Increased resting-state activity in the cerebellum with mothers having less adaptive sensory processing and trait anxiety.

Child-rearing mothers with high levels of trait anxiety have a tendency for less adaptive sensory processing, which causes parenting stress. However, the neural mechanisms underlying this sensory processing and trait anxiety remain unclear. We aimed to determine the whole-brain spontaneous neural activity and sensory processing characteristics in mothers with varying parenting stress levels. Using resting-state functional magnetic resonance imaging, we assessed mothers caring for more than one preschool aged (2-5 years) child and presenting with varying levels of sensory processing, trait anxiety, and parenting stress. Spontaneous neural activities in select brain regions were evaluated by whole-brain correlation analyses based on the fractional amplitude of low-frequency fluctuations (fALFF). We found significant positive correlations between levels of sensory processing with trait anxiety and parenting stress. Mothers having less adaptive sensory processing had significantly increased resting-state network activities in the left lobule VI of the cerebellum. Increased fALFF values in the left lobule VI confirmed the mediation effect on the relationship between trait anxiety and sensory processing. A tendency for less adaptive sensory processing involving increased brain activity in lobule VI could be an indicator of maternal trait anxiety and the risk of parenting stress.

INTRODUCTION

Everyday life is full of various sensory stimuli. Sensory processing refers to the ability to regulate and organize reactions to sensory stimuli in a graded and adaptive manner (Ben‐Sasson, Gal, Fluss, Katz‐Zetler, & Cermak, 2019; Dunn, 1997; Miller, Anzalone, Lane, Cermak, & Osten, 2007). In other words, sensory processing refers to the ability of the brain to correctly respond to the surrounding environmental stimuli and remain at the correct responsiveness level. Dunn (1997) explains sensory processing as a neurological threshold and behavioral response; the neurological thresholds refer to the intensity of stimuli needed for the central nervous system (CNS) to notice or react to stimuli, while the behavioral responses refer to the manner of response in relation to the thresholds.

Although most people present with balanced sensory processing abilities, approximately 15% of the population present with a tendency for less adaptive sensory processing patterns (Brown, Tollefson, Dunn, Cromwell, & Filion, 2001). The brains of individuals with a tendency for less adaptive sensory processing, who present hyper‐responsive or hypo‐responsive behaviors, are thought to be unable to receive stimuli or filter out irrelevant stimuli (Dunn & Brown, 1997; Lane et al., 2019); e.g., “they startle easily from unexpected or loud noises,” “they don't notice when other people come in the room,” “they don't seem to notice when their hands or faces are dirty,” “they are unaware of odors that others notice,” “ they keep the shades down,” and “I touch others when I'm talking” (Brown et al., 2001; Dunn, 1997). The response process is not as automatic as that in most individuals and requires more effort for those with less adaptive tendency for sensory processing. This may interfere with engagement in daily activities such as eating, grooming, and socializing (Lane et al., 2019).

Healthy individuals with a tendency for less adaptive sensory processing, such as those with low sensory input registration or sensory hypersensitivity, have been shown to have high trait anxiety (Engel‐Yeger & Dunn, 2011; Horder, Wilson, Mendez, & Murphy, 2014). Trait anxiety predisposes individuals to daily evasive behavior as well as excessive and volatile emotions (Endler & Kocovski, 2001; Spielberger, Gorsuch, Lushene, Vagg, & Jacobs, 1983). In adults with autistic traits, abnormal sensory processing is positively associated with trait anxiety (Horder et al., 2014). Sensory processing ability has been studied in adults with mental health issues (Dunn, 2001; Kinnealey, Koenig, & Smith, 2011), including anxiety and social–emotional issues, and can predict psychological distress (Bar‐Shalita & Cermak, 2016). Particularly, there is a strong association between trait anxiety and sensory processing difficulties, which can cause stress in routine situations.

Significantly, anxiety in child‐rearing mothers is associated with depressive symptoms and care stress (Correia & Linhares, 2007; Vismara et al., 2016). Increased trait anxiety in mothers has been shown to induces parenting stress (Austin, Hadzi‐Pavlovic, Leader, Saint, & Parker, 2005). Moreover, a high level of trait anxiety in mothers is a risk factor for child maltreatment (Douki et al., 2013). A study of mother–child mutual play reported that mothers with increased trait anxiety were less sensitive to their child's behaviors (Nicol‐Harper, Harvey, & Stein, 2007). In addition, maternal anxiety is associated with less adaptive sensory processing even in healthy adults (Uljarevic, Prior, & Leekam, 2014). Mothers with a tendency for less adaptive sensory processing were reluctant to respond promptly to their children's signs, including crying (Turner, Cohn, & Koomar, 2012). Low threshold prenatal sensory patterns correlated with maternal–infant postnatal attachment (Branjerdporn, Meredith, Wilson, & Strong, 2020).

In a study of the rearing brain, a mother's brain becomes sensitive to baby stimuli during the first months of life (Swain et al., 2008). In other words, child‐rearing mothers are constantly exposed to the stimulus of their baby, in addition to other daily sensory stimuli. Mothers have a response bias to infant facial stimuli, which is generally perceived as adaptive (Lucion et al., 2017). As environment stimuli are also typically present, a process is envisioned in which unrelated stimuli are suppressed, and the target infant facial stimulus unconsciously and consciously pops up. If there is a tendency for less adaptive sensory processing, such processing cannot be performed. In this case, the child's sensory input may become difficult and lead to child‐rearing stress. Taken together, these previous findings suggest that trait anxiety in mothers can influence a tendency for less adaptive sensory processing, which can lead to difficulties in parenting.

“Trait anxiety” is defined more as a personality feature, whereas “state anxiety” is a temporary reaction to adverse events (Spielberger et al., 1983). A recent fMRI study has shown differences for resting‐state functional connectivity (rs‐FC) for healthy human trait anxiety and state anxiety. Furthermore, with regard to structural gray matter (GM), trait anxiety was related to volume alterations, anterior cingulate, limbic regions such as amygdala with and cingulate gyrus, precuneus, cuneus, and inferior frontal gyrus, and cerebellar involvement; the cerebellum was particularly strongly related (Saviola et al., 2020).

Further, neurobiological studies have reported an association of task‐induced neural activity with the fractional amplitude of low‐frequency fluctuations (fALFF) in the resting‐state brain activity (Mennes et al., 2011). fALFF can reflect individual characteristics in healthy adults, including the Big Five personality traits (Kunisato et al., 2011), trait extroversion (Wei et al., 2014), trait empathy (Cox et al., 2012), trait grit (Wang et al., 2017), subjective well‐being (Kong, Hu, Wang, Song, & Liu, 2015), trait hopefulness (Wang et al., 2017), and perceived stress (Wang et al., 2019). However, there are no studies on the characteristics of spontaneous neural activity in child‐rearing mothers with a tendency for less adaptive sensory processing and trait anxiety using measurements of fALFF by resting‐state functional MRI (rs‐fMRI).

It is unclear whether a tendency for less adaptive sensory processing induced by trait anxiety is a stressor and mediates the effects of trait anxiety observable in maternal parenting in daily parenting scenarios. Although neurobiology can elucidate the role of sensory processing in trait anxiety, relevant studies on the neural mechanism have been limited by their reliance on clinical samples with specific forms of psychopathology such as general anxiety disorder (Peterson, Thome, Frewen, & Lanius, 2014) and post‐traumatic stress disorder (Moreno‐Rius, 2018; Wang et al., 2016).

Regarding the neural basis of sensory processing characteristics in healthy adults, studies have reported positive correlations of modality‐specific (e.g., visual, auditory, or tactile) sensory scores with the GM volume in the related primary sensory areas (Yoshimura et al., 2017). Moreover, the neural basis of sensory processing has been suggested to involve the neocortex, basal ganglia, and cerebellar activities (Koziol, Budding, & Chidekel, 2011). The neocortex is a sensory processor and elegant motor programmer. The basal ganglia and the cerebellum interact with the neocortex and have been involved in the adaptation and behavior of sensory information. In a recent study, connectome‐based predictive modeling (CPM) suggested predicting maternal anxiety toward their infant between cerebellum and motor‐sensory‐auditory network and between frontoparietal and motor–sensory–auditory networks (Rutherford, Potenza, Mayes, & Scheinost, 2020). Finally, the cerebellum has been suggested to be involved in emotion (e.g., anxiety) and motor control (Moreno‐Rius, 2018; Stoodley & Schmahmann, 2009). Accordingly, we hypothesized that the cerebellum is involved in trait anxiety, which involves less adaptive processing of sensory input in mothers.

We here aimed to identify the neural correlates of sensory processing and trait anxiety using rs‐fMRI fALFF. We also aimed to enroll child‐rearing mothers for testing our hypothesis that subclinical anxiety reflects the atypical neural activity of brain regions involved in regulating sensory perception, sensory processing, and emotional behavior. Furthermore, we determined whether there was a correlation of alterations in regional brain activities with parenting stress.

METHODS

Participants

Between 2015 and 2016, we enrolled 33 mothers (age range = 27–46 years, mean age = 35.9 years, standard deviation [SD] = 4.5 years) through advertisements targeted to female caregivers caring for more than one preschool, typically developing child, as previously described (Shimada et al., 2018). The ethnicity of all participants was Japanese.

The study protocol was approved by the Ethics Committee of the University of Fukui, Japan (Approval # FU‐20150109), and all procedures were conducted in accordance with the Declaration of Helsinki and the Ethical Guidelines for Clinical Studies of the Ministry of Health, Labor, and Welfare of Japan. The participants received explanations regarding the purpose and meaning of the study, and written informed consent was obtained from all subjects.

All participants had completed ≥12 years of education and were living above the relative poverty line, which is set at 50% of the median household income in Japan (Organization for Economic Cooperation and Development, 2016). Based on self‐report questionnaires, none of the participants had a history of brain injury, neurological or major psychiatric illness, current medication use, excessive alcohol intake, or cigarette smoking. Moreover, none of the participants were pregnant or had been diagnosed with or treated for depression or anxiety disorder. All the participants were either right or left‐handed according to the Flinders Handedness survey (FLANDERS; Nicholls, Thomas, Loetscher, & Grimshaw, 2013).

All the participants met the safety requirements for undergoing rs‐fMRI (exclusion of ferromagnetic implants, claustrophobia, pregnancy, and other factors). The standardized questionnaire was collected by mail after the brain imaging.

Psychological questionnaires

Anxiety

We used the trait subscale of the State‐Trait Anxiety Inventory (STAI), a 20‐item self‐reported questionnaire (Spielberger et al., 1983), to measure the participants' current anxiety mood. The STAI‐Trait assesses how respondents “generally feel” (e.g., “I am a steady person” or “I lack self‐confidence”). Each STAI‐Trait item has a weighted score of 1–4. A rating of 4 indicates the presence of a high trait anxiety level.

Depression

The Beck Depression Inventory‐II (BDI‐II; Beck, Steer, & Brown, 1996) was used to measure the participants' current depressed mood. The BDI‐II scores range from 0 to 63 with the cut‐off points 14, 20, and 29 indicating mild, moderate, and severe depression levels, respectively.

Sensory processing

The Adult/Adolescent Sensory Profile (AASP; Brown, Cromwell, Filion, Dunn, & Tollefson, 2002) was used to measure the participants' sensory processing degree. The AASP is a 60‐item questionnaire designed as a trait measure of six sensory modalities involved in everyday sensory stimuli: visual (e.g., prefers darkness), auditory (e.g., holds hands over ears to protect them from sound), touch, taste/smell, movement (vestibular/proprioceptive), and activity level. It assesses how often the respondent performs a particular behavior using a 5‐point scale (1, almost never; 2, seldom; 3, occasionally; 4, frequently; and 5, almost always; range of possible scores, 60–300). In contrast, the 60‐item questionnaire is classified into four quadrants based on the Dunn's model (Dunn & Brown, 1997). The four quadrants are defined by a “neurological threshold continuum axis” (i.e., behaviors hyper‐responsive versus hypo‐responsive to sensory stimuli) and a “passive‐active behavior axis” (i.e., the person does/does not try to compensate behaviorally for an abnormal threshold). The AASP is the most widely used sensory processing scale in the world (DuBois, Lymer, Gibson, Desarkar, & Nalder, 2017).

In a recent study, sensory processing problems were suggested to include sensory over‐responsivity (SOR), under‐responsivity (SUR), and seeking symptoms (Ben‐Sasson et al., 2019; Miller et al., 2007). The SOR score used the sum of the avoidance quadrant and the sensitivity quadrant of the sensory profile score (Ben‐Sasson et al., 2019). Similarly, some or all four‐quadrant scores are sometimes summed up (Daluwatte, Miles, Sun, & Yao, 2015; Horder et al., 2014; Khodabakhsh & Rosli, 2020; Mayer, 2017; Metz et al., 2019). The short sensory profile (SSP) version for children initially has a total score, and the higher the total score, the more atypical sensory processing (Daluwatte et al., 2015; Tomchek & Dunn, 2007). However, in previous studies, the four‐quadrant scores were often analyzed individually (Engel‐Yeger & Dunn, 2011; Meredith, Bailey, Strong, & Rappel, 2016).

Thus, the four quadrants of Dunn's model may overlap within an individual, as described in “At least one sensory quadrant of four quadrants” (Crane, Goddard, & Pring, 2009; Wickremasinghe et al., 2013). Initially, the four‐quadrant scores of Dunn's model are closely related theoretically and statistically (Engel‐Yeger & Dunn, 2011; Meredith et al., 2016). In particular, the “neurological threshold axis,” which constitutes the four quadrants, has been confirmed to be continuous by skin conductance measurements and Electroencephalography (EEG), but the other “passive‐active axis” has not been confirmed (Brown et al., 2001; Metz et al., 2019). Therefore, we adopted the AASP total scores to confirm the neurological characteristics underlying individual differences in sensory processing (van den Boogert et al., 2021).

Parenting stress

We used the Japanese version of the Parental Stress Index (PSI‐J) (Narama et al., 1999) adapting the PSI (Abidin, 1995) for measuring maternal parenting stress. The PSI‐J is a 78‐item self‐report questionnaire, which is divided into child and parent rating items on a five‐point scale that ranges from 1 (completely disagree) to 5 (completely agree). The child domain of stressors includes the child's adaptability and behavioral characteristics (e.g., degree to please parents, child's mood, degree to annoy parents, distractibility, and hyperactivity). The parent domain of stressors includes parental characteristics and feelings of social childcare support in the family (e.g., parental role restriction, social isolation, relationship with spouse, parental competence, depression/guilt, attachment, health). Higher scores indicate higher levels of parenting stress.

fMRI data acquisition

Scanning took place on the GE Discovery MR 750 3.0 Tesla scanner (General Electric, Milwaukee, WI) using a 32‐channel head coil. Functional images were acquired using a T2*‐weighted gradient‐echo echo‐planar imaging sequence to produce 40 continuous transaxial slices with a thickness of 3.5 and 0.5 mm gap, respectively, covering the entire cerebrum and cerebellum (repetition time [TR] = 2,300 ms; echo time [TE] = 30 ms; flip angle [FA] = 81°; field of view [FOV] = 192 mm; 64 × 64 matrix; voxel dimension = 3.0 × 3.0 mm; 201 acquisitions). During the scan, the participants were instructed to close their eyes, remain awake, and think of nothing in particular.

We acquired high‐resolution structural whole‐brain images using a 3D T1‐weighted fast spoiled‐gradient recalled imaging sequence (TR = 6.38 ms; TE = 1.99 ms; FA = 11°; FOV = 256 mm; 256 × 256 matrix; 172 slices; voxel dimension = 1.0 × 1.0 × 1.0 mm).

fMRI data analysis

Preprocessing

Imaging data were preprocessed and analyzed using the Statistical Parametric Mapping software (SPM12; Wellcome Trust Centre for Neuroimaging, London, UK) and the Data Processing Assistant for rs‐fMRI (DPARSF; Chao‐Gan & Yu‐Feng, 2010) implemented in MATLAB R2016 (MathWorks, Natick, MA). To account for the time required for MRI signal equilibration and subject adaptation to the scanning environment, the first 10 volumes were discarded. The remaining 191 images were corrected for slice timing, followed by spatial realignment to correct for head motion.

We adjusted for head motion effects by computing the mean frame‐wise displacement (FD; Power, Barnes, Snyder, Schlaggar, & Petersen, 2012). All participants' data were within the motion thresholds for inclusion in the analysis, defined as translational parameters <3 mm, rotational parameters <3°, and FD <0.5. Subsequently, high‐resolution T1 images were co‐registered with the functional images using a nonlinear image registration approach. Next, images were segmented using a recently published diffeomorphic anatomical registration algorithm that employs an exponentiated Lie algebra technique (Ashburner, 2007). Subsequently, functional images were spatially normalized to the Montreal Neurological Institute template, resampled to a spatial resolution of 3 × 3 × 3 mm3, and spatially smoothed with a 6‐mm full width at half‐maximum Gaussian kernel. Next, nuisance signals in 24 head‐motion parameters (Friston, Williams, Howard, Frackowiak, & Turner, 1996), the global signal, the time series of the cerebrospinal fluid and white matter, and any linear trends were regressed out of each voxel's time course. Finally, we performed temporal band‐pass filtering (0.01–0.8 Hz) of the residual time series to reduce the effect of low‐ and high‐frequency drifts and noise, respectively (Lowe, Mock, & Sorenson, 1998).

Fractional amplitude of low‐frequency fluctuations analysis

To investigate the spontaneous neural activity, we calculated the fALFF rather than the original ALFF because the former is considered less sensitive to physiological noise and artifacts that could weaken low‐frequency oscillation approaches (Power et al., 2012). To perform the fALFF calculation, the time course of each voxel signal was transformed into the corresponding power spectrum by fast Fourier transform (FFT). Subsequently, the power spectrum obtained by FFT was square‐root‐transformed and averaged across 0.01–0.08 Hz at each voxel. The obtained averaged square root was divided by the global mean value, providing fALFF maps (Zou et al., 2013). Finally, for standardization, individual fALFF maps were divided by the grand average of the fALFF value.

Statistical analysis

Statistical analyses were performed using SPSS Version 24 (IBM Corp., Armonk, NY). Data were expressed as mean ± SD. Using the datasets mentioned above, we performed a correlation analysis to investigate the relationships among trait anxiety, sensory processing characteristics, and parenting stress. Next, we performed a whole‐brain correlation analysis of STAI and AASP total scores with fALFF values to determine the relationship between the degree of sensory processing and resting‐state brain activities. The model included age, BDI‐II scores, and mean FD as nuisance covariates. In addition, the mean FD, which was derived from individual analysis, was included to further exclude residual head‐motion effects. The statistical threshold was set at p <.005 uncorrected at the peak level and p <.05 at the cluster level, with family‐wise error (FWE) corrected over the whole brain. Further, we analyzed the correlation of the fALFF values with the STAI trait scores and the PSI total scores.

A path analysis mediated using the bootstrapping technique to obtain a 95% bias‐corrected confidence interval (CI) of indirect effect was utilized to determine whether the fALFF value significantly mediated the association between trait anxiety and the degree of sensory processing. The bootstrap test was conducted using the R 3.1.2 Test package (http://www.R-project.org/).

RESULTS

Descriptive statistics

Among the 33 participants, six were excluded (three did not fill out the questionnaire and three had a history of depression). Among the six excluded participants, one was not living above the relative poverty line and another was not married.

Artifact‐free images suitable for rs‐fMRI analyses were obtained from 27 female caregivers (age = 35.6 ± 4.3 years; AASP total scores = 141 ± 23.8; STAI trait scores = 42.6 ± 9.5; BDI‐II scores = 11.3 ± 6.1; PSI total scores = 193.5 ± 40.7) who were caring for more than one preschool aged (2–5 years) child, including seven first‐time mothers (Table 1). The participants included four mothers with AASP total scores >1 SD (>164.8) from the mean.

TABLE 1

Participants' demographic characteristics and psychological questionnaires score (n = 27)

	Mean	SD	Range	%
Age (years)	35.6	4.3	27–43
Right‐handed				84.8
Completed at least 12 years of education				100
Married (nondivorced, nonwidowed)				100
Number of family members	4.6	1.1	3–7
Number of children	2	0.8	1–4
Months since last childbirth	31	1.7	1–69
Living above the relative poverty line				100
State–trait anxiety inventory: Trait score	42.6	9.5	25–63
Beck depression inventory‐II score	11.3	6.1	2–23
Adult/adolescent sensory profile score (total)	141	23.8	95–214
Quadrant scores low registration	31.4	6.8	22–55
Sensation seeking	40.2	5.7	32–55
Sensory sensitivity	36.6	9.1	18–61
Sensation avoiding	32.9	8.8	20–53
Modality‐specific subscales visual	24.6	4.8	17–33
Auditory	24.6	6.3	15–43
Touch	31.2	7.2	20–55
Taste/smell	17.5	3.9	10–24
Movement (vestibular/proprioceptive)	17.4	3.4	11–27
Activity level	25.8	4.6	17–37
Parenting stress index score (total)	193.5	40.7	118–302
Child domain score	86.3	18.7	51–122
Parent domain score	107.3	25.6	64–180

There were significant positive correlations of AASP total scores with the trait anxiety and with the PSI total (STAI, r = .537, p = .004; PSI, r = .434, p = .024, respectively) in mothers with various levels of sensory processing and parenting stress (Figure 1a,b). There was no significant association between the AASP total scores and the BDI‐II scores (r = .176, p = .381).

FIGURE 1

(a) Scatterplot showing the relation between trait scores from the STAI and AASP total scores. (b) Scatterplots showing the relation between PSI scores and AASP total scores. STAI, State‐Trait Anxiety Inventory; AASP, Adult/Adolescent Sensory Profile; PSI, Parenting Stress Index

Questionnaire data are summarized in Table 2.

TABLE 2

The correlations between psychological questionnaires score

					Correlation
	Psychological questionnaires	1	2	3	4	5	6	7	8	9	10
1	State–trait anxiety inventory: Trait score
2	Beck depression inventory‐II score	.608**
3	Adult/adolescent sensory profile score(total)	.537**	.176
4	Low registration	.478*	.152	.760**
5	Sensation seeking	−.016	−.105	.512**	.394*
6	Sensory sensitivity	.563**	.223	.902**	.597**	.213
7	Sensory avoiding	.507**	.194	.844**	.406*	.207	.799**
8	Parenting stress index score (total)	.681**	.748**	.434*	.514**	.155	.316	.345
9	Child domain score	.484*	.674**	.375	.351	.153	.257	.373	.888**
10	Parent domain score	.729**	.698**	.416*	.560**	.135	.314	.276	.942**	.681**

p <.01.

p <.05.

Imaging results

We observed that individuals with higher AASP total scores had increased resting‐state network activities in the left cerebellum, lobule VI (Talairach's coordinates x = −30, y = −60, z = −24; cluster size = 80 voxels; p = .008, FWE‐corrected cluster level), as shown in Figure 2.

FIGURE 2

Brain regions with significantly increased resting‐state network activities, measured as fractional amplitude of low‐frequency fluctuations (fALFF) using a fast Fourier transform. The main cluster is in the left cerebellum, lobule VI; Talairach's coordinates x = −30, y = −60, z = −24; cluster size = 80 voxels; Z = 4.06, family‐wise error‐corrected cluster level. Color scale represents t‐values in the range 0–5

None of the other values showed a corrected cluster probability approaching significance. Examination of voxels with decreased fALFF revealed no clusters anywhere in the brain. In lobule VI, fALFF values were significantly associated with the STAI scores (r = .466, p = .014). However, we observed no significant associations between the lobule‐VI fALFF values and the PSI total scores (r = .334, p = .089).

We conducted a mediation analysis to assess the mediation effect of fALFF values in the left lobule VI. Figure 3 shows the mediation model used for predicting AASP total scores. In this model, trait anxiety levels, left lobule VI fALFF, and AASP total scores were included as the independent variable, mediator, and dependent variable, respectively. Trait anxiety levels significantly predicted AASP total scores as indicated by previous multilevel regression analyses (β = .537, p <.01). Further, trait anxiety levels predicted fALFF values in the left lobule VI (β = .466, p <.05). When trait anxiety levels and fALFF values in the left lobule VI were entered into the prediction model of the AASP total scores, there was a reduced effect of trait anxiety levels (β = .232, p = .114) while fALFF values in the left lobule VI remained significant (β = .655, p <.01). A bootstrapping procedure tested the mediating effect of fALFF values in the left lobule VI using 5,000 resamples. This technique yielded a 95% bootstrap CI without zero (0.010 to 1.883), which suggested that fALFF values in the left lobule VI significantly mediated the effect of trait anxiety on AASP total scores. We also developed a reverse causality model in which AASP predicts trait anxiety via the left lobule VI and examined its mediating effects. The results showed no significant indirect effect of AASP on STAI via left lobule VI(95% bootstrap CI [−0.15 to 0.22]).

FIGURE 3

Path model of the mediation effect of resting‐state activity (fALFF values) in the left cerebellum, lobule VI, on the relationship between degree of trait anxiety, measured as the trait scores of the State‐Trait Anxiety Inventory (STAI), and sensory modulation (AASP total scores). fALFF, fractional amplitude of low‐frequency fluctuations; AASP, Adult/Adolescent Sensory Profile, SE, standard error; β, Standardized partial regression coefficient; *, p <.05; **, p <.01; n.s., not significant

DISCUSSION

Our findings revealed an association between the degree of sensory processing evaluated using the AASP total scores and the resting‐state brain activity in the left lobule VI (Figure 2). Individuals with higher AASP total scores had higher levels of both trait anxiety and parenting stress, as assessed by STAI and PSI scores, respectively (Figure 1). Additionally, path analysis showed that fALFF values in the left cerebellar lobule VI mediated the effect of trait anxiety levels on AASP total scores (Figure 3). This study elucidates the neural mechanism of the involvement of this region in sensory processing in mothers.

Notably, we observed a strong association between fALFF values in the left lobule VI of the cerebellum and the degree of sensory processing as measured by the AASP total scores. The reason for the association of functional brain activity alterations in left lobule VI with a less adaptive sensory processing phenotype remains unclear. Nonetheless, our findings are consistent with previous rs‐fMRI studies using independent component analysis, which reported a functional connection between this region (lobule VI) and a salience network (Buckner, Krienen, Castellanos, Diaz, & Yeo, 2011; Habas et al., 2009). The salience network is involved in the detection and integration of emotional and sensory stimuli and the coordination of switching between internal and external cognition of the default mode network (Menon & Uddin, 2010). The sensory processing scores, based on the Dunn model, suggest the ability to monitor and adjust information such that the CNS may generate appropriate responses to specific stimuli (Dunn, 1997). Our finding that sensory processing scores were associated with left lobe VI supports that the salience network, including left lobe VI, is the neural basis of sensory processing. A previous study that assessed continuous cognitive processes and resting network switching in adults suggested lobule VI involvement (Castellazzi et al., 2018). Importantly, lobule VI is the only region in the cerebellum that has been identified as crucially involved in switching from nonmotor to motor functions (Bijsterbosch, Smith, Forster, John, & Bishop, 2014; Stoodley & Schmahmann, 2009). Thus, the mechanism of the association of the left lobule VI with a tendency for less adaptive sensory processing, including hypersensitivity and/or low registration of sensory stimuli, could play an important role in triggering correct responses to environmental stimuli.

Additionally, the lobule VI is associated with negative emotions such as fear, anger, and disgust (Baumann & Mattingley, 2012). Individuals with higher sensory processing scores presented with higher trait anxiety scores (Engel‐Yeger & Dunn, 2011; Horder et al., 2014), and greater parenting stress (Austin et al., 2005; Bar‐Shalita & Cermak, 2016), which is consistent with the present report. A recent meta‐analysis study on anxiety‐related brain networks reported an association of high anxiety levels with attenuated connectivity within the salience and sensorimotor network (Xu et al., 2019). For example, adults with general anxiety disorder had low connectivity between the amygdala and the cerebellum. Therefore, our findings suggest that trait anxiety could induce less adaptive sensory processing at the subclinical level.

Although this finding has been discussed from the perspective of a potential cause‐and‐effect mechanism, our evidence only supports an association between sensory processing and the resting‐state brain activity of lobule VI. The cerebellum is considered a general‐purpose co‐processor, with its effects being dependent on various brain centers connected to individual modules (Buckner et al., 2011; Guell et al., 2019) and a cerebellar timing process that contributes to sensory perception (Baumann et al., 2015; Ivry & Keele, 1989). Conversely, participants with high lobule VI activation in the resting state could show subclinical but atypical levels of co‐processor function, as well as atypical cerebellar timing processes in the sensory domain. Further, the cerebellum could be crucially involved in the pathogenesis of anxiety; cerebellar stimulation could potentially be used to treat psychiatric disorders by enhancing the cerebellar modulation of cognition and emotion (Killion & Weyandt, 2020; Phillips, Hewedi, Eissa, & Moustafa, 2015).

Notably, mediation analysis here revealed that trait anxiety symptoms in mothers affected the spontaneous neural activity of the left lobule VI. The tendency for less adaptive sensory processing in these individuals could be induced by subclinical trait anxiety levels, which may activate the resting‐state network dynamics of the left lobule VI and prevent general‐purpose processor function. Therefore, mothers who poorly register sensory input could present a continuous error signal to the cerebellum that does not habituate (D'Angelo & Casali, 2012; Ito, 2008). Subsequently, perception becomes disordered and the mother's action toward the child seems illogical. Our findings are consistent with previous findings that mothers with high trait anxiety show poor responsiveness to the behavior of their child (Nicol‐Harper et al., 2007).

Specifically, we observed a correlation between the degree of sensory processing and both trait anxiety and levels of parenting stress. Moreover, the left‐lobule‐VI mediated between the degree of sensory processing and trait anxiety; however, cerebellar fALFF values were not correlated with parenting stress. Previous studies on parents have shown that human mothers adapt to parenting by means of reward‐related motivational brain networks. In contrast, mothers with high levels of trait anxiety and invasive care tendencies employ different brain networks, including the stress‐related occipital cortex and cerebellum (Atzil, Hendler, & Feldman, 2011; Kim, Strathearn, & Swain, 2016). Taken together, these findings suggest that was observed for less adaptive sensory processing, possibly induced by subclinical trait anxiety, could result in a compensatory increase in the resting‐state brain activity of the cerebellum, which could be a risk indicator for parenting stress.

For mothers who have a tendency for less adaptive sensory processing, it is important to formulate an environmental setting and a support mechanism that is tailored to the situation of each individual mother in order to supplement sensory processing. In particular, mothers with increased fALFF values in cerebellar lobules VI who are more likely to respond to general daily sensory stimuli such as “hold your hand over your ear to protect your ear from sound,” and “I don't notice when people come in,” which makes it easy to feel parenting stress and anxiety. Clinicians may detect them early and intervene early, and provide specific advice of the form, “If you feel stressed about your baby's noisy crying, you can stay away,” “You may attach a bell on your child to make it easier to notice any movement,” which will help reduce the stress and anxiety of rearing a child.

This study has several limitations. First, the study design and lack of a control group comprised of patients with anxiety disorders or neurodevelopmental disorders limit the validity of our findings. We could not enroll such a patient group because we aimed to employ rs‐fMRI as an unbiased whole‐brain approach for identifying the neural correlates of sensory processing and trait anxiety in child‐rearing mothers without other severe psychopathology or at high risk for anxiety disorder. However, given the paucity of findings on this topic, we believe that our contribution is important. Second, because all the psychometric assessments were self‐reported, we ran the risk of including participants with sensory processing disorders. One the other, professional evaluation of healthy individuals without sensory processing disorder is as difficult as evaluating participants with a specific diagnosis. Consequently, without self‐reporting, there is a risk of confounding neuroimaging differences associated with sensory processing and trait anxiety with those involved in enhanced resilience. Taken together, the evidence indicates that the imaging differences observed in our participants can be generalized to the general population because they are outcome independent. Third, this study was performed in a naturalistic setting with some participants having missing data, and consequently being excluded. Therefore, we cannot rule out the possibility of positive selection bias. Finally, to further evaluate the influence of sensory processing on mother–child interaction, additional studies are needed, which can measure brain activity while a mother is interacting with her child (i.e., mother/ child play task analyzed through the MRI scanner).

In summary, this study demonstrates evidence for a neural function marker underlying various levels of trait anxiety and less adaptive sensory processing by the fALFF values in the left cerebellar lobule VI in a sample of child‐rearing mothers. Further, the discussed findings indicate that fALFF could be a clinically meaningful measure for detecting maternal trait anxiety as a factor for parenting stress. Determination of this measure for daily sensory stimulation could be used to screen for parents at risk of maltreating their child for delivery of early guidance interventions, and to further elucidate individual differences within various levels of trait anxiety and parenting stress. These results of our study are promising results for clinical application. One strength of this study is that it allows for future longitudinal and comparative rs‐fMRI studies on different levels of sensory processing in mothers to assess parenting stress. To accumulate such research findings, it will be possible in the future to establish treatments (psychoeducations) tailored to individuals who have various sensory processing patterns, which will adequately mitigate parenting stress and anxiety. Taken together, we believe that these approaches, including early screening and psychoeducation, are critical for assisting mothers to cope with a tendency for less adaptive sensory processing during their parenting period and to form a stable attachment with their child, which could help prevent child maltreatment.

CONFLICT OF INTEREST

The authors declare no potential conflict of interest.

AUTHOR CONTRIBUTIONS

Akemi Tomoda conceived the project. Nobuko Sakakibara, Koji Shimada, and Akemi Tomoda designed the experiments. Nobuko Sakakibara, Kai Makita, Daiki Hiraoka, Ryoko Kasaba, Koji Shimada, Takashi X. Fujisawa, and Akemi Tomoda performed the experiments, collected the data, and analyzed the data. Nobuko Sakakibara, Ryo Kuboshita, and Akemi Tomoda wrote the manuscript. All authors have read and approved the final manuscript.