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Hum Brain Mapp. Author manuscript; available in PMC 2017 October 31. Published in final edited form as: Hum Brain Mapp. 2016 September ; 37(9): 3323–3336. doi:10.1002/hbm.23243.

Unique white matter microstructural patterns in ADHD presentations - a diffusion tensor imaging study Alena Svatkova1,2, Igor Nestrasil1, Kyle Rudser3, Jodene Goldenring Fine4, Jesse Bledsoe5, and Margaret Semrud-Clikeman1 1Department

of Pediatrics, University of Minnesota, Minneapolis, MN, USA

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2Multimodal

and Functional Neuroimaging Research Group, CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic

3Division

of Biostatistics, University of Minnesota, Minneapolis, MN, USA

4Department

of Counseling, Educational Psychology, and Special Education, Michigan State University, East Lansing, MI, USA 5Department

of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, WA, USA

Abstract

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Attention-deficit/hyperactivity disorder predominantly inattentive (ADHD-PI) and combined (ADHD-C) presentations are likely distinct disorders that differ neuroanatomically, neurochemically, and neuropsychologically. However, to date, little is known about specific white matter (WM) regions differentiating ADHD presentations. This study examined differences in WM microstructure using diffusion tensor imaging (DTI) data from 20 ADHD-PI, 18 ADHD-C, and 27 typically developed children. Voxel-wise analysis of DTI measurements in major fiber bundles was carried out using tract-based spatial statistics (TBSS). Clusters showing diffusivity abnormalities were used as regions of interest for regression analysis between fractional anisotropy (FA) and neuropsychological outcomes.

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Compared to neurotypicals, ADHD-PI children showed higher FA in the anterior thalamic radiations (ATR), bilateral inferior longitudinal fasciculus (ILF) and in the left corticospinal tract (CST). In contrast, the ADHD-C group exhibited higher FA in the bilateral cingulum bundle (CB). In the ADHD-PI group, differences in FA in the left ILF and ATR were accompanied by axial diffusivity (AD) abnormalities. In addition, the ADHD-PI group exhibited atypical mean diffusivity in the forceps minor (FMi) and left ATR and AD differences in right CB compared to healthy subjects. Direct comparison between ADHD presentations demonstrated radial diffusivity differences in FMi. WM clusters with FA irregularities in ADHD were associated with neurobehavioral performance across groups. In conclusion, differences in white matter microstructure in ADHD presentations strengthen the theory that ADHD-PI and ADHD-C are two distinct disorders. Regions with white matter

Corresponding author: Alena Svatkova, 717 Delaware St. SE, 353-09, Minneapolis, MN 55414, [email protected] FINANCIAL DISCLOSURE: The authors report no biomedical financial interests or potential conflicts of interest.

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irregularity seen in both ADHD presentations might serve as predictors of executive and behavioral functioning across groups.

Keywords ADHD preferentially inattentive; ADHD combined; white matter microstructure; fractional anisotropy; executive and behavioral performance

INTRODUCTION

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Attention-deficit/hyperactivity disorder (ADHD), characterized by inattention and/or impulsivity-hyperactivity, is the most common neurodevelopmental disorder, diagnosed approximately in 5–10% of children[American Psychiatric Association, 2013; Polanczyk et al., 2007] DSM-5[American Psychiatric Association, 2013] recognizes three presentations of ADHD: combined type (ADHD-C) and predominantly inattentive type (ADHD-PI) as the two most common presentations and predominantly hyperactive/impulsive presentation (ADHD-HI) that is less often diagnosed.

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Aside from the distinction between diagnostic criteria, differences have been identified in many other areas including neuropsychology, neuroanatomy, and neurochemistry.[Baeyens et al., 2006] There continues to be debate whether ADHD-PI and ADHD-C are distinct unassociated disorders or are variants.[Baeyens et al., 2006] From a neuropsychological point of view, ADHD-PI is characterized by sluggish cognitive tempo and difficulties with selective attention and processing speed, while children with ADHD-C show difficulties with impulsivity, activity level, and sustained attention.[Barkley, 2014; McBurnett et al., 2001] Generally, the behavior and cognitive skills found in children with ADHD are modulated by the levels of activity in neural networks.[Sagvolden et al., 2005] The neuromodulator regulatory actions in the fronto-striato-thalamic connections play a critical role in the control of cognitive activity. These areas plus the frontal, limbic, and motor circuits have been implicated to contribute to behavioral problems experienced by children with ADHD. [Sagvolden et al., 2005] The significant impact of structures underlying these circuits has been confirmed by volumetric and shape abnormalities in the frontal, temporal, parietal, and basal ganglia regions as well as in limbic structures.[Ellison-Wright et al., 2008; Frodl and Skokauskas, 2012; Sowell et al., 2003]

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In agreement with the postulated theory of distinct phenotypes, ADHD-PI and ADHD-C presentations have shown overlapping, but unique resting state connectivity patterns.[Fair et al., 2012] Children with ADHD-PI show abnormalities preferentially in cognitive control systems (e.g., dorsolateral prefrontal cortex and cerebellum). The ADHD-C presentation has been localized to the DMN.[Fair et al., 2012] Differences in activation during inhibition tasks[Solanto et al., 2009] or reward processing[Edel et al., 2013] in task-related fMRI studies have also been found between the groups. Moreover, a recent volumetric study demonstrated a decrease in ACC and caudate volume in children with ADHD-C compared

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to both controls and those diagnosed with ADHD-PI and further emphasized importance of these regions in neurobehavioral performances in ADHD.[Semrud-Clikeman et al., 2014]

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Diffusion tensor imaging (DTI) is a tool that provides a comprehensive description of white matter (WM) organization between structurally connected gray matter regions.[Alexander et al., 2007; Basser and Pierpaoli, 2011; Mori and Zhang, 2006] DTI quantification is based on a mathematical calculation of three-dimensional ellipsoid derived from diffusion parameters (eigenvalues) in individual voxels.[Pierpaoli and Basser, 1996] Fractional anisotropy (FA) represents degree of anisotropy in the tissue that is assessed from divergence between diffusion parallel and perpendicular to the principal axon axis[Basser, 1995] and is therefore affected by degree of myelination, axonal packing and axon size, and/or coherence and colinearity of fiber organization.[Alexander et al., 2007; van den Heuvel et al., 2015; Mori and Zhang, 2006] FA ranges from values close to 0 in the tissues with no boundaries for water movement – such as the cerebrospinal fluid (CSF) to values around 1 in highly anisotropic tissues such as corpus callosum with parallel and highly organized fiber structure.[Pierpaoli and Basser, 1996] While smaller FA values in the white matter are considered to reflect white matter alteration,[Alexander et al., 2007] they might be naturally low in the areas with crossing fibers.[Jones et al., 2012] On the contrary, higher FA values were reported in disorders with expected WM disruptions such as Williams syndrome,[Hoeft et al., 2007] bipolar disorder,[Yurgelun-Todd et al., 2007] and ADHD subjects,[Silk et al., 2009] suggesting that increased FA might also indicate white matter pathology. Several explanations such as abnormal extent of myelination, reduced fiber crossing and/or increased fiber organization have been suggested to explain FA elevation.[Li et al., 2010; Silk et al., 2009] In general, FA is considered extremely sensitive, but a less specific marker of white matter microstructural changes.[Alexander et al., 2007; Basser, 1995] Thus careful interpretation with respect to other diffusion metrics i.e. mean (MD), axial (AD) and radial diffusivity (RD) is necessary.

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In ADHD, DTI findings have remained inconclusive, reporting both increase[Peterson et al., 2011; Silk et al., 2009; Tamm et al., 2012] and decrease[Ashtari et al., 2005; Lawrence et al., 2013; Makris et al., 2008] in FA in patients with respect to healthy individuals. Because most of the previous studies either have not separated ADHD patients into clinical presentations or studied the ADHD-C group exclusively, methodological and cohort differences should be considered as a plausible cause of these discrepancies. A recent study using DTI found an increase in FA in the sagittal striatum specifically exclusively among those with the ADHD-PI when compared to healthy subjects.[Peterson et al., 1999] Although indirect evidence pointed to potential divergence in WM microstructure between ADHD subgroups, so far only a few studies have focused on a description of WM abnormalities distinguishing ADHD presentations. One study, which estimated DTI parameters in ADHD-C and ADHD-PI separately, introduced evidence of differences in white matter organization between typically developed children and the two main ADHD presentations.[Lei et al., 2014] Hong et al. revealed decreased structural connectivity in ADHD-C comparing to ADHD-PI using network-based analysis in the neuronal circuits in the right hemisphere.[Hong et al., 2014]

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Our study evaluated DTI in two groups of children with ADHD to compare white matter patterns and their microstructural substrates using tract-based spatial statistics tool (TBSS). [Smith et al., 2006] TBSS overcomes limitations introduced in other voxel-based approaches and provides improved sensitivity and interpretability of voxel-by-voxel comparison that might address previous inconsistencies in ADHD research. Our second goal was to relate white matter regions that are implicated in ADHD to attention and impulsivity; core features of ADHD. We hypothesized microstructural white matter differences between ADHD presentations and also typically developed children. In addition, we hypothesized that regions showing white matter abnormalities in ADHD have neural underpinnings for neuropsychological outcomes.

MATERIALS AND METHODS Author Manuscript

Participants

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A total of 65 children completed a DTI scan as well as selected neuropsychological measures. Movement artifacts excluded 7 DTI scans (5 ADHD-C, 2 control subjects) and signal dropouts excluded 2 controls. Three groups of children: 20 ADHD-PI (mean age 180±29 months; 16 males/ 7 females), 13 ADHD-C (mean age153±28 months; 10 males/ 3 females) and 23 controls (mean age 170±34 months; 16 males/ 4 females) with high-quality DTI scans were analyzed (Table 1). Our study was part of a larger multimodal MRI study. Volumetric results were published in Semrud-Clikeman et al. (2014)[Semrud-Clikeman et al., 2014]. This study did not demonstrate significant differences in total brain and white matter volumes between healthy individuals and ADHD presentations, although it showed significantly smaller volumes of the anterior cingulate cortex (ACC) and caudate nucleus in ADHD-C compared to ADHD-PI and healthy controls.[Semrud-Clikeman et al., 2014] Participants had no history of other psychiatric disorder, learning disability, traumatic brain injury, or seizure. All participants were right-handed and were required to also show a fullscale IQ above 80 on the Wechsler Abbreviated Scale of Intelligence.[Psychological Corporation, 2002] Patients met the DSM-IV-TR[American Psychiatric Association, 2000] criteria for either ADHD-C or ADHD-PI subgroup. In retrospect, they would continue to meet the DSM-V criteria.[American Psychiatric Association, 2013] Parents signed consent for the child to participate and the children signed assents as appropriate. This study was approved by the appropriate institutional review board.

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All subjects in the study were native English speakers. 17,8% of participants (2 controls, 4 ADHD-PI, 4 ADHD-C) were self-declared minorities. A total of 7 patients in the ADHD-C group and 14 in the ADHD-PI group had stimulant history. Neuropsychological measures All subjects completed a comprehensive neuropsychological battery conducted by fully trained doctoral-level graduate students under the supervision of a licensed neuropsychologist (MSC).

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Inhibitory control was measured using the Color-Word subtest from the Delis-Kaplan Test of Executive Function, which consists of 3 subtasks.[Delis, DC., Kaplan, E., Kramer, 2001] The first subtest shows names of colors printed in black ink in random order. The second one displays 1 of 4 basic colors. The third task consists of 5 rows of 10 words printed in ink that differs from the actual word and the child is to read the color, not the word. Specifically, the aim is to inhibit the habitual response of reading the word and focus on the color of the ink instead. The interference score is calculated by subtracting average time necessary for completing the third task from the sum of time needed for first and second subtasks and is considered a reliable measure of executive functioning and cognitive flexibility.[Van der Elst, 2006]

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The Behavior Rating Inventory of Executive Function (BRIEF)[Jarratt et al., 2005] parent and teacher form represents a scale for executive function measurement, including 86 items, providing a score on 8 clinical scales and three broad indexes.[Jarratt et al., 2005]. The Global Executive Composite (GEC) was used in this study and is the main index combining ratings of inhibition, shift, emotional control, working memory, skills to initiate, plan/ organize and monitor performances together with ability to keep materials and spaces organized.[Sullivan and Riccio, 2007] A semi-structured interview was conducted with each parent (SIDAC - Structured Interview for Diagnostic Assessment of Children for DSM-IV)[American Psychiatric Association, 2000] to assess the severity and presence of ADHD symptoms (inattention, impulsivity, and hyperactivity). The SIDAC has been used in several studies and is an adaptation of the Kiddie-SADS.[Kaufman et al., 1997] DTI acquisition and analysis

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DTI data were acquired on the 3T GE SIGNA scanner (GE Healthcare, Waukesha, WI) with 25 directions (b=1000s/mm2) and 1 unweighted B0 scans (b= 0 s/mm2), TR (repetition time) = 10200 ms, TE (echo time) = 75.9 ms, flip angle = 90°, FOV (field-of-view)= 220×220, voxel size 0.859×0.859×3mm3, 38 slices, no gap.

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Data were post-processed using FSL software version 5.0.6 (FMRIB Software Library, http://www.fmrib.ox.ac.uk/fsl).[Jenkinson et al., 2012] After motion, ECC (eddy-current) correction and careful brain-extraction (BET), images were fitted into a tensor model in order to calculate fractional anisotropy (FA), mean diffusivity (MD), axial (AD) and radial diffusivity (RD) images. The color-coded FA maps were visually checked for movement artifacts or occasional signal dropouts. In addition, movement of each diffusion-weighted scan in the session relatively to the first (diffusion unweighted) scan was calculated. Averaged movement values in mm for x-, y-, z- axes were quantified per subjects and only subjects with average movement less then 0.6 mm in x- and y-axis and 1.2 mm in z-axis were included. Tract-based spatial statistics (TBSS)[Smith et al., 2006] was used for diffusion parameters analysis. Firstly, all FA images were registered into a common space using nonlinear registration. A study-specific child template was used as a registration target image. Next, the transformed images were averaged to create a mean FA image and then a study-specific

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skeleton was generated. The FA threshold was set up to 0.3 in order to exclude grey matter regions and cerebrospinal fluid from the analysis. The JHU-ICBM-tract atlas[Wakana et al., 2007] was used to define regions of interest (ROI) for major fiber bundles that have previously demonstrated importance in ADHD.[Chuang et al., 2013; Lawrence et al., 2013; Silk et al., 2009; Tamm et al., 2012] FSLmaths was used to separate the FA-skeleton voxels within the specific tract ROIs. Specifically, corticospinal tracts (CST), superior longitudinal fascicles (SLF), inferior longitudinal fascicles (ILF), inferior fronto-occipital fascicles (IFOF), uncinate fascicles (UF), cingulum bundles (CB), and anterior thalamic radiations (ATR) from both hemispheres, together with the forceps major (FMa) and forceps minor (FMi), were analyzed. Statistical analysis

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Demographic and clinical characteristics were summarized by group (ADHD-PI, ADHD-C, and healthy controls) with means and standard deviations (Table 1). Differences in white matter DTI metrics were evaluated based on a nonparametric voxel–wise statistical analysis of the skeleton-voxels within separate tracts using threshold-free cluster enhancement (TFCE)[Smith and Nichols, 2009] method in the ‘randomize’ program of FSL (pADHD-PI) and in the FMi (ADHD-PI>C), b) abnormalities in AD in the left ILF and the right CB (ADHD-PI>C), c) local mean differences between ADHD-PI and ADHD-C in RD in the FMi (ADHD-PI>ADHD-C), d) irregularities in MD in the FMi in 3D view. Significant voxels were spatially smoothed using ‘fill’ tool in TBSS to enhance visualization of the results. ADHD-PI – ADHD preferentially inattentive, ADHD-C – ADHD combined C – healthy individuals (controls), ILF – inferior longitudinal fascicle ATR – anterior thalamic radiation, FMi - forceps minor

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Figure 3. Linear regression graphs

Solid trend-lines demonstrate associations between WM clusters altered in the ADHD disorder with neuropsychological measurements across groups. Dashed lines represent relationships for the ADHD-C, ADHD-PI and control groups separately. BRIEF GEC – General executive component, SIDAC - Structured Interview for Diagnostic Assessment of Children for DSM-IV, ILF – inferior longitudinal fascicle, ATR – anterior thalamic radiation

Author Manuscript Hum Brain Mapp. Author manuscript; available in PMC 2017 October 31.

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Author Manuscript 8.0 (1.5) 4.1 (1.9) 2.1 (1.0)

1.7 (2.7) 0.7 (1.2) 0.4 (0.7)

Hyperactivity Impulsivity

SIDAC

75.8 (9.6)

48.6 (13.9)

GEC Attention

8.6 (2.5)

11.7 (1.5)

BRIEF

109.5 (7.6)

110.4 (9.2)

Interference

10/3

16/7

Sex (M/F)

Stroop

153.4 (27.9)

169.8 (34.1)

Age (Months)

FSIQ

13

Mean (SD)

WASI

Sample Descriptors

Groups ADHD-C

23

N

C

0.9 (1.0)

1.4 (1.38)

7.8 (2.2)

69.9 (12.1)

9.4 (2.9)

103.9 (11.2)

16/4

179.5 (28.5)

20

ADHD-PI

−1.7 (−2.4, −1.0)

−3.4 (−4.7, −2.2)

−6.3 (−7.7, −4.8)

−27.1 (−35.3, −19)

3.1 (1.5, 4.7)

0.9 (−4.9, 6.7)

mean diff (95% CI)

ADHD-C vs. C

Unique white matter microstructural patterns in ADHD presentations-a diffusion tensor imaging study.

Attention-deficit/hyperactivity disorder predominantly inattentive (ADHD-PI) and combined (ADHD-C) presentations are likely distinct disorders that di...
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