Received: 31 January 2019 Revised: 26 June 2019 Accepted: 1 July 2019 DOI: 10.1002/hbm.24727 RESEARCH ARTICLE Stepwise functional connectivity reveals altered sensory-multimodal integration in medication-naïve adults with attention deficit hyperactivity disorder Clara Pretus1,2* | Luis Marcos-Vidal3,12* | Magdalena Martínez-García3 | Marisol Picado1 | Josep Antoni Ramos-Quiroga4,5,6 | Vanesa Richarte4,5,6 | Francisco X. Castellanos7,8 | Jorge Sepulcre9,10 | Manuel Desco3,11,12,13 | Óscar Vilarroya1,2† | Susanna Carmona3,11,12† 1 Departament de Psiquiatria i Medicina Legal, Universitat Autònoma de Barcelona, Barcelona, Spain 2 Fundació Institut Mar d'Investigacions Mèdiques, Barcelona, Spain 3 Unidad de Medicina y Cirugía Experimental, Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain 4 Psychiatry Department, Vall d'Hebron University Hospital, Barcelona, Spain 5 Group of Psychiatry, Mental Health and Addiction, Vall d'Hebron Research Institute, Barcelona, Spain 6 Biomedical Network Research Centre on Mental Health (CIBERSAM), Health Institute Carlos III, Barcelona, Spain 7 Center for Neurodevelopmental Disorders, The Child Study Center at NYU Langone Medical Center, New York, New York 8 Division of Child and Adolescent Psychiatric Research, Nathan Kline Institute for Psychiatric Research, Orangeburg, New York 9 Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, Massachusetts 10 Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 11 Centro de Investigación Biomédica en Red de Salud Mental, Madrid, Spain 12 Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III, Madrid, Spain 13 Fundación Centro Nacional de Investigaciones Cardiovasculares Carlos III, Madrid, Spain Correspondence Jorge Sepulcre, Department of Radiology, Abstract Athinoula A. Martinos Center for Biomedical Neuroimaging studies indicate that children with attention-deficit/hyperactivity Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, disorder (ADHD) present alterations in several functional networks of the sensation- Massachusetts to-cognition spectrum. These alterations include functional overconnectivity within Email: sepulcre@nmr.mgh.harvard.edu sensory regions and underconnectivity between sensory regions and neural hubs Manuel Desco, Unidad de Medicina y Cirugía supporting higher order cognitive functions. Today, it is unknown whether this same Experimental, Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain pattern of alterations persists in adult patients with ADHD who had never been med- Email: desco@hggm.es icated for their condition. The aim of the present study was to assess whether Funding information medication-naïve adults with ADHD presented alterations in functional networks of Instituto de Salud Carlos III, Grant/Award the sensation-to-cognition spectrum. Thirty-one medication-naïve adults with ADHD Number: CP16/00096; Ministerio de Economía y Competitividad, Grant/Award and twenty-two healthy adults underwent resting-state functional magnetic reso- Number: SAF2012-32362 nance imaging (rs-fMRI). Stepwise functional connectivity (SFC) was used to charac- terize the pattern of functional connectivity between sensory seed regions and the *Clara Pretus and Luis Marcos-Vidal shared first authorship † Óscar Vilarroya and Susana Carmona shared last authorship Hum Brain Mapp. 2019;40:4645–4656. wileyonlinelibrary.com/journal/hbm © 2019 Wiley Periodicals, Inc. 4645 4646 PRETUS ET AL. rest of the brain at direct, short, intermediate, and long functional connectivity dis- tances, thus covering the continuum from the sensory input to the neural hubs supporting higher order cognitive functions. As compared to controls, adults with ADHD presented increased SFC degree within primary sensory regions and decreased SFC degree between sensory seeds and higher order integration nodes. In addition, they exhibited decreased connectivity degree between sensory seeds and regions of the default-mode network. Consistently, the higher the score in clinical severity scales the lower connectivity degree between seed regions and the default mode network. KEYWORDS ADHD, adult ADHD, default mode network, resting-state fMRI, stepwise functional connectivity 1 | I N T RO D UC T I O N The aim of the present study was to test whether medication-naïve adult patients with ADHD show impaired connectivity between primary Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental sensory and higher order cognitive circuits. For this purpose, we applied disorder characterized by excessive levels of inattention, impulsivity, and a SFC protocol aimed to detect which parts of the brain are connected hyperactivity (American Psychiatric Association, 2013). Approximately with primary sensory regions not only through direct paths (i.e., one- 35% of children with ADHD still fulfill DSM-IV diagnostic criteria for step functional distance, which would be the standard functional con- ADHD in adult life (Biederman, Petty, Evans, Small, & Faraone, 2010). nectivity analysis), but also through indirect connections that involve a Neuroimaging research on ADHD has typically focused on net- varying number of “link-step” distances or “relay stations.” Hence, in works supporting higher order functions, with little attention to find- contrast to other standard methods such as functional connectivity ings in primary sensory regions. For instance, several studies report a strength evaluation, SFC allows measuring functional connectivity weaker segregation between cognitive control networks and default between any pair of brain locations that are connected by any finite mode network both in children and adults with ADHD (Cao et al., number of relay stations. 2009; Castellanos et al., 2008; Hoekzema et al., 2014; Lin et al., 2018; Based on the assumption that adult ADHD may share similarities Sun et al., 2012), although only a few studies highlight the need to with childhood ADHD in terms of functional connectivity alterations, clarify how sensory regions interact with higher order association net- we predicted that our sample of medication-naïve adults with ADHD works in ADHD (Castellanos and Proal, 2012; Lin et al., 2018). In would show increased SFC within sensory regions, as well as decreased response to this, recently developed analysis tools such as the step- connectivity between sensory seeds and networks supporting executive wise functional connectivity (SFC) approach (Sepulcre, Sabuncu, Yeo, functions, and increased connectivity between sensory seeds and key Liu, & Johnson, 2012) allow evaluating the presence of abnormalities nodes of the DMN. in multilevel information processing systems from early sensory to higher order cognitive circuits in the brain. 2 | METHODS Making use of an SFC protocol, Carmona et al. (2015) provided evidence that the information flow between primary sensory cortices 2.1 | Participants and higher order association nodes might be disrupted in children with ADHD. Compared with controls, children with ADHD presented A total of 31 adults with combined ADHD and 22 healthy controls increased interconnectivity within primary sensory cortices at initial were recruited (see demographics in Table 1). We ensured both sexes steps of the sensation to cognition continuum (Carmona et al., 2015). were well represented in both groups (12 women in the ADHD group At the final steps of the sensation to cognition continuum, children and 16 women in the HC group). The ADHD patients were selected presented decreased SFC degree with executive processing areas and by a specialized team of psychiatrists and psychologists from Vall increased SFC degree with DMN areas. These studies indicate atypi- d'Hebron Hospital in Barcelona (Spain), where they were evaluated. All cal connectivity transitions between sensory and higher order large- patients met DSM-V criteria (American Psychiatric Association, 2013) scale functional networks, thus potentially compromising the flow of for ADHD combined subtype and were medication naïve. information across the sensation-to-cognition continuum. However, Standard ADHD scales were administered to both groups, includ- whether this pattern is also present in the adult form of the disorder ing the Conners Adult ADHD Rating Scale (CAARS) (Conners, is still unknown. Sitarenios, & Parker, 1998), the Wender Utah Rating Scale (WURS) PRETUS ET AL. 4647 T A B L E 1 Demographic and clinical data of the ADHD and control samples. Three controls did not complete the attention- deficit/hyperactivity disorder (ADHD) rating scale. Independent sample t-tests or chi-square were used to compare groups ADHD (N = 31) Controls (N = 22) Characteristic Mean (SD) Mean (SD) Stat (df ) p-value Age (range 19 to 52) 35.4 (9.9) 30.4 (5.8) t(51) = −2.11 .040 ADHD rating scale 32.3 (9.8) 6.0 (5.8) t(48) = −11.89 < .001 Sex (number of women) 16 12 χ2(1) = 0 .044 n.s. Number scanned with replacement headcoil 14 6 χ2 (1) = 1.75 n.s. Framewise displacement .054 (.036) .036 (.030) t(51) = −1.92 .061 (Ward & Wender, 1993), and the ADHD Rating Scale (DuPaul, Power, whole brain intensity signals) were regressed out to minimize the Anastopoulos, & Reid, 1998). All ADHD scores were significantly effects of movement. higher in the ADHD sample (see demographic data in Table 1). Given the impact of in-scanner motion on functional connectivity Exclusion criteria included comorbidity with other psychiatric dis- analyses (Ciric et al., 2017; Di Martino et al., 2014; Power et al., eases or personality disorders, assessed by the structured Clinical 2014), participants with a mean framewise displacement (FD) over Interview for Axis I (SCID-I) (First, Spitzer, Gibbon, & Williams, 1997) 0.2 mm as measured by the MCFLIRT tool (Jenkinson, Bannister, and Axis II disorders (First, Spitzer, Gibbon, Williams, & Benjamin, Brady, & Smith, 2002) were discarded. Additionally, we plotted a rest- 1994). Participants with substance abuse disorder, including those ing state functional connectivity quality control index (RSFC-QC; who consumed tobacco and cannabis within the last 6 months, were Power et al., 2014) to assess the effect of motion in functional con- also excluded. Participants with an estimated WAIS-III IQ (Wechsler, nectivity as a function of node distance. The data quality control 1997) lower than 80 were excluded. The study was approved by the showed no relationship between functional connectivity estimates Hospital de Vall d'Hebron Ethics Committee, and informed consent was and node distance, thus pointing to a reduced effect of head motion obtained from all participants before taking part in the study. artifacts in our data (Figure S1). 2.2 | fMRI image acquisition and preprocessing 2.3 | SFC analysis Images were acquired using a Philips Achieva 3T scanner. T1-weighted The SFC analysis allows computing the number of functional paths images were obtained using a FSPGR sequence (TR: 8.2 ms, TE: 3.7 ms, between previously defined seed regions and every other voxel in the FA: 88, voxel size: 0.94 × 0.94, slice thickness: 1.00 mm, gap: 0 mm, brain at successive numbers of relay stations or “link-step” distances. matrix size: 256 × 256 × 180). An EPI-T2* sequence was used to obtain Intermediate voxels work as relay stations or “link-steps” that range the resting-state functional volumes in a single run that lasted between 0 (direct, one link-step connection) and 6 (seven link-step 5.8 min (116 time points, TR: 3000 ms, TE: 35 ms, FA: 90, in-plane connections) before stabilizing (Sepulcre et al., 2012) (see Figure 2). voxel size: 1.80 × 1.80 mm, slice thickness: 3.0 mm, gap: 1.0 mm, Based on the number of relay stations, the degree of functional con- matrix size: 128 × 128). Due to a technical problem, 11 participants nectivity can be classified as direct (one link-step, and thus zero relay (evenly distributed between groups; χ 2 = 1.753; p = .186) were stations), short (two and three link-steps), medium (four and five link- scanned using a different radiofrequency head coil (16 channels steps), and long (six and seven link-steps). instead of 8 channels), which was considered in the analyses. Partic- For each processed brain, we computed the whole-brain connec- ipants were instructed to remain still and awake with their eyes tivity matrix by calculating the Pearson's correlation coefficient R for open during the functional run. each pair of voxels. From this point onwards, only positive correla- Functional MRI data were preprocessed with the software pack- tions were considered given the challenge of interpreting negative ages SPM12 (Welcome Department of Imaging Neuroscience, correlations after global signal regression in functional connectivity London, UK) and AFNI (Scientific and Statistical Computing Core, studies (Murphy, Birn, Handwerker, Jones, & Bandettini, 2009; Van National Institute of Mental Health, Bethesda, MD). After removing Dijk et al., 2010). Correlation matrices were then filtered to contain the first three volumes, functional images were realigned to the mean only correlations surviving false discovery rate (FDR) correction image to correct for motion-related artifacts, despiked with (q < 0.001). The resulting matrix was then binarized. As a result, we 3dDespike AFNI tool (c1 = 2.5, c2 = 4), normalized to MNI standard obtained an unweighted “one link-step” matrix for each subject con- space, and spatially smoothed with a 6-mm full-width-at-half- taining 1's for each pair of voxels whose signals were significantly cor- maximum Gaussian kernel. All functional images were downsampled related and zeros otherwise. to 4-mm voxels to facilitate computational calculations (Sepulcre In parallel, a set of three masks including three bilateral sensory et al., 2012). Finally, nuisance covariates (six rigid body realignment seed regions was designed, each encompassing eight voxels (each parameters, mean white matter, mean cerebrospinal fluid, and mean voxel was 4 mm isotropic) forming a cube. Following Sepulcre et al. 4648 PRETUS ET AL. (2012), the MNI coordinates for the most anterior, lateral, and supe- 2.5 | Cortical and network visualization rior voxel of each cube was [8, −76, 10] in the primary visual cortex, To facilitate interpretation of results in the context of large-scale [56, −12, 10] in the auditory cortex, and [0, −28, 66] in the somato- functional networks, the percentage of significant voxels that over- sensory cortex. To assess the degree of combined SFC of all sensory lapped with each of the seven cortical resting-state functional net- seeds irrespective of modality, a fourth mask was built combining works described by Yeo et al. (2011) was calculated for each analysis information from all three primary sensory regions. using MATLAB 2019a. Each n-step map encoded the number of n-step connections (SFC Surface projections of SFC maps were performed via a Matlab in- values) between every voxel in the brain and the voxels within the mask house script that uses nearest neighbor (for Yeo's atlas) or linear (for including the three bilateral seed regions. At each link step, SFC maps the quantitative maps) interpolation and the surface normals to pro- were standardized to Z-scores by subtracting the mean and dividing by ject cortical voxels onto the surface. The surfaces employed were the its SD to yield SFC values. Henceforth, we refer to these Z-score values left and right “Q1-Q6_R440.#.midthickness.164k_fs_LR.surf.gii” of the as the SFC values. A more detailed description of the SFC method can software Connectome Workbench (Marcus et al., 2011). To avoid be found in Sepulcre et al. (2012) and Sepulcre (2014). redundancy, we only present stepwise connectivity profiles of the left hemisphere with uneven step numbers in the main document. 2.4 | Statistical analysis All the statistical analyses were performed using SPM12. Groups were 3 | RESULTS homogeneous for mean FD, head coil, sex, and IQ but differed in age (t[51] = −2.11; p = .04). 3.1 | SFC maps in patients with ADHD and healthy For each of the seven SFC maps, general linear models were fit for controls each group separately. These models included age (mean centered to The combined SFC maps for adult patients with ADHD and healthy con- zero), FD (mean centered to zero), and head coil as covariates of no inter- trols are shown in Figure 1a,b, respectively. As expected, at short link- est. Statistical inference was performed over the intercept of the models step distances (one and three link-step maps), both groups exhibited to identify SFC values significantly greater than zero. Since this analysis functional connectivity between the sensory seeds and primary sensory was performed for exploratory purposes only, we displayed clusters of at regions. At longer link-step distances (five and seven link-step maps), least 10 contiguous voxels surviving an uncorrected p < .01. functional connectivity between sensory seed regions and frontoparietal General linear models were also fit to compare the seven SFC areas was established in both groups. In turn, healthy controls showed maps between groups. These models included group as a variable of connectivity with medial frontal areas and the precuneus at longer link- interest and age, sex, FD, and head coil as variables of no interest (age step distances, which was not observed in the ADHD group. and FD were mean centered to zero). Even though FD, sex, and head coil did not differ significantly between groups, they were included as a precaution. For each model, we tested the effect of group through a 3.2 | Between-group differences t-test on the value of its estimate. Between group comparisons are shown in Table 2 and map projec- In addition to group differences, we tested the association tions of between-group differences are presented in Figure 2a. All between symptom severity—ADHD Rating Scale—and functional con- results were corrected for multiple comparisons (see Methods, Statis- nectivity profiles in each step within the group of ADHD patients. In tical analysis). this correlation analysis, we included head coil, FD, sex, and age as The analysis revealed functional connectivity differences starting nuisance covariates. from the three-step connectivity maps, with ADHD patients exhibiting As supplementary analyses, we explored if group differences were increased seed region connectivity with the left calcarine sulcus com- consistent across gender (see Figure 3) and across the three main sen- pared to controls. These functional connectivity differences were sory modalities (see Figure 4). In addition, we measured the predictive maintained until the seven link-step maps, which exhibited between- power of the group differences using the software PRoNTo (Schrouff group differences that peaked in the right lingual gyrus. et al., 2013) (see Figure 5 and Table S1). Patients with ADHD showed decreased functional connectivity in the To correct for multiple comparisons, we used a Monte-Carlo simu- five link-step distance map in the left medial orbitofrontal gyrus compared lation implemented in the AFNI 3dClustSim function (Forman et al., to controls. These differences, however, faded in the seven link-step map. 1995; accessed July 18, 2018). This method was used to obtain an Network plots showed that the functional connectivity decreases experiment Family Wise Error (FWE) corrected type 1 error probabil- observed in controls versus ADHD patients in five-step maps largely ity of 0.05 (αFWE = .05). To achieve it, we needed a map FWE overlapped with the DMN (77.84%) and, to a lesser extent, the corrected type 1 error probability of .0071 (because we have 7 maps frontoparietal network (13.51%) and limbic circuits (8.65%, see Figure 2b). and 0.05/7 = 0.0071). For this purpose, we thresholded the statistical Results from the supplementary analysis indicated that group dif- maps with a minimum cluster size of 174 contiguous 4 mm3voxels sur- ferences were consistent across gender and sensory modalities (see viving the uncorrected p < .05. Figures 3 and 4). They also indicated a predictive power of the main PRETUS ET AL. 4649 F I G U R E 1 Surface projections of the one-sample t-test results of uneven link-step distances for (a) the adult sample of ADHD patients and (b) the control sample of healthy adults. Each image represents Cohen's D ranging from the value that corresponds with a p < .01 (.6 for the control sample and .47 for the ADHD sample) and a value of 1. Cohen's D effect sizes greater than 1 are collapsed to 1. Left hemispheres are displayed. Abbreviation: ADHD, attention-deficit/hyperactivity disorder [Color figure can be viewed at wileyonlinelibrary.com] T A B L E 2 Results of the stepwise Peak MNI coordinates functional connectivity (SFC) analyses, Between-subject Number of Highest Cluster-level including between-subject comparisons comparison x y z voxels T-score p-value (adult patients with attention- One-step deficit/hyperactivity disorder [ADHD] Three-steps vs. healthy controls) for each SFC map at different functional distances (one-step ADHD > controls to seven-steps). Results were corrected L calcarine −4 −64 16 248 3.25 <.001 for multiple comparisons by means of a Five-steps Monte-Carlo simulation Controls > ADHD L medial orbitofrontal gyrus −4 58 −17 185 3.62 .001 ADHD > controls L calcarine −4 −68 16 398 3.47 <.001 Seven-steps ADHD > controls R lingual gyrus 16 −61 −9 283 3.17 <.001 Abbreviations: L, left; R, right. group differences of 55.6, 64.08, and 62.46% for three, five, and As observed in Figure 3, symptom severity was positively associated seven link-steps distances, respectively (see Figure 5 and Table S1). with the degree of functional connectivity in the left middle frontal gyrus and the right superior temporal gyrus, regions that largely overlapped with the sensory-motor network (56.65% in three link-step, 59.88% in 3.3 | Association with ADHD symptom severity five link-step, and 60.42% in seven link-step maps) and the dorsal atten- Table 3 and Figure 3 show the results of the regression analysis with tion network (28.24% in three-step, 26.38% in five link-step, and 25.57% the symptom severity scales. in seven link-step maps). With regard to negative correlations, we found 4650 PRETUS ET AL. F I G U R E 2 (a) Surface projections of the two-sample t-test results of uneven link-step distances for the between-groups' contrast between adult ADHD patients and the control sample. Images display Cohen's D effect sizes, and the positive (hot) and negative (winter) color maps range from the absolute value corresponding to an uncorrected p < .05 to an absolute value of 1. Values greater than 1 and lower than −1 are collapsed to 1 and − 1, respectively. Subplot (b) indicates to which large-scale resting-state functional networks the significant voxels belong according to the parcellation of Yeo et al. (2011). Left hemispheres are displayed. Abbreviation: ADHD, attention-deficit/hyperactivity disorder [Color figure can be viewed at wileyonlinelibrary.com] that the higher the ADHD rating scale score the lower the degree of that the increased functional connectivity within sensory regions may functional connectivity in the bilateral superior frontal gyrus, clusters that persist in adulthood. However, sensorial integration into the DMN was largely overlapped with the DMN (68.15% in three link-step, 64.14% in lower in adults with ADHD compared with controls, the reverse pattern five link-step, and 62.96% in seven link-step maps) and the frontoparietal of that found in children with ADHD (hyper-connectivity) (Carmona network (21.17% in three link-step, 25.50% in five link-step, and 25.05% et al., 2015). Thus, deviations from typical SFC patterns in adult ADHD in seven link-step maps). The associations between ADHD symptom only partially resembled those observed in children with ADHD in previ- severity scores and mean SFC values in each significant cluster per link- ous studies. The correlations between SFC values and symptom severity step distance map are illustrated in Figure 3c, d. in the adult ADHD sample corroborated the between-groups findings. In particular, ADHD rating scale scores were positively associated with increased functional connectivity within the somatosensory-motor net- 4 | DISCUSSION work and between seed regions and the dorsal attention network, and inversely associated with functional connectivity between sensory seed The present study aimed to elucidate how primary sensory regions inter- regions and the DMN and the frontoparietal network at short, medium, act with higher order association networks in adult ADHD, a disorder and long functional distances. We discuss each of these findings below. that is typically approached with a strong focus on higher order cognitive functions in neuroimaging research. Hence, we used SFC to assess multi- level information processing between early sensory and higher order 5 | I N C R E A S ED SF C I N V I S U A L C O R T I C E S cognitive circuits in the brain of medication-naïve adults with ADHD compared to healthy adults. Our results partially align with a previous Our results indicate increased functional connectivity between pri- SFC study on children with ADHD (Carmona et al., 2015), suggesting mary sensory areas and the visual cortex in adults with ADHD PRETUS ET AL. 4651 T A B L E 3 Results of the stepwise functional connectivity (SFC) analyses, including positive and negative associations of the attention- deficit/hyperactivity disorder (ADHD) rating scale in adult ADHD patients with each of the SFC maps at different functional distances (one-step to seven-steps). Results were corrected for multiple comparisons by means of a Monte-Carlo simulation Peak MNI coordinates ADHD rating scale x y z Number of voxels Highest R Cluster-level p-value One-step Three-steps Positive association L middle frontal gyrus −36 3 59 556 0.68 <.001 Negative association R superior frontal gyrus 20 31 53 496 0.64 <.001 Five-steps Positive association R superior temporal gyrus 56 −35 21 1,179 0.62 <.001 Negative association L superior frontal gyrus −28 65 15 859 0.71 <.001 R precuneus 12 −58 35 184 0.59 .001 Seven-steps Positive association R superior temporal gyrus 56 −35 21 1,185 0.61 <.001 Negative association L middle frontal gyrus −40 23 46 287 0.7 <.001 R superior frontal gyrus −28 65 15 441 0.65 <.001 R precuneus 12 −58 35 206 0.61 .001 Abbreviations: L, left; R, right. compared to controls. These findings are in line with Carmona et al. 6 | D E C R E A S E D SF C I N D M N R E G I O N S (2015) observations in children with ADHD, suggesting a similar pat- tern of deviations in children and adults with ADHD at intermediate While the intravisual loops were more functionally connected in adults and long functional distances. However, while children with ADHD with ADHD compared to controls, the circuits connecting sensory cor- showed hyperconnectivity within a small area of the lateral occipital tices with areas associated with higher order cognitive functions were cortex at short link-step distances, the differences cover almost all of weaker. At medium functional distance (five link-step distance), adults the bilateral medial occipital cortices in adults with ADHD (including with ADHD exhibited reduced degree of functional connectivity with, V1, V2, V3, which are part of both the dorsal and the ventral visual predominantly, the DMN, and with the frontoparietal and limbic net- circuitry), and are present at short, intermediate, and long link-step works to a lesser extent. Such differences do not necessarily imply a distances. less direct connection between sensory regions and the DMN. Since We believe that increased connectivity at medium and long func- our measurements of between-region functional connections included tional distances may reflect the general visual network hyper- a number of relay stations (which define functional distance), these dif- connectivity frequently described in children with ADHD (Cao et al., ferences could be due to: (a) a weaker connection between the sen- 2006; Kessler, Angstadt, Welsh, & Sripada, 2014; Marcos-Vidal et al., sory cortices and the relay stations, or (b) a weaker connection 2018; Wang et al., 2009). Our results suggest that at least part of the between the relay stations and areas associated with higher order cog- sensory information in adults with ADHD keeps reverberating within nitive functions. Since the direct functional connectivity between pri- the visual loops, decreasing the information flow between sensory mary sensory cortices and the relay stations was not weaker, the most regions and neural hubs supporting higher order cognitive functions. likely compromised loops are those connecting relay stations (atten- In addition, our observations support the notion that existent models tional or secondary sensory cortices) with the DMN. of ADHD would benefit from incorporating alterations in primary sen- These results are in line with studies pointing at deficits in DMN inter- sory areas, which are often ignored or taken as a false positive connectivity in adult ADHD, for example, decreased functional connectiv- (Castellanos and Proal, 2012) but may nonetheless significantly alter ity between the anterior cingulate cortex and the precuneus/posterior multilevel information processing in ADHD. cingulate cortex (Castellanos et al., 2008), decreased network homogeneity 4652 PRETUS ET AL. F I G U R E 3 Results of the regression analysis using the ADHD rating scale score as a predictor of stepwise functional connectivity. Subplots (a), (c), and (e) depict positive correlations and subplots (b), (d), and (f) depict negative correlations. Top surface images show the correlation coefficient R at three, five, and seven link-step distances, and the color maps range from r = 0.25 absolute value (which corresponds to the minimum [bilateral] significant correlation at p < .05) to r = 0.6 absolute value. Subplots (c) and (d) show the scatter plots for the positive and negative correlations, respectively. Bottom surface images ( e and f) indicate to which large-scale resting-state functional networks the significant voxels belong according to the parcellation of Yeo et al. (2011). Left hemispheres are displayed. Abbreviations: ADHD, attention- deficit/hyperactivity disorder; SFC, stepwise functional connectivity [Color figure can be viewed at wileyonlinelibrary.com] in the DMN (Uddin et al., 2008), and distributed hypo-connectivity within altered functional connectivity with other DMN nodes (Castellanos the DMN (Sripada et al., 2014). Additionally, children with ADHD have et al., 2008; Uddin et al., 2008), reduced deactivation while complet- been found to exhibit decreased short and long-range functional connec- ing a task (Peterson et al., 2009), and slower cortical thinning in chil- tivity density in regions of the DMN (Tomasi & Volkow, 2012) and dren with higher symptom severity (Shaw et al., 2011). Since the increased local functional connectivity in the boundaries of the DMN MPFC plays a key role within the DMN, the sensory hypoconnectivity (Marcos-Vidal et al., 2018). In adults with ADHD weaker segregation has with the MPFC observed in the present data set could be pointing at been found between the DMN and cognitive control networks (Lin, alterations in DMN-associated functions such as mind-wandering Cocchi, et al., 2018). Altogether, these results point to a lack of integration (Fox et al., 2005), which could underlie attentional deficits in ADHD. in DMN regions and a lack of segregation between DMN and task positive Our findings contrast with those in Carmona et al. (2015), which networks. found greater SFC in the DMN in children with ADHD. In general, the As a part of the DMN, the medial prefrontal cortex (MPFC) was DMN undergoes intense maturational changes with age as it transi- particularly affected in our sample. ADHD-associated alterations in tions from sparse within-network functional connectivity in typically the MPFC have been reported in a wide variety of studies, including developing children to a more robustly interconnected network in PRETUS ET AL. 4653 neurotypical adults (Fair et al., 2008). On a speculative note, the whether the increased SFC between seed regions and nodes of the hypoconnectivity and hyperconnectivity profiles observed at long dorsal attention network predicted attentional performance. functional distances in adults and children with ADHD, respectively, Regarding negative correlations, we found that the higher the could be ascribed to altered DMN consolidation in early ages, yielding scores in ADHD symptom severity, the lower the degree of connec- to abnormal functional connectivity profiles when compared to age- tivity between the seed regions and the DMN and the frontoparietal matched controls. However, a longitudinal study would be needed in network at short, medium, and long functional distance (three to order to establish conclusions on the evolution of default mode SFC seven link-step maps). These findings are aligned with studies pointing throughout the lifespan of ADHD patients from childhood into adult- to impaired functioning of the frontoparietal network in ADHD hood compared to controls. (Castellanos and Proal, 2012; Dickstein, Bannon, Xavier Castellanos, & The discrepancies between our study and the previous study on Milham, 2006; Lin et al., 2015; Silk et al., 2005; Silk, Vance, Rinehart, SFC in children with ADHD (Carmona et al., 2015) could also be Bradshaw, & Cunnington, 2008) but also highlight the relevance of related to medication status. While Carmona et al. (2015) analyzed DMN alterations in adult ADHD. brain activity in a mixed sample of medicated and medication-naïve Altogether, our results are consistent with the view that ADHD is children, our present study comprised medication-naïve adults. As associated with altered information flow between sensory and neural reported by Carmona et al. (2015), medication status can influence nodes supporting higher order cognitive functions: whereas primary sensory areas seem to be hyperconnected in the first steps, they seem SFC profiles (see Figure 5 in Carmona et al., 2015). Moreover, to be under-connected to brain regions supporting higher order cogni- atomoxetine has been found to strengthen the anticorrelation tive functions, especially the DMN, at long functional distances between the DMN and task-positive networks (Lin, Tseng, Lai, (Sepulcre et al., 2012). Our results also point out that, in terms of SFC, Matsuo, & Gau, 2015), and ADHD medication in general has been the ADHD brain is highly heterogeneous—as indicated by our limited proposed to normalize DMN activity (Pereira & de Castro-Manglano, classification accuracy—and suggest that part of this variability might 2017). All in all, medication status seems a relevant factor to consider be driven by differences in the severity of ADHD symptoms. in future studies specifically addressing DMN functional connectivity in ADHD across the lifespan. 8 | LIMITATIONS 7 | ASSOCIATIONS WITH CLINICAL SCALES The main limitation of our study is the relatively small sample size with a wide age range. This stems from the difficulty in recruiting ADHD At short, medium, and long functional distance, ADHD symptom patients who reach adulthood without comorbidity with other disor- scores were positively correlated with the degree of functional con- ders (including tobacco and alcohol use in the last 6 months) and, nectivity between seed regions and regions of the somatosensory- importantly, without previous exposure to ADHD medication. motor and dorsal attention networks. The positive correlation with Also, as our sample exclusively comprised patients with the com- somatosensory-motor regions is coherent with the increased SFC bined subtype, we could not provide a specific account of what pre- within sensory cortices in ADHD patients compared to controls. cise ADHD phenotypes are associated with the SFC alterations. The Indeed, children with ADHD also show increased degree centrality, question remains whether impaired sensory-multimodal integration that is, increased number of direct connections with other nodes in may be affecting attentional control, as well as other symptoms such the somatosensory cortex (Di Martino et al., 2013). Anatomical stud- as hyperactivity and impulsivity. Further research should be able to ies also point to alterations in somatosensory-motor cortices that per- draw more precise conclusions on what particular phenotypes are sist into adulthood (Marcos-Vidal et al., 2018). For instance, studies in linked to which specific SFC alteration. children with ADHD report decreased gray matter volume in the Regarding methodological concerns, we should specify two. First, somatosensory, motor, and premotor cortices (Carmona et al., 2005), computational constraints required us to downsample data to rela- while studies in adults with ADHD found increased cortical thickness tively large voxels (4 mm3). As computational power continues to in the presupplementary motor area and the somatosensory cortex increase, the specific details of our results could be reexamined in the (Duerden, Tannock, & Dockstader, 2012). original data and in future data sets acquired at even greater temporal We also found a positive correlation between ADHD symptom and spatial resolutions. Second, SFC analysis does not provide infor- severity and SFC in the dorsal attentional network. This finding dove- mation on the directionality of the functional connectivity network tails with studies pointing to altered within network connectivity in under study; that is, the alterations observed could be interpreted as the dorsal attentional network in adults with ADHD (Sidlauskaite, affecting sensory-to-cognitive and/or cognitive-to-sensory informa- Sonuga-Barke, Roeyers, & Wiersema, 2016) and increased connectiv- tion processing. If SFC decrease was affecting sensory-to-cognitive ity between the dorsal attentional network and regions of the DMN functional streams, this would involve a reduced information feed in medication-naïve children with ADHD (Lin, Lin, et al., 2018). Given from sensory up to higher level association nodes; if it was affecting the relevance of the dorsal attention network in attentional perfor- cognitive-to-sensory functional streams, this would entail lower cogni- mance (Vossel, Geng, & Fink, 2014), it would be interesting to test tive control over incoming perceptual information—thus hindering 4654 PRETUS ET AL. selective attention. Hence, although previous studies using SFC analy- Biederman, J., Petty, C. R., Evans, M., Small, J., & Faraone, S. V. (2010). sis tend to interpret their results in the sensory-to-cognitive direction How persistent is ADHD? A controlled 10-year follow-up study of boys with ADHD. Psychiatry Research, 177, 299–304. https:// (Carmona et al., 2015; Hong et al., 2019; Sepulcre et al., 2012), the linkinghub.elsevier.com/retrieve/pii/S0165178109005198 directionality of the observed alterations should be tested in future Cao, Q., Zang, Y., Sun, L., Sui, M., Long, X., Zou, Q., & Wang, Y. 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Hum Brain Mapp, 36, 2544–2557. http://doi.wiley.com/10.1002/hbm. characterization was biased neither by comorbidities nor by medication. 22790 Our results suggest that the brain of adults with ADHD presents an Carmona, S., Vilarroya, O., Bielsa, A., Trèmols, V., Soliva, J. C., Rovira, M., … atypical flow of information from short to long functional distances of Bulbena, A. (2005). Global and regional gray matter reductions in the sensation to cognition continuum. In particular, SFC in medication- ADHD: A voxel-based morphometric study. Neuroscience Letters, 389, naïve adults with ADHD was characterized by over-connectivity within 88–93. Castellanos, F. X., Margulies, D. S., Kelly, C., Uddin, L. Q., Ghaffari, M., primary sensory regions followed by under-connectivity between sen- Kirsch, A., … Milham, M. P. (2008). Cingulate-precuneus interactions: sory regions and nodes of the DMN. 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