Skip to main content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
JAMA Psychiatry. Author manuscript; available in PMC 2020 Aug 11.
Published in final edited form as:
JAMA Psychiatry. 2017 Jan 1; 74(1): 76–84.
doi: 10.1001/jamapsychiatry.2016.3072
PMCID: PMC7418037
NIHMSID: NIHMS1042352
PMID: 27851842

Estimating the heritability of structural and functional brain connectivity in families affected by Attention Deficit Hyperactivity Disorder

Gustavo Sudre, PhD,1 Saadia Choudhuri, MA,1 Eszter Szekely, PhD,1 Teighlor Bonner, BA,1 Elanda Goduni, BA,1 Wendy Sharp, MSW,1 and Philip Shaw, MD PhD1
Author information Copyright and License information Disclaimer

Associated Data

Supplementary Materials

Abstract

Importance.

Despite its high heritability, few risk genes have been identified for ADHD. Brain-based phenotypes could aid gene discovery. Here we focus on the myriad of structural and functional connections that support cognition. Disruption of such connectivity is a key pathophysiological mechanism for ADHD, and identifying heritable phenotypes within these connections could provide candidates for genomic studies.

Objective:

To identify the structural and functional connections that are heritable and pertinent to ADHD.

Methods:

This objective is attained by studying extended, multigenerational families, enriched for ADHD. Structural connectivity was defined by diffusion tensor imaging (DTI) of white matter tract microstructure and functional connectivity through resting state functional magnetic resonance imaging (rsfMRI). Heritability and association with ADHD symptoms were estimated on 24 extended, multigenerational families enriched for ADHD (305 members with clinical phenotyping, 213 with DTI, 193 with rsfMRI data). Findings were confirmed on 52 nuclear families (132 members with clinical phenotypes, 119 with DTI, 84 with rsfMRI).

Findings.

Microstructural properties of white matter tracts connecting ipsilateral cortical regions and the corpus callosum were significantly heritable (ranging from h2=0.69 (SE 0.13, p=0.0000002) for radial diffusivity of the right superior longitudinal fasciculus to h2=0.45 (SE 0.06, p=0.0009) for fractional anisotropy of the right inferior fronto-occipital fasciculus). Association with ADHD symptoms was found several tracts, most strongly for the right superior longitudinal fasciculus (t=−3.05, p<0.003). Heritable patterns of functional connectivity were detected within the default mode, cognitive control and ventral attention networks (mean h2 =0.34[SE0.15]). In all cases subregions within each network showed heritable functional connectivity with the rest of that network. More symptoms of hyperactivity/impulsivity (t=2.63, p=.008) and inattention (t=−2.34, p=0.02) were associated with decreased functional connectivity within the DMN. Phenotypic and genetic correlations were found between the ventral attention network and a major association white matter tract (the inferior fronto-occipital fasciculus).

Discussion:

Using multigenerational, extended and nuclear families, we identify the features of structural and functional connectivity that are both significantly heritable and associated with ADHD. Additionally, we find shared genetic factors account for some phenotypic correlations between functional and structural connections. Such work helps prioritize the facets of the brain’s connectivity for future genomic studies.

Introduction.

There has been limited progress in identifying the specific genes contributing to the established high heritability of ADHD 1,2. The use of heritable brain-based phenotypes pertinent to the disorder might accelerate progress in part as they lie closer to genes than the more distal clinical phenotype 3,4. Here we focus on the myriad structural and functional connections within the brain that support multiple cognitive, motor and affective processes 5–8. We do so as ADHD is increasingly viewed as the product of anomalous connectivity or ‘miswiring’ that results in disruption to large scale brain systems, producing symptoms 8–10. Additionally, such a focus addresses a gap in our knowledge. Although the heritability and association with ADHD of grey matter morphology has been extensively investigated 11–13, less is known of which aspects of connectivity are both heritable and pertinent to ADHD. Such a study would complement estimates of the heritability of structural and functional connectivity among healthy twins and families 14–23, and among families affected by bipolar affective disorder and schizophrenia 24–26.

Structural and functional connectivity can be studied on many levels. Here we use magnetic resonance imaging to define in vivo the microstructural properties of major white matter tracts.. We take this approach as ADHD and its core cognitive deficits have been associated with anomalies in the white matter tracts connecting different cerebral cortical regions 6. We also define functional connectivity, through the coordinated patterns of neural activity, or intrinsic networks, that emerge spontaneously when a subject is not engaged in task oriented behavior 27,28. ADHD has been conceptualized as an imbalance between these intrinsic networks, particularly the default mode network (DMN) –prominent during internally directed thought- and the networks supporting cognitive control and attention 29,30.

Imaging of the brain’s structural and functional connectivity provides a multitude of phenotypes and it is important to prioritize these for future genomic study. We take the strategy of first identifying the subset of phenotypes that is highly heritable. Such highly heritable phenotypes boost the chances of detecting underlying genes. Further prioritization can then be made on the strength of association with ADHD symptoms. We estimate connectivity within multigenerational, extended families in which a high proportion of members are affected by ADHD. This affords an efficient strategy to define both heritability and association with ADHD symptoms. We confirm initial heritability estimates in a separate cohort of nuclear families, also affected by ADHD. This approach also meets three further aims. First, we can define the heritability of all of the major intrinsic networks, broadening the prior focus on the DMN 19,21. Second, we can determine which heritable connectivity features are also pertinent to the symptoms of ADHD. Finally, our family based design allows us to answer the question: do components of the structural and functional connections share genetic determinants, or are they genetically distinct?

Methods.

Participants.

Inclusion criteria for the extended families were (1) the presence of second, third or higher degree relatives; (2) a diagnosis of ADHD in at least 25% of family members (around ten times the adult ADHD and four times the childhood ADHD prevalence rates 31,32). For nuclear families, the main inclusion criterion was at least two first-degree relatives (sibling or parent-child), at least one with ADHD.

The diagnosis of adult ADHD used the Conners’ Adult ADHD Diagnostic Interview for DSM-IV™, a clinician administered structured interview that establishes the number of symptoms of inattention and hyperactivity-impulsivity (0 to 9 for each category). The interview ascertains both current, adult symptoms of ADHD and the childhood history of these symptoms. We leveraged the family design to obtain collateral confirmation of childhood symptoms when possible. Presence of other psychiatric diagnoses was established through the Structured Clinical Interview for DSM Axis I Disorders. For children, the parental Diagnostic Interview for Children and Adolescents Interviews was used 33. Interviews were conducted by two experienced clinicians (PS and WS; inter-rater reliabilities, kappa >0.9), and neurological assessment by a physician (PS), Exclusion criteria were an IQ <80 (determined with Wechsler intelligence scales), neurological disorders affecting brain structure, current substance dependence, or psychotic disorders. Twenty-one of the 24 extended families and 42 of the 52 nuclear families were white, non-Hispanic. The institutional review board of the National Human Genome Research Institute approved the research protocol, and written informed consent was obtained from adult participants and parents; children gave written assent.

Neuroimaging.

Diffusion tensor imaging (DTI) data were collected on a 3-T HDx MRI system (GE Healthcare, Milwaukee, WI) with a single-shot dual-spin-echo echo-planar imaging sequence. Imaging parameters, preprocessing and tensor fitting are described in the Supplemental Material. The same acquisition parameters were used throughout except that 60 volumes were acquired for children to shorten scan time, compared to 80 in adults. Quality control measures included the re-acquisition of corrupted data in real time 34, visual inspection and removal of corrupted data. Participants were excluded if they had >10% of corrupted volumes; a trait value exceeding the sample mean by three or more standard deviations; or a mean overall tract fractional anisotropy of less than 0.25. Overall, 332 of the original 363 DTI data sets were retained. Although there were no significant correlations between head motion parameters and tract measures, we nonetheless considered motion as a covariate.

DTI-TK software registered the diffusion tensors into a common template space 35,36. We considered all of the eleven tracts measured by this software: the bilateral uncinate, inferior fronto-occipital, superior longitudinal, inferior longitudinal, corticospinal fasciculi, and the corpus callosum. For each tract, fractional anisotropy (FA), a summary metric of overall tract diffusion properties was defined, along with axial diffusivity (AD), and radial diffusivity (RD) which are proxies for the flow of water along the axis and the radius of the axon respectively.

Resting state fMRI was acquired using a gradient-echo echo-planar (EPI) series with whole-brain coverage. Participants were instructed to lie still for 5 minutes, 5 seconds and gaze at a fixation point. Preprocessing used the AFNI software package 37. The first 3 EPI volumes were removed, as well as any volumes that showed motion of > 0.2 mm, and volumes that contained more than 10% of voxels considered to be outliers. Following such ‘scrubbing’, the remaining subjects had a mean of 278s of usable data (SD 34s) and a lower limit of 180 seconds was set. In total 277 (193 extended, 84 nuclear) of the 340 originally acquired rsFMRI scans were retained. The amount of usable data was associated with hyperactivity/impulsivity symptoms (at t=1.68, p=0.1), but did not vary with inattention (t=1.05, p=0.29), age or sex (all p>0.1). The EPI volumes were registered to the individual’s T1-weighted anatomical image and to a MNI template38. Activation in white matter and lateral ventricle masks were removed using ANATICOR and the time derivative of the motion parameters were also regressed out 39.

We used Independent Component Analysis to decompose the BOLD signal into spatially distinct maps and their time courses40. Each independent component is a spatial map of functionally connected regions -an intrinsic network- that shows the strength of the contribution of every voxel to the intrinsic network. These intrinsic networks closely resemble, and are named for large-scale brain networks that support cognition. We focused on the seven major intrinsic networks described by Yeo and colleagues: the default mode, dorsal and ventral attention, cognitive control, affective, visual and somatomotor networks 41. Finally, dual regression created subject-specific spatial maps for each network 42. In each map, the value of each voxel shows the strength of the functional connection between that voxel and the rest of the network for that subject.

Analysis.

Heritability was estimated using Sequential Oligogenic Linkage Analysis Routine (SOLAR)43. It uses a variance component method to estimate the proportion of phenotypic variance due to additive genetic factors (i.e. narrow sense heritability)- see Supplemental Material. Inverse normalization was applied to phenotypes as heritability estimates in SOLAR are sensitive to skewed distributions. SOLAR also estimated the phenotypic correlation between heritable traits and the underlying genetic and environmental correlations, applying a false discovery rate (FDR) of 0.05 to correct for multiple testing 44.

ADHD symptom counts were regressed against each heritable trait, using a mixed-effects model with family identity as the random term. For adults, we were primarily interested in current symptoms, as we earlier found white matter tracts anomalies to be associated with current, not childhood, symptoms in unrelated adults 10. Sex, age, age2, movement, and movement2 were considered as covariates and retained if p <0.1.

We estimated the heritability of 33 white matter tracts properties in the extended families (3 properties for 11 tracts). A Bonferroni correction was applied and heritability declared significant at p<.05/33=0.0015. Confirmation of heritability in the nuclear family cohort was taken at a nominal p<0.05. In resting state analyses in extended families, the probability of false positive spatial clusters was estimated using a non-parametric approach (permutation) setting a voxel-wise p<0.05 with a cluster-corrected alpha level p<0.002 (see supplemental material for details). Confirmation of the heritability in the nuclear families was taken at a at nominal p<0.05 at the voxel level. No cluster extent correction was applied in the nuclear families as we were testing heritability within a region of interest initially defined by the extended families. In testing for associations between heritable connectivity measures and ADHD symptoms, results within each modality were adjusted for multiple testing using Bonferroni correction.

We modeled age–related change in the connectivity measures using linear mixed models- Supplemental Material. We also examined whether heritability estimates were similar in ‘youth’ (<=21years) and ‘adult’ (>21 years) groups. Finally, we determined if associations between ADHD symptoms and the connectivity measures differed between these age groups.

Results

Within the extended families, 115 of 305 (38%) relatives had ADHD. In the nuclear families, 78 of 132 individuals (59%) were affected – Table 1, Supplemental Figures 1,2.

Table 1:

Demographic details of extended families.

FAMILYTotal size/ ADHD N(%)Age (years): Mean (SD)Min (years)Max (years)DTI: N (%)rsMFRI N (%)
AA25 /7 [28%]31.2 (17)9.864.216 (64%)15 (60%)
BB22 /9 [41%]34.5 (20.6)4.565.517 (77%)15 (68%)
CC21 /9 [43%]31.3 (20.4)5.770.916 (76%)11 (52%)
DD19 /7 [37%]29.8 (20.7)5.468.911 (57%)11 (57%)
EE19 /8 [42%]25.3 (18.5)4.858.916 (84%)15 (78%)
FF18 /6 [33%]32.8 (21.2)7.770.116 (88%)12 (66%)
GG17 /4 [24%]37.1 (23)10.377.414 (82%)14 (82%)
HH17 /7 [41%]15 (10.2)4.735.310 (58%)9 (52%)
II16 /5 [31%]18.3 (13.5)6.644.311 (68%)9 (56%)
JJ15 /6 [40%]32.2 (13.6)22.641.92 (13%)0 (0%)
KK13 /4 [31%]32.1 (21.4)6.065.99 (69%)8 (61%)
LL13 /4 [31%]28 (19.1)5.471.19 (69%)9 (69%)
MM13 /8 [62%]28.8 (18.4)6.248.76 (46%)7 (53%)
NN12 /7 [58%]40 (23.5)18.285.89 (75%)9 (75%)
OO10 /3 [30%]29.3 (17.2)8.349.58 (80%)7 (70%)
PP8 /3 [38%]24 (16)7.643.16 (75%)5 (62%)
QQ8 /2 [25%]32.5 (17.5)7.362.36 (75%)6 (75%)
RR8 /2 [25%]39.1 (26.9)10.077.67 (87%)7 (87%)
SS8 /2 [25%]28.3 (24)4.971.35 (62%)4 (50%)
TT6 /4 [67%]40.6 (16.3)26.962.75 (83%)5 (83%)
UU5 /2 [40%]50.5 (21.7)19.668.23 (60%)4 (80%)
VV4 /3 [75%]35.6 (22.1)12.855.14 (100%)4 (100%)
WW4 /1 [25%]21.5 (13.8)11.041.34 (100%)4 (100%)
ZZ4 /2 [50%]25.4 (16)7.037.13 (75%)3 (75%)
Total305 /115 [38%]30.4 (19.7)213 (69%)193 (63%)

Structural connectivity.

Fourteen of 33 white matter tract properties emerged as significantly heritable in the 213 relatives from extended families - Figure 1, Supplemental Table 3. Estimates ranged from h2=0.69 (SE 0.13, p=0.0000002) for radial diffusivity of the right superior longitudinal fasciculus to h2=0.46 (SE 0.15, p=0.0009) for fractional anisotropy of the right inferior fronto-occipital fasciculus. Twelve of these tract properties were further confirmed as significantly heritable (at p<0.05) in 119 individuals from nuclear families.

An external file that holds a picture, illustration, etc. Object name is nihms-1042352-f0001.jpg
Heritability of white matter tract properties in extended and nuclear families.

The panels show heritability estimates for fractional anisotropy, radial diffusivity and axial diffusivity. Superior and anterior views are provided. In the extended families, tracts are shown which were significantly heritable at a Bonferroni adjusted p<0.05. In the nuclear families, tracts are confirmed as heritable at p<0.05. Two of these 12 heritable tracts properties were also significantly associated with inattentive symptoms, and one tract property was associated with hyperactive-impulsive symptoms.

We next examined association between the heritable tract properties and ADHD symptom count. Radial diffusivity of the right superior longitudinal fasciculus was associated with inattention at a corrected level of significance (t=−3.05, P<0.003, Bonferroni adjusted P<0.05). Axial diffusivity of this tract showed a nominally significant association with inattention (t=−2.51, p=0.01). Association was also found between fractional anisotropy of the right inferior fronto-occipital fasciculus and hyperactivity/impulsivity at a nominal level of significance (t=−2.35, p=0.02). Results of DSM-5 diagnostic group contrasts are given in the Supplemental Material. Thus, radial diffusivity of the right superior longitudinal fasciculus emerged as the most robustly heritable and ADHD-associated white matter tract property.

Functional connectivity.

The intrinsic functional networks found to have regions of heritable functional connectivity in the 193 relatives from extended families are shown in Figure 2 and Supplemental Table 4. First, within DMN, functional connectivity between a posterior cingulate region and the remainder of the network was heritable (h2=0.36, SE 0.16, cluster level significance p<0.002). Within the cognitive control network, functional connectivity between its right inferior parietal ‘component and the rest of the network emerged as heritable (h2= 0.32, SE=0.15, cluster level significance p<0.002). For the ventral attention networks, heritability localized to the right superior frontal gyrus (h2= 0.36, SE=0.15, p<0.002). No patterns of heritable functional connectivity were found within the other networks.

An external file that holds a picture, illustration, etc. Object name is nihms-1042352-f0002.jpg
Heritable patterns of functional connectivity within intrinsic networks.

In each panel, regions are shown (in red) that have heritable functional connectivity with the rest of that network. For each network, the connectivity regression coefficients are thresholded at the 95th percentile for visualization and shown in blue. Results are shown for the default mode, the ventral attention network and cognitive control network. Associations were found between both symptoms dimensions and the heritable functional connectivity of the default mode network. Additionally, there was an association between hyperactive/impulsive symptoms and the functional connectivity patterns within the ventral attention network.

These heritability findings were confirmed using rsfMRI data from 84 members of nuclear families- Figure 3, Supplemental Table 5. Functional connectivity between the posterior cingulate region and the rest of the network was found to be heritable in nuclear families. Similarly, the patterns of heritable functional connectivity within the cognitive control, dorsal and ventral attention networks first delineated in extended families were also present in nuclear families. Throughout, the heritable regions in the nuclear families were less extensive than those initially defined in the extended families.

An external file that holds a picture, illustration, etc. Object name is nihms-1042352-f0003.jpg
Phenotypic and genetic correlations between heritable properties of white matter tracts and intrinsic networks.

Each line represents a significant correlation (applying a FDR q<0.05). Heritable components of the ventral attention network showed both phenotypic and genetic correlations with the inferior fronto-occipital fasciculus. Abbreviations: DAN= dorsal attention network; Cog= cognitive control network; DMN= default mode network; VAN=ventral attention network; SLF= superior longitudinal fasciculus; ILF= inferior longitudinal fasciculus; unc= uncinate; IFO=inferior fronto-occipital; CC=corpus callosum

Associations were found between the heritable functional connectivity of the DMN and both hyperactive/impulsive symptoms (t=2.63, p=0.008) and inattentive symptoms (t=2.34, p=0.02). A significant association between hyperactive/impulsive symptoms and the functional connectivity patterns within the ventral attention network was also found (t=2.76, p=0.006).

Considering developmental trends, the fractional anisotropy of most white matter tract showed a childhood and adolescent increase, which stabilized and then decreased in adulthood – (see Supplemental Material pages 10–16). We did not detect age related change in connectivity between the heritable regions and the rest of each network. Heritability estimates were mostly similar in younger (<=21years) and adult (>21 years) groups. Also, associations between symptoms and connectivity measures did not vary significantly between these age groups. Heritability estimates were robust to the exclusion of those on psychostimulants, and the exclusion of those on any psychotropic medication– Supplemental Tables 6 and 7.

Phenotypic and genetic correlations.

Among white matter properties, 383 of 492 possible phenotypic trait pairs were significantly correlated (applying a FDR, q<0.05). Correlated traits clustered more by diffusivity property than by tract location – see Figure 3 and Supplemental Figures 3,4. Functional connectivity was defined by an approach that provides independent components and thus phenotypic correlations were neither expected nor found.

Genetic correlations were found within and across modalities. Within the twelve heritable white matter properties, shared heritability was found for 58 of the 66 possible pairs (FDR, q<0.05). Genetic correlations were present between the cognitive control and dorsal attention networks at trend level only (rhog=.41 , p=.06).

Finally, we tested for cross-modal correlations. Heritable components of the ventral attention network showed both phenotypic and genetic correlations with the inferior fronto-occipital fasciculus. Specifically, the ventral attention network showed phenotypic (rhop=−.11, p=.02) and genetic correlations (rhog=−.45, p=.02) with radial diffusivity of the right interior fronto-occipital fasciculus. This implies that this phenotypic correlation is partly genetically determined. Some cross-modal correlations were purely phenotypic, including a correlation between the heritable aspects of the DMN and the right superior longitudinal fasciculus (rhop=−.12, p=.03).

Discussion

Several facets of structural and functional connectivity emerged as significantly heritable within extended families affected by ADHD. A separate cohort of nuclear families confirmed this heritability. For white matter tracts, heritability was found for microstructural features of the association (superior longitudinal fasciculi, inferior fronto-occipital, uncinate fasciculi) and commissural (corpus callosum) but not projection tracts (corticospinal tract). Heritable patterns of functional connectivity were also noted within the default mode, cognitive control and attention networks. Association with ADHD symptom severity emerged primarily for the heritable facets of the right superior longitudinal fasciculus and the DMN. Finally, cross-modal phenotypic and genetic correlations were found. A white matter tract, the inferior fronto-occipital fasciculus was phenotypically correlated, and shared common genetic determinants with the ventral attention network.

Microstructural properties of the right superior longitudinal fasciculus were both heritable and associated with ADHD, following adjustment for multiple comparisons. Meta-analyses find this tract is compromised in ADHD 6, associated with working memory and sustained attention in children with and without ADHD 45–48, and lesions of the tract are tied to deficits in these cognitive domains 49,50. Thus, the tract is a promising candidate phenotype for future genetic studies.

Our estimates of heritability of white matter tracts are consistent with prior studies of extended families and twins, either unaffected or heavily affected by psychiatric disorder 14–18,20,24–26. However, different tracts appear to be associated with different psychiatric disorders. For example, while heritable measures of the corpus callosum have been associated with bipolar affective disorder 24, we link ADHD symptoms with heritable properties of the right superior longitudinal fasciculus.

Using families enriched for ADHD, we confirm the heritability of the DMN that was first reported among extended families not ascertained for mental illness 19. We also show an association between these heritable DMN components and ADHD symptoms, implying a partly genetic determination of the atypical DMN activity that can disrupt goal-directed activity and drive ADHD symptoms. Further, we find that other intrinsic networks closely associated with ADHD- the cognitive control and ventral attention networks- had heritable patterns of functional connectivity.

We detected genetic and phenotypic correlations spanning our measures of structural and functional connectivity. We find that genetic factors contribute to the phenotypic correlation between the functional connectivity within the ventral attention network and a major white matter tract, the inferior fronto-occipital fasciculus. The inferior fronto-occipital fasciculus connects dorsal parieto- and basal temporo-occipital regions to the dorsolateral prefrontal and orbitofrontal cortex 51,52, making it a plausible substrate for the physical connections within the ventral attention network. The ventral attention system is right lateralized and it is thus of note that we find the right inferior fronto-occipital fasciculus to be correlated 53. Altered microstructural properties of this tract have been reported in adult ADHD 5,54, consonant with its role in cognitive skills which are often impaired in ADHD, such as visuospatial integration and attention set–shifting 55–58. Such cross-modal phenotypes that share genetic determinants may provide well-constrained phenotypes, ideal for future genomic studies.

There are several limitations. First, while the study’s cross-sectional design limits inferences about developmental trends, we found that heritability estimates and the associations between symptoms and connectivity measures did not differ by age group. However, a longitudinal study is needed to fully characterize possible interactions between age and heritability in ADHD. Second, many but not all findings in the extended families were confirmed in the nuclear families. The lack of confirmation could arise from differences in age composition between the extended and nuclear families and the smaller overall size of the nuclear family cohort. Finally, although we estimated movement during the resting state scan, we did not enquire whether the participant was able to sustain gaze throughout.

Demonstrating heritability and associations with a disorder is an initial but vital stage for the use of the structural and functional connectivity as phenotypes. The next step is to ask which genes drive this heritability and confer risk for ADHD.

Supplementary Material

Figure S1

Suppl Material

eTable1-5; supplemental methods; efigure 1a & 1b

More supplemental materials

Acknowledgements

Funded by the intramural research program of the NHGRI and NIMH.

Footnotes

All authors declare no conflict of interest.

References

1. Brikell I, Kuja‐Halkola R, Larsson H. Heritability of attention‐deficit hyperactivity disorder in adults. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics. 2015;168(6):406–413. [PubMed] [Google Scholar]
2. Faraone SV, Perlis RH, Doyle AE, et al. Molecular genetics of attention-deficit/hyperactivity disorder. Biological Psychiatry. 2005;57(11):1313–1323. [PubMed] [Google Scholar]
3. Castellanos FX, Tannock R. Neuroscience of attention-deficit/hyperactivity disorder: the search for endophenotypes. Nature Reviews Neuroscience. 2002;3(8):617–628. [PubMed] [Google Scholar]
4. Glahn DC, Thompson PM, Blangero J. Neuroimaging endophenotypes: strategies for finding genes influencing brain structure and function. Human brain mapping. 2007;28(6):488–501. [PMC free article] [PubMed] [Google Scholar]
5. Konrad A, Dielentheis TF, El Masri D, et al. Disturbed structural connectivity is related to inattention and impulsivity in adult attention deficit hyperactivity disorder. European Journal of Neuroscience. 2010;31(5):912–919. [PubMed] [Google Scholar]
6. van Ewijk H, Heslenfeld DJ, Zwiers MP, Buitelaar JK, Oosterlaan J. Diffusion tensor imaging in attention deficit/hyperactivity disorder: A systematic review and meta-analysis. Neuroscience & Biobehavioral Reviews. 2012;36(4):1093–1106. [PubMed] [Google Scholar]
7. Posner J, Park C, Wang Z. Connecting the dots: a review of resting connectivity MRI studies in attention-deficit/hyperactivity disorder. Neuropsychology Review. 2014;24(1):3–15. [PMC free article] [PubMed] [Google Scholar]
8. Di Martino A, Fair DA, Kelly C, et al. Unraveling the miswired connectome: a developmental perspective. Neuron. 2014;83(6):1335–1353. [PMC free article] [PubMed] [Google Scholar]
9. Konrad K, Eickhoff SB. Is the ADHD brain wired differently? A review on structural and functional connectivity in attention deficit hyperactivity disorder. Human Brain Mapping. 2010;31(6):904–916. [PMC free article] [PubMed] [Google Scholar]
10. Sudre G, Shaw P, Wharton A, Weingart D, Sharp W, Sarlls J. White matter microstructure and the variable adult outcome of childhood Attention Deficit Hyperactivity Disorder. Neuropsychopharmacology. 2015;40:746–754. [PMC free article] [PubMed] [Google Scholar]
11. Winkler AM, Kochunov P, Blangero J, et al. Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies. Neuroimage. 2010;53(3):1135–1146. [PMC free article] [PubMed] [Google Scholar]
12. Joshi AA, Lepore N, Joshi SH, et al. The contribution of genes to cortical thickness and volume. Neuroreport. 2011;22(3):101. [PMC free article] [PubMed] [Google Scholar]
13. Friedman LA, Rapoport JL. Brain development in ADHD. Current Opinion in Neurobiology. 2015;30:106–111. [PubMed] [Google Scholar]
14. Shen K-K, Rose S, Fripp J, et al. Investigating brain connectivity heritability in a twin study using diffusion imaging data. NeuroImage. 2014;100:628–641. [PMC free article] [PubMed] [Google Scholar]
15. Brouwer RM, Mandl RCW, Peper JS, et al. Heritability of DTI and MTR in nine-year-old children. NeuroImage. 2010;53(3):1085–1092. [PubMed] [Google Scholar]
16. Chiang M-C, McMahon KL, de Zubicaray GI, et al. Genetics of white matter development: a DTI study of 705 twins and their siblings aged 12 to 29. Neuroimage. 2011;54(3):2308–2317. [PMC free article] [PubMed] [Google Scholar]
17. Kochunov P, Jahanshad N, Marcus D, et al. Heritability of fractional anisotropy in human white matter: A comparison of Human Connectome Project and ENIGMA-DTI data. NeuroImage. 2015;111:300–311. [PMC free article] [PubMed] [Google Scholar]
18. Jahanshad N, Kochunov PV, Sprooten E, et al. Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: A pilot project of the ENIGMA-DTI working group. NeuroImage. 2013;81:455–469. [PMC free article] [PubMed] [Google Scholar]
19. Glahn DC, Winkler AM, Kochunov P, et al. Genetic control over the resting brain. Proceedings of the National Academy of Sciences. 2010;107(3):1223–1228. [PMC free article] [PubMed] [Google Scholar]
20. Jahanshad N, Lee AD, Barysheva M, et al. Genetic influences on brain asymmetry: A DTI study of 374 twins and siblings. NeuroImage. 2010;52(2):455–469. [PMC free article] [PubMed] [Google Scholar]
21. Sinclair B, Hansell NK, Blokland GAM, et al. Heritability of the network architecture of intrinsic brain functional connectivity. NeuroImage. 2015;121:243–252. [PMC free article] [PubMed] [Google Scholar]
22. van den Heuvel MP, van Soelen ILC, Stam CJ, Kahn RS, Boomsma DI, Hulshoff Pol HE. Genetic control of functional brain network efficiency in children. European Neuropsychopharmacology. 2013;23(1):19–23. [PubMed] [Google Scholar]
23. Yang Z, Zuo X-N, McMahon KL, et al. Genetic and Environmental Contributions to Functional Connectivity Architecture of the Human Brain. Cerebral Cortex. 2016;26(5):2341–2352. [PMC free article] [PubMed] [Google Scholar]
24. Fears SC, Kremeyer B, Araya C, et al. Multisystem component phenotypes of bipolar disorder for genetic investigations of extended pedigrees. JAMA psychiatry. 2014;71(4):375–387. [PMC free article] [PubMed] [Google Scholar]
25. Bohlken MM, Brouwer RM, Mandl RC, et al. Structural brain connectivity as a genetic marker for schizophrenia. JAMA psychiatry. 2016;73(1):11–19. [PubMed] [Google Scholar]
26. Bertisch H, Li D, Hoptman MJ, DeLisi LE. Heritability estimates for cognitive factors and brain white matter integrity as markers of schizophrenia. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics. 2010;153(4):885–894. [PMC free article] [PubMed] [Google Scholar]
27. Damoiseaux J, Rombouts S, Barkhof F, et al. Consistent resting-state networks across healthy subjects. Proceedings of the National Academy of Sciences. 2006;103(37):13848–13853. [PMC free article] [PubMed] [Google Scholar]
28. Greicius MD, Krasnow B, Reiss AL, Menon V. Functional connectivity in the resting brain: A network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences. 2003;100(1):253–258. [PMC free article] [PubMed] [Google Scholar]
29. Sripada CS, Kessler D, Angstadt M. Lag in maturation of the brain’s intrinsic functional architecture in attention-deficit/hyperactivity disorder. Proceedings of the National Academy of Sciences. 2014;111(39):14259–14264. [PMC free article] [PubMed] [Google Scholar]
30. Sonuga-Barke EJS, Castellanos FX. Spontaneous attentional fluctuations in impaired states and pathological conditions: a neurobiological hypothesis. Neuroscience & Biobehavioral Reviews. 2007;31(7):977–986. [PubMed] [Google Scholar]
31. Simon V, Czobor P, Balint S, Meszaros A, Bitter I. Prevalence and correlates of adult attention-deficit hyperactivity disorder: meta-analysis. The British Journal of Psychiatry. 2009;194(3):204–211. [PubMed] [Google Scholar]
32. Polanczyk G, de Lima M, Bernardo H, Biederman J, Rohde L. The Worldwide Prevalence of ADHD: A Systematic Review and Metaregression Analysis. American Journal of Psychiatry. 2007;164(6):942–948. [PubMed] [Google Scholar]
33. Reich W Diagnostic interview for children and adolescents (DICA). Journal of the American Academy of Child & Adolescent Psychiatry. 2000;39(1):59–66. [PubMed] [Google Scholar]
34. Sarlls JE PS, Adelman N, Roopchansignh V. Straightforward Method to Improve Sensitivity in Diffusion Imaging Studies of Subjects Who Move. Proceedings of the XX International Society for Magnetic Resonance in Medicine; 2012; Melbourne, Australia. [Google Scholar]
35. Zhang H, Avants BB, Yushkevich PA, et al. High-Dimensional Spatial Normalization of Diffusion Tensor Images Improves the Detection of White Matter Differences: An Example Study Using Amyotrophic Lateral Sclerosis. Medical Imaging, IEEE Transactions on. 2007;26(11):1585–1597. [PubMed] [Google Scholar]
36. Zhang H, Awate SP, Das SR, et al. A tract-specific framework for white matter morphometry combining macroscopic and microscopic tract features. Medical Image Analysis. 2010;14(5):666–673. [PMC free article] [PubMed] [Google Scholar]
37. Cox RW. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical research. 1996;29(3):162–173. [PubMed] [Google Scholar]
38. Holmes CJ, Hoge R, Collins L, Woods R, Toga AW, Evans AC. Enhancement of MR images using registration for signal averaging. J Comput Assist Tomogr. 1998;22(2):324–333. [PubMed] [Google Scholar]
39. Jo HJ, Saad ZS, Simmons WK, Milbury LA, Cox RW. Mapping sources of correlation in resting state FMRI, with artifact detection and removal. Neuroimage. 2010;52(2):571–582. [PMC free article] [PubMed] [Google Scholar]
40. Beckmann CF, DeLuca M, Devlin JT, Smith SM. Investigations into resting-state connectivity using independent component analysis. Philosophical Transactions of the Royal Society of London B: Biological Sciences. 2005;360(1457):1001–1013. [PMC free article] [PubMed] [Google Scholar]
41. Yeo RA, Hill DE, Campbell RA, et al. Proton magnetic resonance spectroscopy investigation of the right frontal lobe in children with attention-deficit/hyperactivity disorder. Journal of the American Academy of Child & Adolescent Psychiatry. 2003;42(3):303–310. [PubMed] [Google Scholar]
42. Beckmann CF, Mackay CE, Filippini N, Smith SM. Group comparison of resting-state FMRI data using multi-subject ICA and dual regression. Neuroimage. 2009;47(Suppl 1):S148. [Google Scholar]
43. Almasy L, Blangero J. Multipoint Quantitative-Trait Linkage Analysis in General Pedigrees. The American Journal of Human Genetics. 1998;62(5):1198–1211. [PMC free article] [PubMed] [Google Scholar]
44. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B. 1995;57:289–300. [Google Scholar]
45. De Schotten MT, Dell’Acqua F, Forkel SJ, et al. A lateralized brain network for visuospatial attention. Nature neuroscience. 2011;14(10):1245–1246. [PubMed] [Google Scholar]
46. Klarborg B, Skak Madsen K, Vestergaard M, Skimminge A, Jernigan TL, Baaré WF. Sustained attention is associated with right superior longitudinal fasciculus and superior parietal white matter microstructure in children. Human brain mapping. 2013;34(12):3216–3232. [PMC free article] [PubMed] [Google Scholar]
47. Vestergaard M, Madsen KS, Baaré WF, et al. White matter microstructure in superior longitudinal fasciculus associated with spatial working memory performance in children. Journal of Cognitive Neuroscience. 2011;23(9):2135–2146. [PubMed] [Google Scholar]
48. Urger SE, De Bellis MD, Hooper SR, Woolley DP, Chen SD, Provenzale J. The Superior Longitudinal Fasciculus in Typically Developing Children and Adolescents Diffusion Tensor Imaging and Neuropsychological Correlates. Journal of child neurology. 2014:0883073813520503. [PMC free article] [PubMed] [Google Scholar]
49. Shinoura N, Suzuki Y, Yamada R, Tabei Y, Saito K, Yagi K. Damage to the right superior longitudinal fasciculus in the inferior parietal lobe plays a role in spatial neglect. Neuropsychologia. 2009;47(12):2600–2603. [PubMed] [Google Scholar]
50. Kinoshita M, Nakajima R, Shinohara H, et al. Chronic spatial working memory deficit associated with the superior longitudinal fasciculus: a study using voxel-based lesion-symptom mapping and intraoperative direct stimulation in right prefrontal glioma surgery. Journal of neurosurgery. 2016:1–9. [PubMed] [Google Scholar]
51. Sarubbo S, De Benedictis A, Maldonado IL, Basso G, Duffau H. Frontal terminations for the inferior fronto-occipital fascicle: anatomical dissection, DTI study and functional considerations on a multi-component bundle. Brain Structure and Function. 2013;218(1):21–37. [PubMed] [Google Scholar]
52. Martino J, Brogna C, Robles SG, Vergani F, Duffau H. Anatomic dissection of the inferior fronto-occipital fasciculus revisited in the lights of brain stimulation data. Cortex. 2010;46(5):691–699. [PubMed] [Google Scholar]
53. Petersen SE, Posner MI. The Attention System of the Human Brain: 20 Years After. Annual Review of Neuroscience. 2012;35(1):73–89. [PMC free article] [PubMed] [Google Scholar]
54. Cortese S, Imperati D, Zhou J, et al. White matter alterations at 33-year follow-up in adults with childhood attention-deficit/hyperactivity disorder. Biological Psychiatry. 2013;74(8):591–598. [PMC free article] [PubMed] [Google Scholar]
55. Doricchi F, Thiebaut de Schotten M, Tomaiuolo F, Bartolomeo P. White matter (dis)connections and gray matter (dys)functions in visual neglect: Gaining insights into the brain networks of spatial awareness. Cortex. 2008;44(8):983–995. [PubMed] [Google Scholar]
56. Epelbaum S, Pinel P, Gaillard R, et al. Pure alexia as a disconnection syndrome: new diffusion imaging evidence for an old concept. Cortex. 2008;44(8):962–974. [PubMed] [Google Scholar]
57. Cremers LGM, de Groot M, Hofman A, et al. Altered tract-specific white matter microstructure is related to poorer cognitive performance: The Rotterdam Study. Neurobiol Aging. 2016;39:108–117. [PubMed] [Google Scholar]
58. Voineskos AN, Rajji TK, Lobaugh NJ, et al. Age-related decline in white matter tract integrity and cognitive performance: A DTI tractography and structural equation modeling study. Neurobiol Aging. 2012;33(1):21–34. [PMC free article] [PubMed] [Google Scholar]
Feedback