Local Functional Connectivity as a Parsimonious Explanation of the Main Frameworks for ADHD in Medication-Naïve Adults

Abstract

Objective:

Neuroimaging studies in children with ADHD indicate that their brain exhibits an atypical functional connectivity pattern characterized by increased local connectivity and decreased distant connectivity. We aim to evaluate if the local and distant distribution of functional connectivity is also altered in adult samples with ADHD who have never received medication before.

Methods:

We compared local and distant functional connectivity between 31 medication-naïve adults with ADHD and 31 healthy controls and tested whether this pattern was associated with symptoms severity scores.

Results:

ADHD sample showed increased local connectivity in the dACC and the SFG and decreased local connectivity in the PCC.

Conclusion:

Results parallel those obtained in children samples suggesting a deficient integration within the DMN and segregation between DMN, FPN, and VAN. These results are consistent with the three main frameworks that explain ADHD: the neurodevelopmental delay hypothesis, the DMN interference hypothesis and multi-network models.

Keywords

ADHD neurodevelopment resting state brain networks functional connectivity adults

Introduction

ADHD is one of the most common neurodevelopmental disorders. Its core symptoms include inappropriate developmental levels of inattention, hyperactivity/impulsivity, or a combination of these two symptom domains (APA, 2013). ADHD has an estimated prevalence of 9% in the school-age population (Visser et al., 2014). However, almost one-third of children with the disorder still fulfill DSM-V diagnostic criteria when they reach adulthood (Agnew-Blais et al., 2016; Biederman et al., 2010; Kooij et al., 2010). Compared to studies with children samples, studies of adults with ADHD are particularly scarce in scientific literature.

Currently, results from Magnetic Resonance Imaging (MRI) studies in children support three main neurobiological models of ADHD (Castellanos & Aoki, 2016): the neurodevelopmental delay hypothesis, the Default Mode Network (DMN) interference hypothesis, and the more recent multi-network models. The neurodevelopmental delay hypothesis (El-Sayed et al., 2003) postulates that ADHD is characterized by neurobiological features that resemble those of a less mature brain, which may remit with time or not (Shaw et al., 2006, 2007, 2010, 2013; Sripada et al., 2014). The DMN interference hypothesis (Sonuga-Barke & Castellanos, 2007) suggests that the brain of patients with ADHD does not adequately suppress this functional network during periods of active processing, and that this deficient suppression is related to the attentional lapses that characterize ADHD symptomatology (Castellanos et al., 2008; Clare Kelly et al., 2008; Gao et al., 2019; Sun et al., 2012). Finally, recent multi-network models propose that the disorder results from an atypical functional connectivity within and between several large-scale networks (Castellanos & Aoki, 2016; Castellanos & Proal, 2012), including basic sensorimotor (Cao et al., 2006; Carmona et al., 2015; Kessler et al., 2014; Marcos-Vidal et al., 2018; Tian et al., 2008), and higher-order cognitive circuits (Menon, 2011). While no one has proposed an underlying process able to encompass these models, we believe that their alterations could be related to abnormalities in local and distant brain functional connectivity patterns.

During development, functional connectivity (FC) shifts from being locally connected, that is, connected to anatomically close areas, to establish distant connections and form large-scale functional networks (Fair et al., 2007). Thus, by analyzing the patterns of local and distant functional connectivity at a whole-brain level we can test whether the brains of adult patients with ADHD show features resembling those of a typically less mature brain (increased local connectivity). This methodology also allows us to test whether alterations in local and distant connectivity are restricted to the DMN or also affect other networks. To date, local and distant FC patterns have only been explored in children with ADHD (Marcos-Vidal et al., 2018; Tomasi & Volkow, 2012). According to these studies, children with ADHD show signs of brain immaturity that affect mainly, but not exclusively, regions of the DMN. Specifically, they found increased local connectivity patterns in children with ADHD in areas pertaining to the DMN as well as to the fronto-parietal and ventral attentional networks (Anterior Cingulate Cortex and Superior Frontal Gyrus) and the limbic network (Orbitofrontal Cortex and Ventral Striatum). In adults with ADHD, local and distant FC patterns remain unexplored.

In this work, we aim to evaluate local and distant functional connectivity patterns in adults with ADHD by cross-sectionally comparing the local and distant connectivity values of a sample of 31 adults with ADHD with a sample of 31 healthy controls (HC). Importantly, all our ADHD subjects were medication-naïve, thus ensuring that group differences are not biased by the potential effects of pharmacological treatment. We also examined the correlation between connectivity values and clinical severity scores. Based on the results obtained in children studies, we expect to find increased local connectivity in areas that comprehend DMN, fronto-parietal, and ventral attentional networks.

Methods

Study Design and Participants

A total of 101 participants were evaluated in this study, 39 of them were discarded because the field of view did not cover the entire brain. Thus, a total of 62 adults were selected for the present study. The ADHD group consisted of 31 adults with combined ADHD who had never received medication for their condition, and the control group was formed by 31 participants (see Table 1). We ensured both sexes were well represented in both groups (17 women in the ADHD group and 15 women in the HC group). A specialized team of psychiatrists and psychologists from Vall d’Hebron Hospital in Barcelona (Spain) evaluated the ADHD sample to ensure they all met DSM-V criteria (American Psychiatric Association, 2013) for ADHD combined subtype. ADHD symptom severity was measured by means of the ADHD Rating Scale (DuPaul, 1991; DuPaul et al., 1998).

Table 1.

Demographic and Clinical Data of the ADHD and Control Samples.

Characteristic	ADHD (N = 31)	HC (N = 31)	Stat (df)	p-Value
Characteristic	Mean (SD)	Mean (SD)	Stat (df)	p-Value
Age (range 19–52)	35.3 (9.9)	32.5 (8)	t (60) = −1.25	.21
ADHD rating scale	32.45 (9.75)	–	–	–
Sex (number of women)	17	15	χ² (1) = 0.25	n.s.
Number scanned with replacement head coil	14	11	χ² (1) = 0.603	.43
Framewise displacement	0.094 (0.073)	0.065 (0.045)	t (60) = −1.85	.07

Note. Three controls did not complete the ADHD Rating Scale. Independent sample t-tests or chi-square were used for group comparisons. None of the comparisons render significant between-group differences. HC = healthy controls; SD = standard deviation; Stat = statistic; df = degrees of freedom.

Exclusion criteria included comorbidity with other psychiatric diseases or personality disorders, which was assessed by the Structured Clinical Interview for Axis I (SCID-I) (First et al., 1996) and Axis II disorders (First et al., 1997). Participants with substance abuse disorders (including tobacco and cannabis consumption within the last 6 months), and those with an estimated WAIS-III IQ (Wechsler, 1997) lower than 80 were also excluded. The study was approved by the Hospital de Vall d’Hebron Ethics Committee, and informed consent was obtained from all participants.

MRI Acquisition

A Philips Achieva 3T scanner was used to acquire the MRI images for the present study. T1-weighted images were acquired with a fast-spoiled gradient echo (FSPGR) sequence. Acquisition parameters were as follows: repetition time (TR) = 8.2 ms, echo time (TE) = 3.7 ms, flip angle (FA) = 88°, matrix dimensions = 256 × 256 × 180, voxel size = 0.94 × 0.94, and slice thickness = 1 mm with no gap. Resting state functional magnetic resonance imaging (fMRI) data were acquired using an echo-planar imaging (EPI)-T2* sequence, which included 116 time points, each lasting 2.655 seconds. Acquisition parameters were: TR = 3000 ms, TE = 35 ms, FA = 90°, matrix dimensions = 128 × 128, voxel size of 1.80 × 1.80 mm, slice thickness = 3.0 mm with a 1 mm gap. Participants were instructed to remain still and awake with their eyes open during the functional run.

Due to technical problems, a different radio frequency (RF) head coil (16 channels instead of 8 channels) was used for 25 out of the 62 of the participants when acquiring the MR images (see Table 1). This was considered in the analyses, although no significant differences were found in temporal contrast-to-noise ratio (Welvaert & Rosseel, 2013) were found between the samples of each head coil (Supplemental Table 2). Furthermore, as displayed in Supplemental Figure 2, the group differences map shows a similar trend when using the whole dataset or the 8-channel head coil sample.

MRI Processing

Preprocessing of fMRI data was performed with the software packages SPM12 (Welcome Department of Imaging Neuroscience, London, United Kingdom) and AFNI (Scientific and Statistical Computing Core, National Institute of Mental Health, Bethesda, MD). Preprocessing started by removing the first three volumes to allow for magnetic stabilization. Then, images were realigned to the mean image in order to correct for motion-related artifacts and despiked with the 3dDespike AFNI tool (c1 = 2.5, c2 = 4). Images were later normalized to the MNI152 stereotactic standard space for comparison purposes, and spatially smoothed with a Gaussian kernel of a full-width-half-maximum (FWHM) of 6 mm to increase the signal-to-noise ratio. Finally, all functional images were down-sampled to an isotropic 4 mm voxel size and nuisance variables (including the six rigid parameters obtained from the motion correction and mean white matter, mean cerebrospinal fluid, and mean whole-brain signals) were regressed out to remove confounding effects. None of the participants have a mean framewise displacement (FD) over 0.5 mm, as measured by the MCFLIRT tool (Jenkinson et al., 2002). See Table 1 for descriptive statistics per group.

Since in-scanner motion may have a substantial impact on functional connectivity analyses (Ciric et al., 2017; DiMartino et al., 2014; Power et al., 2012), a resting-state functional connectivity quality control (RSFC-QC) was additionally plotted in order to assess the effect of motion in functional connectivity as a function of node distance. Supplemental Figure 1 suggests that in-scanner motion did not alter the relationship between functional connectivity and node distance.

Local and Distant Functional Connectivity Analysis

The local and distant functional connectivity technique is a graph-theory-based method used on resting-state fMRI data (Sepulcre et al., 2010). It measures the degree of connectivity of each voxel with those surrounding it (local connectivity) and with those far from it (distant connectivity). The degree of connectivity of a given voxel is computed as the number of voxels functionally connected to that target voxel.

For the present study, we first obtained a connectivity matrix for each subject, which contained the Pearson correlation coefficient of the time series of every voxel with any other voxel in the gray matter mask. This matrix was binarized by substituting correlation values higher than 0.25 by ones and the rest by zeros (following the criteria described in the original work of Sepulcre et al., 2010). We did not take into consideration negative correlations since the preprocessing step of global signal regression can bias the distribution of connectivity values downwards, thus potentially introducing negative correlations that were not initially present in the data (Murphy et al., 2009; Van Dijk et al., 2010).

Local and distant functional connectivity values were calculated as the degree of functional connectivity of each voxel but with physical distance restrictions. Local connectivity maps were computed as the degree of connectivity of each voxel within its neighborhood, defined as the 28 × 28 × 28 mm³ cube surrounding it (Sepulcre et al., 2010). Distant connectivity maps were computed as the degree of connectivity of a voxel with those outside its neighborhood (i.e., outside the 28 × 28 × 28 mm³ cube).

For both functional connectivity maps, we adjusted each voxel’s degree of functional connectivity according to the total number of voxels to which it could be connected. This allowed us to correct for voxel position since voxels located on the borders of the brain have part of their surrounding cube outside the brain and have less potential local connectivity and, therefore, more potential distant connectivity. The corrected distant functional connectivity value ${\hat{d}}_{i}$ was calculated using the following formula:

{\hat{d}}_{i} = \frac{d_{i}}{D_{i}}

Where $d_{i}$ is the distant functional connectivity value of the $i^{t h}$ voxel, and $D_{i}$ is the number of voxels out of the $i^{t h}$ voxel’s cube that fall inside the brain mask, so that ${\hat{d}}_{i}$ varies from 0 to 1. Respectively, the corrected local functional connectivity value ${\hat{l}}_{i}$ was calculated using the following formula:

{\hat{l}}_{i} = \frac{l_{i}}{L_{i}}

Where $l_{i}$ is the local functional connectivity value of the $i^{t h}$ voxel, and $L_{i}$ is the number of voxels of the $i^{t h}$ voxel’s cube that fall inside the brain mask so that ${\hat{l}}_{i}$ varies from 0 to 1.

Characterizing Local and Distant Functional Connectivity in Adults

To examine local and distant connectivity patterns in adults with and without ADHD, and visually compare the results to local and distant adult’s maps obtained by Sepulcre et al. (2010), we transformed the mean group local and distant connectivity maps to group-specific Z-score maps. This transformation was performed only for visualization purposes. Subsequent analyses used the direct local and distant connectivity values.

Statistical Analyses

Group comparisons

Two General Linear Models were fitted, one for local and one for distant functional connectivity maps. These models included as covariates head coil, sex, individual mean FD (mean-centered to zero), and age (mean-centered to zero). For each model, specific contrasts were performed to test group differences. Analyses were performed with SPM12 software (http://www.fil.ion.ucl.ac.uk/spm/software/spm12; version 95 of September 12, 2016).

Correlations with clinical symptoms

Regression analyses were performed to test the associations between local and distant functional connectivity and the severity of ADHD symptoms. Two General Linear Models were fitted, one for local and one for distant functional connectivity differences, which included as regressors the score on the ADHD clinical scale (ADHD score), head coil, sex, individual mean FD (mean-centered to zero), and age (mean-centered to zero). Then, specific contrasts were performed to test the effect of ADHD score on local and distant functional connectivity.

Multiple comparisons correction

Statistical maps were thresholded with a p < .05 and a cluster size of at least 112 contiguous voxels, which corresponds to a cluster-wise Family Wise Error (FWE) corrected p-value of .05 (pFWE < .05). The FWE correction was estimated with the AFNI program 3dClustSim (Forman et al., 1995; accessed September 11, 2018), which performs a MonteCarlo simulation based on the image size, the search volume (in this case the gray matter mask) and the spatial correlation of the image.

Functional connectivity differences in areas with altered local connectivity

As a post-hoc analysis, we assessed whether the alterations found in local functional connectivity co-occur with an alteration of their functional connectivity patterns. For each subject, we computed the mean blood-oxygen-level-dependent (BOLD) signal of those voxels that resulted significant in the group comparisons (p < .05 FWE corrected). Then, mean BOLD signal was correlated with that of each voxel in the brain, obtaining one connectivity map per subject. Finally, we fitted one linear model per voxel with each subject’s connectivity value as dependent variable and group, sex, head coil, FD (mean-centered to zero), and age (mean-centered to zero) as independent variables. Then, specific contrasts were used to test group differences.

Cortical and Network Visualization

For visualization purposes, we calculated the percentage of voxels that pertained to each of the seven cortical large-scale resting-state functional networks described by Thomas Yeo et al. (2011).

Surface projections of local and distant maps were performed via a Matlab in-house script that uses nearest neighbor (for the categorical classification in the seven cortical large-scale resting-state functional networks (Thomas Yeo et al., 2011) or linear interpolation (for the quantitative maps) and the surface normals to project cortical voxels onto the surface. The surfaces employed were the left and right “Q1-Q6_R440.#.midthickness.164k_fs_LR.surf.gii” of the software Connectome Workbench (Marcus et al., 2011).

Results

Characterization

In Figure 1, maps of z-score values are projected on brain surfaces with saturation values of −2.5 (minimum) and 2.5 (maximum).

Figure 1.

One-sample characterization of local and distant functional connectivity in adults with ADHD and healthy controls.

Between-Group Differences

As displayed in Figure 2 and Table 2, adults with ADHD showed increased local functional connectivity in frontal regions, including the Superior Frontal Gyrus (SFG) and the dorsal Anterior Cingulate Cortex (dACC; max T. 3.422) compared with controls. Regions with increased local functional connectivity mainly overlap with the frontoparietal (36%), ventral attentional (31%), and default mode (24%) networks. For further details of the percentage of overlap of the results with each network of Yeo’s parcellation, see Supplemental Table 1 (Supplemental Appendix).

Figure 2.

(a) Differences in local functional connectivity between adults with ADHD and healthy controls, (b) group differences in local functional connectivity in terms of large-scale functional networks (Thomas Yeo et al., 2011). Each color represents a different functional network.

Table 2.

Results of the Local and Distant Functional Connectivity Analyses.

Between-group comparisons	Peak MNI coordinates			Number of voxels	Highest T-score	Cluster-level p-value
Between-group comparisons	x	y	z	Number of voxels	Highest T-score	Cluster-level p-value
Local functional connectivity
ADHD > HC
Cluster 1 (Bil. Dorsal Anterior Cingulate Cortex and R. Superior Frontal Gyrus)	11	38	40	363	3.4217	p < .001
HC > ADHD
Cluster 1 (Bil. Precuneus and Bil. Posterior Cingulate Cortex)	−8	−51	22	139	−3.8293	p < .02

Note. R = right; L = left; Bil = bilateral; HC = healthy controls; MNI = Montreal Neurological Institute.

Regarding the opposite contrast, adults with ADHD showed decreased local functional connectivity in an area that included part of the Precuneus and the Posterior Cingulate Cortex (now ahead PCC; max T. −3.829). In terms of large-scale functional networks, the regions that exhibited decreased local functional connectivity pertain mainly to the default mode network (80%) and, to a lesser extent, to the visual (8.6%) and frontoparietal (10%) networks. Finally concerning the distant functional connectivity analysis, no clusters survived the threshold of p < .05 FWE.

Symptom Severity Correlations

Figure 3 shows the results of the regression analyses between the degree of local functional connectivity and the ADHD symptom severity scale. Results revealed four clusters whose local connectivity negatively correlated significantly with the severity of ADHD symptoms. That is, higher scores on the ADHD clinical scale were associated with decreased local functional connectivity in several occipital, parietal, and frontal lobe regions. Peak values of the four significant clusters were located in the medial PreFrontal Cortex mPFC (r = −0.4733), the PCC (R = −0.4720), the left occipital cortex (R = −0.4691), and the right occipital cortex (R = −0.4897) (Table 3). When examined in terms of large-scale functional networks, these regions pertained mainly to the visual (44%) and default mode (41%) networks, and to a lesser extent, to the attentional (5.8%), frontoparietal (4.7%), and limbic (3.5%) networks. No significant correlations were found between ADHD score and distant functional connectivity.

Figure 3.

Correlations with clinical severity: (a) Results of the regression analyses between the degree of local functional connectivity and ADHD clinical scores. Peak-values of the four significant clusters are located in the mPFC (11, 58, −12; R = −0.4733), the PCC (4, −53, 50; R = −0.4720), the left Occipital Cortex (−24, −72,0; R = −0.4691), and the right Occipital Cortex (15, −84, 0; R = −0.4897), (b) results of the regression analyses in terms of large-scale functional networks (Thomas Yeo et al., 2011). Each color represents a different functional network, and (c) plots of the regression analysis using the mean local connectivity value of each cluster as dependent variable and the ADHD rating scale as independent variable. The effect of the covariables of no interest mentioned in the statistical analysis section (head coil, sex, age, and FD) was removed from local connectivity measure before plotting the regression.

Table 3.

Correlation between Local and Distant Functional Connectivity Measures and ADHD Symptoms Severity as Assessed by the ADHD Rating Scale.

ADHD rating scale	Peak MNI coordinates			Number of voxels	Highest R	Cluster-level p-value
ADHD rating scale	x	y	z	Number of voxels	Highest R	Cluster-level p-value
Local functional connectivity
Negative associations
Cluster 1 (L. Occipital Cortex)	−24	−72	0	169	−0.4691	p < .01
Cluster 2 (Bil. medial PreFrontal Cortex)	11	58	−12	175	−0.4733	p < .005
Cluster 3 (R. Occipital Cortex)	15	−84	0	133	−0.4897	p < .05
Cluster 4 (Bil. Precuneus and Bil. Posterior Cingulate Cortex)	4	−53	50	232	−0.4720	p < .002

Note. R = right; L = left; Bil = bilateral; MNI = Montreal Neurological Institute.

Table 4.

Results of the Seed-Based Functional Connectivity Analyses.

Connectivity differences	Peak MNI coordinates			Number of voxels	Highest T	Cluster-level p-value
Connectivity differences	x	y	z	Number of voxels	Highest T	Cluster-level p-value
SFG/dACC cluster (ADHD > HC)
Cluster 1: precentral	−6	−34	64	313	4.3013	p < .001
PCC cluster (HC > ADHD)
Cluster 2: medial prefrontal cortex	−6	62	0	378	4.4693	p < .001
Cluster 3: precuneus	10	−54	8	126	4.4667	p < .001

Note. SFG = superior frontal gyrus; dACC = dorsal anterior cingulate cortex; PCC = posterior cingulate cortex; ADHD = Attention-Deficit and Hyperactivity Disorder; HC = healthy controls; MNI = Montreal Neurological Institute.

Functional Connectivity Differences in Areas with Altered Local Connectivity

As a post-hoc analysis, we tested whether there were group differences in functional connectivity between the cluster in the SFG/dACC (ADHD > HC) and the rest of the brain; and between the cluster in the precuneus/PCC (HC > ADHD) the rest of the brain. As observed in Figure 4, we found greater functional connectivity in adults with ADHD between the SFG/dACC cluster and the bilateral sensorimotor cortices (T = 4.3013). Regarding the precuneus/PCC cluster, adults with ADHD present a decreased level of connectivity within the precuneus (T = 4.4667) and between the PCC and the bilateral medial Prefrontal Cortex (mPFC) (T = 4.4693), both regions pertaining to the DMN (85.91%). No other significant group differences were detected.

Figure 4.

(a) Functional connectivity group differences taking the PCC cluster as seed. Functional connectivity group differences (PCC cluster as seed) in terms of large-scale functional networks (Thomas Yeo et al., 2011). Each color represents a different functional network. (b) Functional connectivity group differences taking the SFG/dACC cluster as seed. Functional connectivity group differences (SFG/dACC cluster as seed) in terms of large-scale functional networks (Thomas Yeo et al., 2011). Each color represents a different functional network.

Discussion

In this study, we investigated the local and distant connectivity patterns of 31 medication-naïve adults with ADHD by cross-sectionally comparing them to 31 healthy adults. Mimicking the results observed in children with ADHD (Marcos-Vidal et al., 2018), we found that adults showed increased local connectivity in an area comprising part of the dACC and the SFG. In addition, adults with ADHD exhibited decreased local functional connectivity in the PCC, which is one of the nodes of the DMN. Furthermore, the lower the local functional connectivity in the PCC and other areas of the DMN, the more severe the clinical symptoms as assessed by the ADHD rating scale. As will be explained along the discussion, our results provide an integrative explanation for the three main theoretical frameworks on ADHD: the DMN interference hypothesis, the neurodevelopmental delay hypothesis, and multi-network models.

Increased Local Connectivity in the dACC/SFG

Adults with ADHD show increased local connectivity in a cluster that encompasses the dorsal part of the ACC and part of the SFG. Alterations in the dACC have been extensively reported in both children and adults with ADHD (Makris et al., 2010; Sun et al., 2012). The dACC is, together with the anterior insula, one of the key nodes of the Ventral Attentional Network (VAN), also known as salience network. The interplay between the VAN, the Fronto-Parietal Network (FPN), and DMN has been proposed as a critical feature in ADHD (Carmona et al., 2015; Menon, 2011). Specifically, it has been suggested that ADHD symptoms related to mind wandering might result from an inability of the VAN to disengage the DMN, leading to continuous intrusions of self-referential thinking or mind wandering during executive tasks (Fox et al., 2015; Mason et al., 2007; McKiernan et al., 2006).

The SFG has been less studied in the context of ADHD. This region is mainly involved in executive (dorsolateral part) and default mode (anteromedial part) processes (Li et al., 2013). A recent study indicates that SFG shows decreased degree centrality in children with ADHD (Jiang et al., 2019). Moreover, it is important to note that the same cluster encompasses both the dACC and the SFG, reflecting a higher level of connectivity between VAN, FPN, and DMN. Under this paradigm, our findings dovetail with the hypothesis that in ADHD the ability of VAN to modulate the activation of FPN and DMN might be altered.

Our findings also agree with the neurodevelopmental delay hypothesis. The dACC/SFG cluster lies in the boundaries separating the FPN, the VAN, and the DMN networks, thus, increased connectivity within this region might represent a sign of atypical segregation. Segregation is a developmental process through which functional connectivity between anatomically close regions is reduced or even becomes negative. Functional segregation, together with the integration between anatomically distant regions, underlies the typical developmental pattern of the large-scale functional networks (Fair et al., 2007, 2009). Reduced between-network segregation has been previously reported not only in children with ADHD (Mills et al., 2018), but also in adults with the disorder (Fan et al., 2019). Our study reveals that the brain of patients with ADHD shows features that resemble those of a more immature brain, and suggests that, at least when the disorder has never been medicated and persists into adulthood, those features do not remit with age.

Reduced Local Connectivity in the PCC

We also found decreased local functional connectivity within the PCC in adults with ADHD. The PCC is one of the core nodes of the DMN (Greicius et al., 2009; Leech et al., 2011; Margulies et al., 2009), and one of the principal hubs of the functional whole-brain network (Buckner et al., 2009). Thus, its integration is essential during neurodevelopment for keeping the typical small-world organization of the brain and for maintaining efficient communication among different functional systems (Buckner et al., 2009). This is the first study to show that local functional connectivity within the PCC is also affected in adults with ADHD.

Neuroimaging studies have consistently reported alterations in the PCC in patients with ADHD. For instance, functional studies have found overactivation of the PCC during attentional tasks, which has been related to attentional lapses (Fassbender et al., 2009). Other studies also revealed functional connectivity reductions between two of the main default mode nodes, the PCC and the mPFC (Castellanos et al., 2008; Fair et al., 2010; Sripada et al., 2014), as well as functional connectivity increases between the PCC and nodes of other networks, such as the ACC and the Anterior Insula (Castellanos et al., 2008; Sripada et al., 2014; Sun et al., 2012). These studies, together with our results of decreased functional coherence within the precuneus, point toward an abnormal large-scale functional network segregation and integration that can be also explained in the context of the DMN interference hypothesis.

Furthermore, we found that the lower the degree of local connectivity within DMN regions, including the PCC, the higher the ADHD symptom severity score. This association goes in line with that found by Oldehinkel et al., 2016 who reported that lower connectivity within the DMN was associated with the severity of inattention.

Post-hoc Analyses

As post-hoc analyses, we tested whether there are group differences in functional connectivity when taking as seeds the clusters obtained from the main analyses, that is, when taking as seeds the dACC/SFG and the PCC.

On the one hand, we found that adults with ADHD showed increased functional connectivity between the dACC/SFG and the motor cortex. The dACC sends strong motor output, and has direct connections to the spinal cord and oculomotor areas, thus giving it direct control over motor action (Fries, 1984). Alterations in the motor cortices have been extensively reported in ADHD using anatomical (Mostofsky et al., 2002), diffusion tensor imaging (Hamilton et al., 2008; Langevin et al., 2014), task-based (Mostofsky et al., 2006), and resting-state (Carmona et al., 2015) fMRI data. According to previous data they might be related mainly to motor symptoms (Gilbert et al., 2011; Oldehinkel et al., 2016) although this statement cannot be directly extracted from our data.

On the other hand, when taking the PCC as a seed we found decreased functional connectivity between that cluster and the mPFC in patients with ADHD. As previously stated, the mPFC and the PCC are the two principal hubs of the DMN, and thus, show a high level of functional connectivity in neurotypical populations (Andrews-Hanna et al., 2010). Our results are consistent with previous reports that show reduced between-region integration within the DMN (Castellanos et al., 2008; Mattfeld et al., 2014; Sripada et al., 2014). In fact, they extend the previous literature by suggesting that the decreased inter-region connectivity between the PCC and the mPFC might be related to decreased integration, or decreased local connectivity, within the PCC. According to that, the reduced local connectivity within the PCC offers a parsimonious explanation of previous reports indicating less integration within the DMN, as well as poor communication between the DMN and the rest of the brain networks. It would be interesting that future studies test whether atypical within and between network connectivity can be explained in terms of atypical local connectivity, as a similar overlap of within and between regions connectivity has also been observed in many other psychiatric disorders (Liu et al., 2008; Long et al., 2018; Tang et al., 2018; Xu et al., 2019).

Relation with the Results in Children

To date, this is the first study that explores local and distant functional connectivity in adults with ADHD. Our results are similar to those obtained in children (Marcos-Vidal et al., 2018). Specifically, both samples exhibited greater local connectivity in areas of the SFG and the dACC. This suggests a persistent lack of segregation between DMN, VAN, and FPN across development in those areas. However, children with ADHD did not show decreased local connectivity in the PCC. The PCC is thought to be one of the most important regions in ADHD etiology (Castellanos & Proal, 2012; Gao et al., 2019) and its activation and connectivity normalizes with methylphenidate medication. Thus, the different results in children and adults with ADHD concerning the PCC could be explained by the medication condition of the children, as some of them were medicated/has been previously medicated (Pereira & de Castro-Manglano, 2017; Picon et al., 2020; Querne et al., 2017 ). While the child sample contained both medicated and medication-naïve children, all the adults have never received medication. However, all these comparisons must be taken with care, since we are making inferences in developmental trajectories from cross-sectional data.

Limitations and Conclusion

One of the main limitations of this study is the relatively small sample size. Even though it is challenging to recruit medication-naïve adults with ADHD, the fact is that reduced sample sizes imply low power of statistical analyses. Another limitation of this study is the cross-sectional nature of the data that prevent us from directly testing the neurodevelopmental delay hypothesis. Another limitation is the usage of different head coils for some of the participants. Although we have controlled the effect of the head coil in the statistical analysis, it is difficult to know exactly its effects on results.

In summary, we compared the local and distant functional connectivity patterns between adults with ADHD and healthy adults. We found that adults with ADHD show increased local connectivity within the dACC and the SFG, and decreased local functional connectivity within the PCC. We also found that PCC’s local connectivity is correlated with clinical symptomatology, and that this region presents a decreased level of functional connectivity with the mPFC. These findings reflect a level of integration and segregation proper of a more immature brain, and that affects the regions and networks relevant for the DMN interference hypothesis. Moreover, secondary analyses also show alterations of sensory networks as well, specifically visual and sensorimotor cortices, highlighting the importance of the interplay between basic sensory-motor and higher-order cognitive circuits.

Supplemental Material

sj-pdf-1-jad-10.1177_10870547211031998 – Supplemental material for Local Functional Connectivity as a Parsimonious Explanation of the Main Frameworks for ADHD in Medication-Naïve Adults

Supplemental material, sj-pdf-1-jad-10.1177_10870547211031998 for Local Functional Connectivity as a Parsimonious Explanation of the Main Frameworks for ADHD in Medication-Naïve Adults by Luis Marcos-Vidal, Magdalena Martínez-García, Daniel Martín-de Blas, Francisco J. Navas-Sánchez, Clara Pretus, Josep Antoni Ramos-Quiroga, Vanesa Richarte, Óscar Vilarroya, Jorge Sepulcre, Manuel Desco and Susanna Carmona in Journal of Attention Disorders

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This resesrch is funded by Instituto de Salud Carlos III, Ministerio de Ciencia e Innovación and “la Caixa” Foundation.

ORCID iD

Luis Marcos-Vidal

Supplemental Material

Supplemental material for this article is available online.

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