Distinct neural activation pattern of age on subcomponents of inhibitory control: a fMRI meta-analysis

doi:10.21203/rs.3.rs-1544054/v1

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Distinct neural activation pattern of age on subcomponents of inhibitory control: a fMRI meta-analysis

https://doi.org/10.21203/rs.3.rs-1544054/v1

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Inhibitory control (IC) is a fundamental cognitive function showing age-related change across the healthy lifespan. Since different cognitive load of two subcomponents of IC was existed, that is cognitive inhibition and response inhibition, there are regions differentially activated during two subcomponents of IC. In this study, we aimed to characterize whether there is distinct age-related activation pattern in these two subcomponents. A total of 278 fMRI articles were included in the current analysis. Multilevel kernel density analysis was used to derive the brain activation under each subcomponent of IC. Contrast analyses were conducted to capture the distinct activated brain regions for two subcomponents and meta-regression analyses were performed to obtain brain regions with distinct age-related activation patterns in two subcomponents of IC. The results showed that the right inferior frontal gyrus and the bilateral insula were activated during two IC subcomponents. Contrast analyses revealed stronger activation in the superior parietal lobule during cognitive inhibition, whereas greater activation was observed during response inhibition primarily in the right inferior frontal gyrus, bilateral insula and angular gyrus. Furthermore, regression analyses showed that activation of the left anterior cingulate cortex, left inferior frontal gyrus, bilateral insula, and left superior parietal lobule increased and decreased with age during cognitive inhibition and response inhibition, respectively. Results showed distinct activation patterns of aging for the two subcomponents of IC, which may be related to the differential cognitive loads of the two subcomponents of IC. These findings may help to enhance our knowledge of age-related changes in activation patterns of IC.

inhibitory control

response inhibition

cognitive inhibition

fMRI

aging

life span

Inhibitory control (IC) refers to the ability to suppress unwanted actions that are not appropriate to the current situation or resist distractions and adapt to conflicting situations (Aite A et al., 2016; Aron, 2007; Goldman-Rakic PS et al., 1996), with which we can selectively attend to task-relevant information and engage in goal-directed rather than habitual actions as well as stay away from dangerous environments in life. Dysfunctional inhibitory control is considered to be one of the symptoms of various disorders, including affective and anxiety disorders(Tomáš Paus et al., 2010), eating disorders(Bartholdy et al., 2019), learning difficulties(Eickhoff et al., 2008), and substance abuse(Steele et al., 2018), and more importantly, it is thought to be an essential cause of cognitive function decline in the aging brain(Zacks & Hasher, 2000). Herein, better understanding of the development trajectory of inhibitory control and its neural correlates across the healthy life span could not only help identify the pivotal declined time point of inhibitory control in our life and related neural networks but also help us adopt an early intervention strategy for those people who are at risk of inhibitory control deficits.

1.1. The behavioral and neural developmental trajectory of inhibitory control

In recent years, there has been an increase in the number of research examining the behavioral and neural developmental trajectory of inhibitory control across the life span (refer to Fig. S1 of the Supplementary Materials). However, the results of existing researches examining the behavioral developmental trajectory of inhibitory control were still controversial. For example, some studies suggest a steady increase of inhibitory control from adolescent to adult(Aite A et al., 2016; Velanova K et al., 2008), some studies reveal a stopped improvement in inhibitory control from adolescent to adulthood (Humphrey G & I, 2016; Luna B et al., 2004; Ordaz et al., 2013), and some studies found that inhibitory control developed until adolescents and declined slightly from young adults to elderlies (Schachar R & GD, 1990; Williams BR et al., 1999).

In neuroimaging research examining the neural development trajectory of inhibitory control, the results were also not consistent. To be specific, increased frontal activation in adults compared with children has been observed in some fMRI studies(Bunge et al., 2002; Luna B et al., 2004; Tamm et al., 2002). Rubia et al.(2013) reported superior performance in adults was paralleled by increased activation in a network comprising prefrontal and parietal cortical regions and putamen in cognitive inhibition. Research by Vink et al (2014) showed increases in activation with age during response inhibition in the right striatum, right inferior frontal cortex (rIFG), and SMA. Using a Go/NoGo task, Cope et al (2020) reported a significant positive linear activation associated with age in the frontal, temporal, parietal, and occipital cortices, meaning activations in these regions increased with aging in response inhibition. Whereas other neuroimaging studies have reported greater activation in frontal and parietal lobes in children and adolescents compared with adults in cognitive inhibition (Booth et al., 2003). Durston et al(2002) has reported the activations in the bilateral ventral prefrontal cortex, the right parietal lobe, and the right dorsolateral prefrontal cortex were larger for children than adults during response inhibition task. Apart from that, age-related degeneration in inhibitory control task-relevant white matter was also found (Coxon et al., 2012; Forstmann et al., 2012). The above studies, which report controversial development patterns of behavioral performance and neural activations during inhibitory control, prove that the development patterns of inhibitory control with aging need to be further clarified.

As mentioned above, results from existing studies on behavioral and neural developmental trajectory of inhibitory control across life span are still controversial, which may come from the samples with limited age ranges included in different researches or the phenomenon that there are few existing studies or theories have systematically differentiated age-related changes between different inhibitory control tasks. Specifically, Aite et al.(Aite A et al., 2016) recruited 160 participants ranged from 10–23 years old to investigate the developmental patterns of inhibitory control and the degree of specificity of inhibitory control in children, adolescents and adults, which did not include older adults. Similarly, study from Humphrey and Dumontheil(Humphrey G & I, 2016), which revealed a stopped improvement in inhibitory control from adolescent to adulthood, only included 90 participants ranged from 12–18 years old. On the other hand, most of current studies on behavioral and neural developmental trajectory of inhibitory control across life span included only a single type of inhibitory control tasks(Andrés et al., 2008; Anguera & Gazzaley, 2012; Booth et al., 2003). For example, results from Anguera et al.(Anguera & Gazzaley, 2012) reported that in a stop signal task, the stop signal reaction time of older adults was slower than that observed in younger adults, suggesting an age-related deficit in inhibitory control in the older population, while Booth et al.(Booth et al., 2003) reported that children had more errors and slower reaction times compared to adults in a selective attention task. Similarly, Andrés et al.(Andrés et al., 2008) found that aging affected the ability to cancel a strong response in the stop signal task but did not affect the performance in the Stroop task. It is worth noticed that Hasher & Zacks(Hasher & Zacks, 1988) postulated a theoretical framework that capacity of working memory was constrained by the resources and varied in different working memory tasks, and this capacity of working memory declined across the adult lifespan. The above conclusions inspire us that as inhibitory control and working memory are both subcomponents of executive functions(Miyake et al., 2000), and there are not only shared neural correlates(Miyake et al., 2000) but also certain interactions(Pennington, 1996) in the process of inhibitory control and working memory, the theoretical framework of capacity of working memory postulated by Hasher & Zacks(Hasher & Zacks, 1988) may also be applicable to inhibitory control, which means that different developmental trajectory of inhibitory control across life span reported by previous studies may due to different cognitive load of inhibitory control tasks included in each of previous study since that different inhibitory control tasks comprise differential cognitive processes, which may require varying degrees of task demand and thereby inhibitory load(Sebastian, Baldermann, et al., 2013). Therefore, there is a critical need to clarify similarities and differences between different inhibition control tasks(Andrés et al., 2008; Anguera & Gazzaley, 2012; Dalley et al., 2011; Sebastian, Pohl, et al., 2013; Swick et al., 2011), and further explore the developmental trajectory of inhibitory control throughout a wider age range under the framework of different subcomponents of inhibitory control.

1.2. Subcomponents of inhibitory control and its neural correlates

Recent study suggested that inhibitory control is not a unitary construct, which could be further differentiated into cognitive inhibition and response inhibition. Cognitive inhibition involves suppression of competing cognitive processing in order to solve relevant problems. Response inhibition involves suppression of a prepotent response or an already initiated actions to perform a different, more context-appropriate response(Sebastian, Baldermann, et al., 2013; Yuecui Kan et al., 2021). The dissociation of cognitive inhibition and response inhibition may provide useful information to further understand different manifestations of inhibitory dysfunctions, which will greatly benefit clinical research.

Differences between inhibition difficulties and complexity of two types of inhibition tasks might originate from differences in cognitive load in these subcomponents of inhibitory control tasks(Sebastian, Baldermann, et al., 2013). Results of behavioral study from Stahl et al.(Stahl et al., 2014) used a multi-component modeling approach and showed that the control of response-related interference is not a unitary construct and the cognitive interference can be separated from response inhibition. Given, Noreen and Macleod(Noreen & MacLeod, 2015) showed no significant correlations or commonalities between different inhibition tasks such as Stroop, Go/NoGo and Stop signal tasks, suggesting that different inhibitory control tasks primarily assess different aspects of inhibition processes and involve different brain system or neural mechanisms, which may attribute to the variety of inhibitory control tasks with different cognitive load and eventually result in differences of inhibition behavioral performance with aging across the healthy life span.

Cognitive inhibition can be captured by paradigms including the Stroop, Flanker, Simon, stimulus response compatibility (SRC) and antisaccade tasks(Almdahl IS et al., 2021; Stahl et al., 2014; van Velzen LS et al., 2014). In the Stroop or Flanker tasks, participants are required to suppress interference due to stimulus competition or irrelevant information, and need to resolve a conflicting representation arising from cognitive level(Hung et al., 2018). A measure of cognitive inhibition is thus the difference in reaction time in incompatible as compared to compatible or baseline trials(Sebastian, Baldermann, et al., 2013). While, response inhibition, the ability to suppress prepotent or automatic responses, is usually assessed by the Go/NoGo task (Haoyun Zhang et al., 2019; Le et al., 2020) or stop signal task(S. E. Hu et al., 2019; Jenny R. Riech et al., 2021). In the Go/NoGo task, participants have to withhold a prepotent but not yet initiated action, while in the stop signal task participants have to cancel an already initiated response. A measure of response inhibition is the proportion of correctly withheld responses as compared to incorrectly withheld actions in a no-go stimulus or the stop signal reaction time, which may reflect the latency of inhibition process in stop signal task.

In line with findings of behavioral study between cognitive inhibition and response inhibition, researches on characterizing the neural correlates of inhibitory control also found that there is a significant difference in neural correlates between the two components. For example, Rubia et al.(Rubia et al., 2006) demonstrated a stronger activation of the cingulo-opercular network in cognitive inhibition compared to response inhibition. Sebastian et al(Sebastian, Baldermann, et al., 2013) revealed that cognitive inhibition activated the pre-SMA and parietal regions to a greater extent than response inhibition. More recently, through quantitatively synthesizing the published studies on inhibitory control, Hung et al.(2018) reported a stronger activation of the dorsal frontal and parietal lobe for cognitive inhibition tasks compared to a stronger activation of the fronto-striatal network including the dorsal anterior cingulate cortex (dACC), supplementary motor cortex, lateral prefrontal cortex, basal ganglia and parietal regions in response inhibition tasks, while the left anterior insula is consistently activated in cognitive inhibition and response inhibition. In sum, the findings of distinct inhibitory networks during different subcomponents of inhibitory control provided supporting evidence for the differences in neural correlates between cognitive inhibition and response inhibition. It was further confirmed that age-related distinct brain regions activation patterns in two subcomponents of inhibitory control across life span, which may originate from different cognitive load involved between two subcomponents of inhibitory control processes, can reflect the behavioral developmental trajectory of inhibitory control.

In current study, we investigate how neural activation during two subcomponents of inhibitory control changes across the healthy lifespan with aging using meta-analytic technology. There are two aims that should be explored in the current study: first, to characterize the common or distinct neural correlates in two subcomponents of inhibitory control. Second, to identify the distinct activation pattern with aging in two subcomponents of inhibitory control. Since the cognitive loads among two subcomponents are different, we expect that the neural developmental trajectory of inhibitory control may show a distinct activation pattern in two subcomponents of inhibitory control.

2.1 Literature search and article selection

Firstly, two online citation indexing services—PubMed and Web of Science—were searched. This search used keywords “fMRI” with "response inhibition", "interference resolution", "action withholding", "action cancellation", "response inhibition", "cognitive inhibition", "inhibitory control", "stop signal", "stopping", "go nogo","action restraint" or "countermanding", including articles published prior to April,2020, yielding a total of 9419 articles. After removing duplicates, the total articles that were screened were 7985. We then compiled 39 eligible articles identified in a previous meta-analysis(Zhang et al., 2017). The following exclusion criteria were applied to eliminate articles that were not directly relevant to this study:(1) non-original studies (e.g., review, abstract), (2) studies that did not report results either in Talairach or Montreal Neurology Institute (MNI) coordinate space, (3) studies with sample size below five, (4) studies on older adults with dementia, head injury, stroke or any neurological or other psychiatric diseases, (5) pharmacological or training-related studies, only if they did baseline comparison, and fulfill our inclusion criteria, then we can include it, (6) no control group or within group contrast. A total of 278 articles were included in the current meta-analysis. Figure 1 shows the detailed searching and selection procedures. The final dataset was then divided into two subcomponents of inhibitory control: 60 articles in cognitive inhibition and 218 articles in response inhibition.

Insert Fig. 1 here.

2.2 Data Extraction

We extracted the following information from each study: authors, year of publication, sample size, experimental design, paradigms, mean age with the age range, task contrasts and cluster coordinates in the MNI or Talairach space.

2.3 Experiment categorization

2.3.1 Cognitive inhibition

Cognitive inhibition is the inhibitory process of suppression of competing cognitive processing in order to solve relevant problems(Hung et al., 2018). For cognitive inhibition domain, we include commonly used cognitive interference paradigms(Hung et al., 2018), which are Stroop, Flanker, Simon, stimulus response compatibility (SRC) and antisaccade tasks. We examined changes in activation between incongruent and neutral or incongruent and congruent conditions to measure a straightforward processing of cognitive interference. 60 articles consisting of 68 experiments were included to explore the neural correlates of cognitive inhibition. The characteristics of each study are listed in Table S1 of the Supplementary Materials.

2.3.2 Response inhibition

Response inhibition is the process of suppression of a prepotent response to perform a different, more context-appropriate response(Hung et al., 2018). For response inhibition, we include the classical paradigms, including Go/NoGo and stop-signal tasks, which primarily require inhibition of prepotent motor responses. Qualified response inhibition experimental contrasts measured differences in activation between go and no-go or stop conditions. 218 articles comprising 223 contrasts using the Go/NoGo paradigm or Stop Signal paradigm were employed to identify the response inhibition-related activation patterns.

2.4 Multilevel kernel density analysis (MKDA)

Meta-analyses were performed using the MKDA (Wager TD et al., 2007) toolbox (https://www.colorado.edu/ics/research/wager-lab) to identify brain regions activated during inhibitory control. Peak effect coordinates from each study were convolved with a spherical kernel (r = 16 mm)(Wager TD et al., 2004) to generate comparison indicator maps (CIMs), with a value of one indicating that 'this study activated near this voxel' and a value of zero indicating that ‘this study did not activate near this voxel'. The CIMs are averaged to yield the proportion of study in which activation was observed within 16 mm of each voxel. The family wise error (FWE) rate was estimated to correct for multiple comparisons (5000 permutations).

Prior meta-analyses, like activation likelihood estimate (ALE), count how many peak coordinates within each voxel divided by brain and compare this to the number expected by chance if peak coordinates were randomly distributed in the brain, which are limited by the consequence that peak coordinates by any single study may overly influence the results from analyses(Radua & Mataix-Cols, 2012). Using MKDA may overcome this limitation by separating the peaks of each study. In the MKDA method, the null hypothesis is that the n peak coordinates reported in the set of studies to be analyzed are randomly and uniformly distributed throughout gray matter. Thus, the meta-analytic results in this study represent common activated regions across studies: regions in which significant activations were observed in the local neighborhood by more studies than would be expected by chance (p < 0.05, FWE corrected across the entire brain). Specifically, to characterize brain activation patterns, first, we identified brain regions that showed significant convergence across 278 studies comprising 4393 foci from 291 contrasts. Then, contrast analyses were conducted to verify the differences of cognitive load for two subcomponents of inhibitory control and capture the selectively or preferentially activated brain regions for two subcomponents of inhibitory control: cognitive inhibition vs. response inhibition.

2.5 Meta-regression analyses with age

In order to further assess age-related change of activation patterns in subcomponents of inhibitory control, The effect-size seed-based d mapping (ES-SDM) toolbox (SdmPsiGui-v6.21from the Seed-based d Mapping project) was used to perform meta-regression analyses which is for the reason that the ES-SDM software can provide accurate results of regression analyses incorporating meta-regression methods. This is achieved by first using peak coordinates and their statistical values to recreate the statistical parametric maps, and then conducting an image-based meta-analysis(J. Radua & D. Mataix-Cols, 2012). The full width at half maximum (FWHM) in SDM was set at 20 mm(J. Radua & D. Mataix-Cols, 2012) by default to control for false positives and the resulting statistical maps were thresholded at p < 0.05 to control for family-wise error rate. To be specific, we performed two meta-regression analyses in ES-SDM. Data involved in meta-regression analyses derived from response inhibition contrasts and cognitive inhibition contrasts separately. Given that age ranges reported from the original articles are different, the age computed in meta-regression analysis as a continuous variable was determined by the mean age of each sample in the original articles. Results from these two regression analyses were then compared to obtain brain regions with distinct age-related activation patterns in two subcomponents of inhibitory control.

2.6 Validation analyses

Response inhibition (223 experiments of Go/NoGo and stop signal tasks) included much larger number of experiments compared to cognitive inhibition (68 experiments). In order to test the effect of the experiment numbers, we randomly select 68 contrasts from response inhibition and repeated the MKDA analysis and the meta-regression analysis with age using the same settings.

Further, to more completely explore the age-related changes in two subcomponents of inhibitory control within individuals of different ages, we performed additional MKDA analyses. To be specific, we divided dataset from all articles included in the current meta-analysis into four age-groups: underaged, young adults, middle-aged adults and older adults. Then we performed contrast analyses and computed differences among all age-groups in two subcomponents of inhibitory control.

3.1 Meta-analysis of all included inhibitory control experiments

The MKDA analysis of the 278 studies showed significant activations of clusters in both hemispheres including the frontal cortex, the angular gyrus and the supplementary motor area (Fig. 2a). The details of results are listed in Table S2 of the Supplementary Materials.

3.2 Brain activation patterns of each component

3.2.1 Brain activation patterns of cognitive inhibition

In both hemispheres, activated areas during cognitive inhibition tasks included the inferior frontal gyrus, precentral gyrus, anterior insula, inferior parietal lobule, supplementary motor cortex, superior parietal lobule, superior frontal gyrus, middle cingulate gyrus, inferior frontal gyrus and angular gyrus (Fig. 2b; Table 1). Unilateral activations were observed in the right middle frontal gyrus.

Table 1

Brain activation in two subcomponents of inhibitory control
Regions	R/L	MNI			No.Voxs	Maximum P
		x	y	z
Cognitive inhibition
Insula	L	-42	16	4	107	0.32
Insula	R	42	18	-8	397	0.48
Inferior frontal gyrus	R	32	26	-8	210	0.45
Inferior frontal gyrus	L	-40	14	24	159	0.34
Middle frontal gyrus	R	38	4	42	148	0.32
Inferior parietal lobule	L	-34	-46	40	107	0.34
Inferior parietal lobule	R	42	-46	44	188	0.36
Superior parietal lobule	L	-26	-58	48	298	0.45
Superior parietal lobule	R	24	-62	52	269	0.39
Middle cingulate cortex	R	2	12	38	602	0.41
Middle frontal gyrus	L	-26	0	58	166	0.34
Supplementary motor area	R	2	16	50	378	0.41
Supplementary motor area	L	-8	6	52	566	0.4
Response inhibition
Insula	L	-36	16	-4	1983	0.45
Insula	R	44	22	-6	1859	0.55
Inferior frontal gyrus	R	46	16	12	1159	0.48
Inferior frontal gyrus	R	44	36	12	809	0.4
Inferior frontal gyrus	L	-42	16	14	578	0.38
Middle frontal gyrus	R	32	46	28	982	0.34
Precentral gyrus	R	42	4	40	1080	0.38
Precentral gyrus	L	-44	8	34	250	0.22
Middle cingulate cortex	L	0	28	32	901	0.37
Middle cingulate cortex	R	6	-18	34	86	0.2
Superior frontal gyrus	L	-2	20	44	1127	0.41
Superior frontal gyrus	R	20	6	56	857	0.39
Supplementary motor area	R	6	12	56	1148	0.44
Supplementary motor area	R	4	-2	60	514	0.33
Middle temporal gyrus	R	56	-36	2	782	0.3
Superior temporal gyrus	R	58	-44	18	1238	0.38
Supramarginal gyrus	R	50	-44	38	2399	0.43
Supramarginal gyrus	L	-58	-46	26	502	0.27
Angular	L	-52	-52	28	147	0.25
Angular	R	32	-58	48	1374	0.38
Inferior parietal lobule	L	-38	-50	46	933	0.31
Maximum P is the maximum proportion of studies exhibiting the effect at the peak density weighted by sample size. The coordinates are Montreal Neurological Institute (MNI) standard stereotaxic spaces. The voxel size is 2 × 2 × 2mm3. R/L: right/left hemisphere

Insert Fig. 2 and Table 1 here.

3.2.2 Brain activation patterns of response inhibition

Data from response inhibition experiments revealed activations in the right middle frontal gyrus, the right angular gyrus which extend to the middle temporal gyrus and superior temporal gyrus, the right inferior temporal gyrus, and the left middle cingulate gyrus. In addition, activation areas in both hemispheres were observed in the supplementary motor cortex, middle cingulate gyrus, superior frontal gyrus, precentral gyrus, inferior frontal gyrus, anterior insula, inferior parietal lobule and supramarginal gyrus (Fig. 2c; Table 1).

3.3 Common and distinct activation between two subcomponents

Activation patterns common to the two subcomponents of inhibitory control were derived by conjunction analysis (Fig. 3a). Regions commonly activated in two subcomponents of inhibitory control included (1) the supplementary motor cortex, which extended to the middle cingulate cortex and the superior parietal lobule in both hemispheres; (2) the inferior frontal gyrus, which extended to the middle frontal gyrus, and insula in both hemispheres; (3) the right superior occipital gyrus and the left middle occipital gyrus, and (4) the inferior parietal lobule and angular gyrus in both hemispheres.

Contrast analyses between cognitive inhibition and response inhibition revealed significantly different regions activated in two subcomponents of inhibitory control. Specifically, compared to response inhibition, higher activation was found in cognitive inhibition in the left superior parietal lobule (Fig. 3b; Table 2). On the other hand, higher activations observed in response inhibition than cognitive inhibition were in the frontal cortex including the bilateral insula and inferior frontal gyrus, the right middle frontal gyrus and the right superior frontal gyrus, which extended to the bilateral putamen (Fig. 3c; Table 2). Regions in the right middle temporal gyrus and the right angular gyrus also showed higher activation in response inhibition than cognitive inhibition.

Table 2

Brain activation differences between cognitive inhibition and response inhibition
Regions	R/L	MNI			No.Voxs	Maximum P
		x	y	z
Cognitive inhibition > Response inhibition
Superior parietal lobule	L	-20	-62	48	37	0.21
Response inhibition > Cognitive inhibition
Middle frontal gyrus	R	30	42	26	38	0.16
Angular gyrus	R	54	-50	34	450	0.21
Putamen	R	30	10	-6	175	0.19
Middle temporal gyrus	R	56	-26	-4	591	0.19
Insula	L	-30	18	-8	1006	0.23
Inferior frontal gyrus	R	38	24	-12	271	0.2
Insula	R	30	16	-10	312	0.23
Superior temporal gyrus	R	56	-22	-4	445	0.19
Middle temporal gyrus	R	50	-34	-2	146	0.17
Putamen	L	-28	10	-6	114	0.19
Angular gyrus	R	56	-52	34	173	0.21
Inferior parietal lobule	R	48	-50	40	99	0.2
Middle frontal gyrus	R	28	42	26	31	0.16
Maximum P is the maximum proportion of studies exhibiting the effect at the peak density weighted by sample size. The coordinates are Montreal Neurological Institute (MNI) standard stereotaxic spaces. The voxel size is 2 × 2 × 2mm3. R/L: right/left hemisphere

Insert Fig. 3 and Table 2 here.

3.4 Age related brain activation patterns of each component

3.4.1 Age-related change of activation patterns in cognitive inhibition

In the cognitive inhibition tasks, results from a meta-regression analysis with age as a continuous variable across all studies show a positive association with clusters in (1) the middle cingulate cortex, anterior cingulate cortex and insula in both hemispheres; (2) the angular gyrus, superior parietal lobule, inferior frontal gyrus and supplementary motor cortex in left hemisphere. Besides, a negative association between age and clusters were found in bilateral middle frontal gyrus, the left inferior parietal lobule, the right angular gyrus and the right inferior frontal gyrus (Fig. 4a).

3.4.2 Age-related change of activation patterns in response inhibition

Activation in the response inhibition tasks showed significant positive correlations with age in the right angular gyrus, the right middle frontal gyrus, bilateral inferior parietal lobule and bilateral middle cingulate cortex, whereas a negative correlation with age was in (1) the anterior cingulate cortex, inferior frontal gyrus, insula, hippocampus and superior parietal lobule in left hemisphere; (2) the superior frontal gyrus, cerebellum, insula and inferior frontal gyrus in right hemisphere (Fig. 4b).

Insert Fig. 4 here.

3.5. Distinct activation patterns with age between subcomponents of inhibitory control

To characterize distinct brain regions with age-related changes in activation patterns between two subcomponents of inhibitory control, we overlapped results from regression analyses in two subcomponents with age and found different age-related activation pattern between subcomponents (Fig. 4). To be specific, activation of inhibition regions including the left anterior cingulate cortex, the left inferior frontal gyrus, bilateral insula and the left superior parietal lobule showed a positive correlation with age in cognitive inhibition tasks, but a negative association with age in response inhibition tasks.

3.6 Validation analysis

The evaluation of the experiment number contrasting two subcomponents showed no significant differences between the real contrasts and the randomly selected 68 experiments for response inhibition. The activated brain areas are reported in Table S3 and Figure S2 of the Supplementary Materials.

The results from additional MKDA analyses with four age groups are basically consistent with the current research, The details of the results can be seen in Table S4-S6 and Figure S3-S5 of the Supplementary Materials.

Using MKDA and ES-SDM allowed the current meta-analysis to characterize the neural correlates and age-related effects on different subcomponents of inhibitory control. We observed brain areas including the inferior frontal gyrus, insula, middle cingulate cortex and the inferior parietal gyrus are activated across two subcomponents. Contrast analyses to elucidate the distinct neural substrates for each subcomponent revealed that relative to response inhibition, cognitive inhibition produced stronger activation in the left superior parietal lobule, while response inhibition primarily recruited the right inferior frontal gyrus, insula, middle temporal gyrus and angular gyrus. Importantly, by performing a meta-regression analysis with age as a continuous variable, we found distinct age-related activation patterns in different subcomponents of inhibitory control in brain regions including the left anterior cingulate cortex, the left inferior frontal gyrus, the left superior parietal lobule and bilateral insula. Overall, our results indicate common and distinct neural correlates and distinct age-related activation patterns in two subcomponents of inhibitory control.

4.1 Common and distinct neural activation in two subcomponents of inhibitory control

The MKDA results showed that brain regions including the inferior frontal gyrus, insula, middle cingulate cortex, and the superior parietal lobule were activated by both inhibition subcomponents. Therefore, it suggested that the inferior frontal gyrus, insula, middle cingulate cortex, and inferior parietal lobule played core roles in inhibitory control(Choi EY et al., 2012; Yeo BT et al., 2011), which is in line with previous studies(Cieslik EC et al., 2015; Lemire-Rodger et al., 2019; Zhang F & S., 2019). Moreover, Hobeika et al.(Hobeika L et al., 2016) reported activation of domain-oriented regions within the inferior frontal gyrus and conflict-detecting regions within the middle cingulate cortex in both inhibition subcomponents, which can be interpreted as that either cognitive inhibition process or response inhibition process involves the process of spatial orienting and conflict detecting(Hung et al., 2018).

The main clusters of activation between two subcomponents of inhibitory control were observed in (1) the IFG extending to the insula and (2) the middle cingulate cortex (MCC) and the superior parietal lobule. The IFG is known to engage in the process of inhibiting automatic but irrelevant actions while activating task relevant responses at the same time(Sharp et al., 2010; Wang et al., 2019). Moreover, activation of the IFG during detecting changes in the stimulus features is also observed(Dodds CM et al., 2010). The anterior insula has been considered as the center that controls brain activity across different tasks and stimulus modalities and regulates inhibitory control mechanisms(Cai et al., 2019). Previous study from Wager et al(Wager et al., 2005) has reported a positive correlation between neural activity in the anterior insula and task performance in different inhibitory control tasks. One explanation for this positive correlations is that regions including the anterior insula implement a regulating processes that increases with greater input conflict(Miller & Cohen, 2001). Regarding the middle cingulate cortex, studies have revealed that the MCC is the key region for conflict detection in information processing, reallocation of attention resources, and the formation of corresponding actions(Badzakova-Trajkov G et al., 2009). When participants were required to perform a dual task, such as the Stroop task, stronger MCC activation can be observed(Hoffstaedter F et al., 2014; Hoffstaedter F et al., 2013; Palomero-Gallagher N et al., 2019). Based on previous findings and the results on the common regions engaged in different inhibitory control tasks, we propose that the inferior frontal gyrus, the insula, the superior parietal lobule and MCC may comprise the core neural network of the inhibitory control system.

In this meta-analysis, the inhibitory control paradigms classified as cognitive inhibition required conflict resolution and inhibition of response tendencies for successful responding(Nee DE et al., 2007). When performing the cognitive inhibition tasks (i.e. Stroop, Simon, Flanker tasks), participants need to actively reorient attention away from task-relevant stimulus location or feature and then select and initiate an adequate response. Reorienting of attention mainly involved the pre-supplementary motor area and the superior parietal lobule. The superior parietal lobule is showed to play an essential role in facilitating attention re-allocating to characteristics of stimuli and then re-directing attention. Therefore, significantly stronger activation in the left superior parietal lobule observed in cognitive inhibition than response inhibition in current contrast analysis indicated more attentional reallocation load or requirement when performing cognitive inhibition tasks. It thus proved that cognitive inhibition depends largely on inhibition processes of predominant mental set regulated by goal and conflicts(Nee DE et al., 2007).

Whereas for response inhibition, Go/NoGo and stop signals tasks encompass future action selection and inhibition of a predominant response tendency or an ongoing response respectively. As mentioned above, the inferior frontal gyrus plays an inhibitory role in resolving conflicts during response execution and the anterior insula involves in the regulating process of response inhibition. Thus, activated regions in response inhibition were greater than cognitive inhibition primarily located in the inferior frontal gyrus and the anterior insula. The distinctiveness between response and cognitive inhibition, we suggest, may partly due to the difference of cognitive load in these inhibitory control tasks. Participants are required to resolve conflicts and involved more sensory or stimulus-related neural activity in cognitive inhibition tasks, while inhibitory load may further increase in the response inhibition tasks, which require inhibiting a predominant tendency or stopping of already initiated actions. Furthermore, these tasks differ in terms of task-related complexity. Suppressing a response tendency or canceling an ongoing action might increase the inhibitory demand as compared to suppressing interference due to irrelevant information or resolving conflicts, as is the case in cognitive inhibition tasks(Sebastian, Baldermann, et al., 2013). As the engagement of the IFG, MFG and insula plays a core role in the process of inhibitory control, activation in these regions were observed increase with the demands of inhibitory control tasks increase in response inhibition. Overall, these results provide further support for the distinctiveness between response and cognitive inhibition.

4.2 Age-related changes in activation on subcomponents of inhibitory control

In this meta-analysis, we observed neither a completely coherent increase nor a decrease in the inhibition network between two subcomponents. In the cognitive inhibition tasks, activation showed positive association with age in the anterior cingulate cortex, the insula, the superior parietal lobule and the inferior frontal gyrus. These age-related changes fit with the existing literature that prefrontal regions, including the IFG and the MFG became more active with aging(Sebastian, Pohl, et al., 2013). Older adults increasingly recruit additional prefrontal regions to compensate for age-related declining brain structure and function in cognitive inhibition tasks(Sebastian, Baldermann, et al., 2013).Meanwhile, Nielson et al(Kristy A. Nielson et al., 2002) has revealed compensational activation in the left prefrontal cortex during cognitive inhibition. These results may support our assumption that, a simple task in cognitive inhibition required enough functional compensation in prefrontal regions recruited with aging.

A different pattern of functional age-related changes was found in the response inhibition tasks. We found activation of the response inhibition network including the left anterior cingulate cortex, bilateral inferior frontal gyrus and insula, left superior parietal lobule and the right superior frontal gyrus was negatively correlated with age. These seemingly differential results might also be explained by differences in inhibitory load. Based on study from Reuter-Lorenz and Cappell(Reuter-Lorenz & Cappell, 2008), the current findings suggest that the aging brain fails to recruit additional inhibitory regions with inhibition load increasing and a resource ceiling is reached. With task demand increasing, relative hypoactivation is associated with aging in both core and expand inhibition networks, which may further represent a limitation of abilities for flexibly recruiting additional inhibition networks in older adults(Cappell KA et al., 2010; Schneider-Garces NJ et al., 2010). Prakash et al(Prakash RS et al., 2009) has pointed out the flexibility of the cortical regions becomes limited in older adults with number of conflicts increasing. It is important to note that the above-mentioned theories have partly been based on researches about age-related differences in working memory. Turner and Spreng(Turner & Spreng, 2012) reported differential changes in activation patterns for working memory with different cognitive load and inhibition with age. In addition, results from Sebastian(Sebastian, Baldermann, et al., 2013) in contrast to those from Turner and Spreng(Turner & Spreng, 2012) indicated different activation patterns in prefrontal regions during inhibition with medium inhibitory load between low inhibitory load. Our results and these studies indicate that high inhibition task load might result in limited allocation of cognitive resources in older adults, which can be reflected in declined performance associated with lower activation of inhibition networks(Billig AR et al., 2020; Bloemendaal et al., 2018; Pasion R et al., 2019).

4.3 Implications

Several neuroimaging studies have contributed a lot in enhancing our knowledge of neural correlates of subcomponents of inhibitory control or age-related change in activation in two subcomponents(Hung et al., 2018; Simmonds et al., 2008; Swick et al., 2011; Wright et al., 2014; Zhang et al., 2017). However, these studies are limited for that they focused on a restricted age range(Kristy A. Nielson et al., 2002; K. A. Nielson et al., 2004), included a single subcomponent of inhibitory control(S. Hu et al., 2018; Simmonds et al., 2008; Wright et al., 2014), or used a small sample size(Simmonds et al., 2008; Tsvetanov et al., 2018). For example, Simmonds et al.(Simmonds et al., 2008) included only 11 studies in their meta-analysis. It has been argued that to keep the replicability of a meta-analysis, which should include at least 20 studies(Eickhoff SB et al., 2016), otherwise the conclusions may be questionable. Moreover, Tsvetanov et al.(Tsvetanov et al., 2018) conducted a study on activity and connectivity differences underlying inhibitory control across the adult life span only using response inhibition tasks including Go/NoGo and stop signal tasks. Hung et al.(Hung et al., 2018) reported that unique neural activity was associated with different inhibitory control tasks, but the age-related effects on different types of inhibitory control tasks was unknown. Our meta-analysis addressed these limitations and provided an updated review; thus, our understanding on changes of neural correlates underlying inhibitory control with aging was further advanced.

Through synthesizing data from different subcomponents, we found that brain regions including the inferior frontal gyrus and anterior insula, as well as regions including the middle cingulate cortex and supplementary motor cortex are consistently activated across all inhibition tasks. This finding may suggest that these brain areas are core inhibitory control regions. Meanwhile, different age-related changes in activation between subcomponents of inhibitory control can be observed. Functional reorganization of the aging brain in different inhibitory control tasks showed a complex pattern of increase and decline: the corresponding cognitive inhibition tasks require the older adults to increasingly recruit the core inhibition network and additional inhibitory regions, such as frontal regions and bilateral insula. However, a contrary pattern of an age-related decline in the inhibitory network including prefrontal areas and MCC were showed during the process of response inhibition. Current results suggest that these differences might result from the increasing demands on inhibitory function from cognitive inhibition to response inhibition. Furthermore, age-related increased activation of additional inhibitory networks is limited. When the tasks demand exceeds the older adults’ capacity, the activation in inhibition network decreased evidently. These findings are of significance for the understanding of the neuro-developmental mechanisms of inhibitory control and may provide insights into inhibitory control deficits in clinical settings.

4.4 Limitations

The current study still has some limitations. We note the potential limitation in meta-analysis methods in general is that any meta-analysis method is prone to publication bias, since we only consider results available in the published literature and original studies which report coordinates. Moreover, we cannot control the statistical method used in original articles for thresholding the data. A trend to store unthresholded statistical maps is growing up, which allows to perform image-based meta-analyses in the future studies(Gorgolewski et al., 2015).

Another unavoidable limitation – given that the age computed in meta-regression analysis as a continuous variable was determined by the mean age of each sample in the original articles – was that the mean age was affected by extreme values, which cannot well represent the age distribution of all subjects in each original literature. As mentioned above, to more completely explore the age-related changes in two subcomponents of inhibitory control within individuals of different ages, we performed additional MKDA analyses as validation analyses. To be specific, we divided dataset from all articles included in the current meta-analysis into four age-groups: age ranges between 0 and 18 years of age for underaged, 18–35 years of age for young adults, 35–55 years of age for middle-aged adults and 55–80 years of age for older adults. Then we performed contrast analyses and computed differences among all age-groups in two subcomponents of inhibitory control: underaged vs. young adults, young adults vs. middle-aged adults, middle-aged adults vs. older adults, underaged vs. middle-aged adults, young adults vs. older adults and underaged vs. older adults. The results (see Table S4-S6 and Figure S3-S5of the Supplementary Materials) are basically consistent with the current research, which may confirm the reliability and stability of the current research to a certain extent. However, more research reporting results for narrower age-ranges is still critical for future work.

In this meta-analysis, we examined the neural correlates of subcomponents of inhibitory control and the difference of age-related changes in activation between subcomponents. Activations of the MCC, the supplementary motor area, the inferior frontal gyrus, the inferior parietal lobule and the anterior insula were common across different inhibition processes, which revealed that these regions are the core neural system engaged in inhibitory control. On the other hand, differences in the activation patterns of subcomponents of inhibitory control with aging showed a complex pattern in functional reorganization of the aging brain. Specifically, when performing cognitive inhibition tasks, stronger activation of the core inhibition regions was observed in older adults, while activation in prefrontal areas in older adults declined during response inhibition tasks. We summarize that these differences may be driven by the different demand between inhibitory control tasks. Individual recruits more additional inhibition-related brain regions with aging when performing an inhibitory control task. However, with the load of inhibition tasks increasing, limited reallocation of cognitive resources in older adults eventually results in lower activation of inhibition brain regions in older adults during inhibitory control processes. These results may further enhance our knowledge of age-related changes in activation patterns of inhibitory control and may provide insights into inhibitory control deficits in clinical settings.

Acknowledgement

This study was supported by Nature Science Foundation of China (ref: 31900806). The funding organizations played no further role in study design, data collection, analysis and interpretation, and paper writing.

Data and code availability statements

Study in this meta-analysis were searched from two online citation indexing services, PubMed and Web of Science, using the keywords “fMRI” with “response inhibition,” “interference resolution,” “action withholding,” “action cancellation,” “response inhibition,” “cognitive inhibition,” “inhibitory control,” “stop signal,” “stopping,” “Go NoGo,” “action restraint,” or “countermanding,” including articles published prior to April 2020. The following exclusion criteria were applied to exclude articles that were not directly relevant to this study: (1) non-original studies (e.g., reviews and abstracts); (2) studies that did not report results either in Talairach or Montreal Neurology Institute (MNI) coordinate space; (3) studies with a sample size below five; (4) studies on older adults with dementia, head injury, stroke, or any other neurological or psychiatric diseases; (5) pharmacological or training-related studies (if a baseline comparison was performed and the study otherwise fulfilled our inclusion criteria, the article was included); (6) no control group or within-group contrast. Information on source datasets included in the meta-analysis can be found in Supplementary Table S1.

Multi-level kernel density analysis (MKDA) toolbox in this paper can be found here: http://wagerlab.colorado.edu. Effect-size seed-based d mapping can be found here: https://www.sdmproject.com.

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No competing interests reported.

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Distinct neural activation pattern of age on subcomponents of inhibitory control: a fMRI meta-analysis

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Abstract

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1. Introduction

1.1. The behavioral and neural developmental trajectory of inhibitory control

1.2. Subcomponents of inhibitory control and its neural correlates

2. Methods

2.2 Data Extraction

2.3 Experiment categorization

2.3.1 Cognitive inhibition

2.3.2 Response inhibition

2.4 Multilevel kernel density analysis (MKDA)

2.5 Meta-regression analyses with age

2.6 Validation analyses

3. Results

3.1 Meta-analysis of all included inhibitory control experiments

3.2 Brain activation patterns of each component

3.2.1 Brain activation patterns of cognitive inhibition

3.2.2 Brain activation patterns of response inhibition

3.3 Common and distinct activation between two subcomponents

3.4 Age related brain activation patterns of each component

3.4.1 Age-related change of activation patterns in cognitive inhibition

3.4.2 Age-related change of activation patterns in response inhibition

3.5. Distinct activation patterns with age between subcomponents of inhibitory control

3.6 Validation analysis

4. Discussions

4.1 Common and distinct neural activation in two subcomponents of inhibitory control

4.2 Age-related changes in activation on subcomponents of inhibitory control

4.3 Implications

4.4 Limitations

5. Conclusions

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1