a tissue model that distinguishes three types of microstructural environment: restricted intracellular compartment modeled with orientation dispersion using a Watson distribution; (May-Benson et al., 2009) extracellular compartment with Gaussian anisotropically hindered diffusion and (Koziol and Budding, 2012) cerebrospinal fluid (CSF) compartment with freely isotropic diffusion. NODDI allows in vivo estimation of NDI, ODI, and free water fraction (FISO) (Fukutomi et al., 2019). NDI is typically considered to be the most sensitive NODDI metric for white matter microstructure (Mah et al., 2017).

Our study is the first to examine the neural architecture of SPD+ADHD versus SPD-ADHD using either DTI or NODDI. Prior studies have shown that FA and NDI are the most sensitive DTI and NODDI metrics in brain development and WM diseases, with rising FA and NDI values during childhood WM maturation and decreased FA and NDI in most forms of WM pathology (Mukherjee et al., 2001, 2002; Mukherjee and McKinstry, 2006; Chang et al., 2015a,b). Previous work in SPD patients has shown reduced FA in posterior projection and commissural tracts of the bilateral posterior thalamic radiation and splenium of the corpus callosum, as well as the posterior limb of internal capsule (Chang et al., 2015a,b). White matter abnormalities in ADHD have been studied using DTI, although showing heterogeneous results with a lack of consensus in the literature. A meta-analysis of tract-based spatial statistics (TBSS) hypothesizes that the fronto-striatal-cerebellar circuit plays a crucial role in the pathophysiology of ADHD (Chen et al., 2016). We hypothesize that the SPD+ADHD cohort will have globally lower FA and NDI than the SPD-ADHD group, that this difference may be more robust for neural tracts previously implicated in attention, and that the intracellular component as measured by NDI may show a stronger effect than the basic tract anisotropy as measured by FA.

Our study is the first to evaluate the neuroarchitecture of the attentional dimension of SPD, and as such should shed light on the neurobiological mechanism of ADHD in children with SPD. We found delayed/disrupted white matter microstructure in SPD+ADHD compared to SPD-ADHD, especially in males, based on lower FA and especially lower NDI values. This is important because SPD is a common yet understudied disorder that can affect up to one in six children. In our cohort, 43% of individuals with SPD also met research criteria for ADHD, which replicates what has been shown in a previous and unrelated cohort (Anguera et al., 2017), and is up to ten-fold higher than the rate of ADHD in the general population of children (Ben-Sasson et al., 2009; Koziol and Budding, 2012).

SPD refers to a clinical deficit in the ability to modulate, discriminate, or create an organized response to sensory information. As a result, such children may suffer from emotional, social, and educational problems, including anxiety, aggression, inattention, poor self-concept, and academic failure (Miller et al., 2009). The most effective treatment for SPD is sensory integration. A recent randomized clinical trial has proven the effectiveness of occupational therapy with a sensory integration approach compared to placebo in improving the social and cognitive deficits of children with SPD (Miller et al., 2007). Additionally, digital cognitive training of SPD+ADHD subjects has shown sustained benefits for attention at three-year follow up (Jurigova et al., 2021). However, a timely diagnosis and proper therapy are keys to successful treatment, necessitating the development of new objective biomarkers for diagnosis of SPD with deeper understanding of the co-occurring challenges including attention, fine motor control, and emotional regulation. Furthermore, there is a concern that SPD is not a recognized or singular disorder and thus may not warrant study. While we agree that SPD is an umbrella term used to include multiple sensory domains and dimensions, as sensory input and processing with the additional component cognitive control being the basis for thought and action, the dedicated understanding of all aspects of the neural architecture underlying typical and divergent performance is of critical importance. Children with SPD do not usually exhibit morphological abnormalities on structural MRI; however, prior work has demonstrated they have strikingly decreased white matter microstructural integrity in the form of lower FA values on DTI compared to typically developing children, particularly in the posterior cerebral regions that subserve sensory processing and integration (Owen et al., 2013). With DTI findings codifying differences in children with SPD and emerging reports of genetic etiologies (Chang et al., 2015a,b; Mulligan et al., 2019), it is clear that whatever term we use to describe it, be it dysfunction, disorder, or condition, sensory processing needs further study. By pairing phenotyping with advanced neuroimaging, we can further our understanding of SPD as a “brain-based” condition that can be understood and improved with treatment.

Volumetric analyses of children with ADHD, but not screened for SPD, have shown decreased volume in the prefrontal cortex, right basal ganglia (globus pallidus, putamen, caudate), cerebellum, and corpus callosum (Ellison-Wright et al., 2008; Frodl and Skokauskas, 2012; Dougherty et al., 2016). White matter abnormalities in ADHD have been studied using DTI, although showing heterogeneous results with a lack of consensus among studies. A recent investigation found that FA values in the inferior frontal-striatal tract, inferior frontal-occipital fasciculus, SLF, and corpus callosum are negatively correlated with the ADHD polygenic risk score and longer screen time utilization (Yang et al., 2022). A meta-analysis of DTI TBSS studies suggests that the fronto-striatal-cerebellar circuit plays a crucial role in the pathophysiology of ADHD (Chen et al., 2016).

The benefits of NODDI over DTI were robust in our study, where NODDI uncovered microstructural differences in more white matter tracts compared to DTI in SPD+ADHD relative to SPD-ADHD. Non-gaussian white matter microstructure imaging in ADHD is sparse. One study has used Diffusion Kurtosis Imaging (DKI) and found that patients with isolated ADHD had significantly greater WM microstructural complexity than typically developing controls in the bilateral frontal and parietal lobes, insula, corpus callosum, and right external and internal capsules (Helpern et al., 2011). However, as a statistical model of diffusion akin to DTI, DKI is non-specific relative to NODDI and its three-compartment biophysical tissue model (Kamagata et al., 2016). There has been one prior paper using multi-shell diffusion MRI with biophysical modeling to study ADHD that demonstrated decreased dendrite density and volume in certain frontal lobe white matter fiber tracts that subserve the attention function and executive control system (Wu et al., 2020).

Our voxelwise and tract-level statistical analyses with multiple comparisons correction found that boys with SPD+ADHD have decreased NDI in many projection tracts, including the posterior corona radiata, which subserves higher-order sensory functions including multisensory integration, the retrolenticular limb of the internal capsule, which contains the somatosensory and auditory radiations, as well as the posterior limb of the internal capsule and the cerebral peduncles, which contains corticobulbar and corticospinal projection fibers. In addition, commissural fibers of the splenium of the corpus callosum also exhibit low NDI in SPD+ADHD in boys, therefore involving interhemispheric sensory connectivity. Given that WM NDI increases with age during childhood (Chang et al., 2015a,b), this posterior WM tract maturational delay/disruption suggests an association between the degree of sensory tract pathology and the emergence of comorbid ADHD, rather than ADHD being an entirely separate dimension of SPD. These NDI differences between groups cannot be explained by normal brain development, since the male SPD+ADHD and SPD-ADHD cohorts have almost identical mean ages and standard deviations (Table 2).

Furthermore, the bilateral anterior limbs of the internal capsule also show reduced NDI in SPD+ADHD males, implicating prefrontal projection fibers including fronto-striatal connectivity that has been previously implicated in ADHD by a meta-analysis of DTI studies using TBSS (Chen et al., 2016). The maturation of frontal attentional feedback to posterior sensory systems requires normal sensory development. Frontal pathways governing executive function and impulse control lag behind sensory pathway development and their maturation extends well into adolescence. Hence, the hypothesis of abnormal sensory pathway development causing attentional and executive control dysfunction is compelling and merits further investigation.

In addition to these white matter microstructural differences between SPD+ADHD and SPD-ADHD, we found a sexual dimorphism that suggests at least partially different underpinnings in males versus females. There were much stronger NODDI group differences in boys than in girls. Hypotheses about the mechanism of ADHD in girls with SPD require further study in a larger cohort, given the relatively low rates of both ADHD and SPD in girls compared to boys.

Lower FA and especially lower NDI in males with SPD+ADHD compared to SPD-ADHD suggest more delayed and/or disrupted white matter microstructure of the former group. Multi-shell diffusion imaging with NODDI was more sensitive than traditional DTI for detecting these group differences. The posterior distribution of most of the observed white matter differences suggests that a greater degree of sensory tract microstructural disruption is associated with co-morbid ADHD.

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: NIH Data Archive (https://nda.nih.gov) Accession Number:4095004.

The studies involving human participants were reviewed and approved by Committe on Human Research, University of California, San Francisco. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.

EM and PM: conception and design of the study. IM, ML, MR, JW-J, JX, LC, IB, and SP: acquisition and analysis of the data. IM, JW-J, JX, LC, ML, SP, EM, and PM: drafting significant portion of the manuscript and figures. All authors contributed to the article and approved the submitted version.

This work was funded by an American Society of Neuroradiology (ASNR) Scholar Award to IM and a National Institutes of Health (NIH) R01 MH116950 grant to PM and EM.

ML, MR, and EM were employed by Cortica Healthcare during the conduct of this work.

The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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