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\begin{document}

\title{White matter connectivity differences converge with gene expression in a neurodevelopmental disorder of known genetic origin}

\author{Joe Bathelt\\ Affiliation not available }


\maketitle




\section{Abstract}
Knowledge of genetic cause in neurodevelopmental disorders can highlight molecular and cellular processes critical for typical development. Furthermore, the relative homogeneity of neurodevelopmental disorders of known genetic origin allows the researcher to establish the subsequent neurobiological processes that mediate cognitive and behavioural outcomes. The current study investigated white matter structural connectivity in a group of individuals with intellectual disability due to mutations in \textit{ZDHHC9}. In addition to shared cause of cognitive impairment, these individuals have a shared cognitive profile, involving oro-motor control difficulties and expressive language impairment. Analysis of structural network properties using graph theory measures showed global reductions in mean clustering coefficient and efficiency in the \textit{ZDHHC9} group, with maximal differences in frontal and parietal areas. Regional variation in clustering coefficient across cortical regions in \textit{ZDHHC9} mutation cases was significantly associated with known pattern of expression of \textit{ZDHHC9} in the normal adult human brain. The results demonstrate that a mutation in a single gene impacts upon white matter organisation across the whole-brain, but also shows regionally specific effects, according to variation in gene expression. Furthermore, these regionally specific patterns may link to specific developmental mechanisms, and correspond to specific cognitive deficits. 

\section{Introduction}
Many cognitive and psychiatric disorders are highly heritable \cite{Lee_2015,Haworth_2009}. In some cases, genetic risk factors have been identified, but understanding the neural mechanisms linking altered gene transcripts to cognitive or behavioural outcomes remains challenging. One reason for this is the heterogenous nature of the vast majority of these disorders, which presents a major challenge to establishing the neural endophenotypes that mediate any gene-cognition relationships; any group defined on the basis of a cognitive impairment or behavioural difficulty will likely contain individuals with different genetic and neural causes, making it difficult to identify mechanisms at the group level. One promising approach has been to study neuroanatomical differences in groups of individuals that have rare but clearly defined genetic causes of those impairments \cite{Meyer_Lindenberg_2009, Griffa_2013}. These groups, whilst necessarily small in size, have an homogenous aetiology. Studying these groups can therefore provide a powerful means for identifying the neurobiological pathways that potentially mediate cognitive and behavioural phenotypes in the wider population. For instance, the study of a rare familial speech disorder (KE family, FOXP2 mutation) highlighted the importance of striatal networks for emergent higher-order language skills \cite{Watkins_2011,Liegeois_2011}.


However, studies of brain differences have mainly focussed on focal differences in brain areas or white matter tracts that show the most pronounced group differences. This is true of both genetically defined group comparisons and case-control designs more generally. However, genetic differences are likely to have wide-ranging effects on the organisation of neural ensembles across many areas. To explore this fully requires a more advanced network science approach, cable of establishing how organisational principles differ across groups of individuals \cite{Petersen_2015, Meyer_Lindenberg_2009}. We take this approach here.  


In a network analysis brain regions are described as a nodes and their connections as edges. Nodes typically correspond to regions of interest \cite{Dell_Acqua_2012,Fornito_2015}. In the current study, edges represented fractional anisotropy (FA) of white matter connections between the regions. FA indicates anisotropic diffusion of water \cite{Alexander_2011}. Higher levels of FA are associated with more ordered organisation of axons and increased myelination \cite{Feldman_2010}.  Higher values of FA have been linked to higher cognitive performance \cite{Navas_S_nchez_2013, Clayden_2011} and lower levels to white matter damage in patient studies \cite{Verstraete_2013, Kubicki_2005}. Organisational principles of the structural brain network can be quantified using graph theory \cite{Bullmore_2009,Rubinov_2010}. These approaches have been used to study typical and atypical brains across the lifespan \cite{Griffa_2013,Hagmann_2012,Di_Martino_2014,Deco_2014}. 

A few studies have employed a network analysis approach to investigate how genetic differences may influence brain organisation \cite{Ottet_2013,Meoded_2014,Hong_2014,Leow_2014, Bruno_2016}. These studies focused on common variants of trophic factor genes \cite{Meoded_2014}, genes involved the regulation of synaptic weights \cite{Ottet_2013,Meoded_2014,Hong_2014}, and mutations associated with specific phenotypes \cite{Leow_2014,Bruno_2016}. Genetic differences were associated with differences in structural brain network organisation, with specific effects for each genetic factor. This suggests that studying differences in brain organisation may offer important insight into understanding the effects of genetic variation. 


In the present study we take a network analysis approach to studying brain organisation in a genetically defined neurodevelopmental disorder. Mutations in \textit{ZDHHC9} are a recurrent cause of X-linked Intellectual Disability (XLID) \cite{Raymond_2007}. The \textit{ZDHHC9} gene codes for a palmitoylation enzyme, involved in post-translational modification of specific target substrates. Palmitoylation plays an important role in the recruitment of receptors and ion channels at the synapse \cite{Topinka_1998,Young_2013,ElHusseini_2000}. A systematic assessment of clinical history and cognitive deficits across multiple XLID-associated genes led to the observation that \textit{ZDHHC9} mutations are associated with homogeneous neurological and cognitive features, including disproportionate attention problems, language impairment, and deficits in oro-motor control in the context of mild to moderate intellectual disability \cite{Baker_2015}. The majority of affected individuals also had a history of epilepsy that resembled Rolandic epilepsy in presentation and spike topography \cite{Baker_2015}. Previous neuroimaging work in our group investigated focal differences in brain structure in \textit{ZDHHC9} cases. These studies indicated differences in subcortical volumes (thalamus, putamen, caudate nucleus) and hypoplasia of the corpus callosum \cite{Baker_2015}. Reductions in cortical thickness were found that were most pronounced in areas around the temporo-parietal junctions and inferior frontal lobe \cite{Bathelt_2016}. Mutation of \textit{ZDHHC9} was also associated with reductions in white matter structural integrity involving cortical, cortico-subcortical, and interhemispheric tractrs \cite{Bathelt_2016}. 


Given strong evidence for pervasive effects on white matter integrity, we expected the \textit{ZDHHC9} mutation would have an impact on structural brain organisation. More specifically, we predicted that in addition to any global impact of gene mutation, we ought to observe some regional specificity, according to variability in the expression of that gene across the brain. This regional specificity may correspond to the areas of most marked cognitive impairment resulting from the mutation, and overlap with other genes known to impact upon similar developmental mechanisms. In short, across our analyses we explored how both a mutation to, and regional expression of, \textit{ZDHHC9} are associated with structural brain organisation.



\section{Participants and Methods}

\subsection{Participants }
The study compared 7 males with inherited loss of function mutations in the \textit{ZDHHC9} gene (Age in years: mean=29.13, SE=4.86, Range=13.83-41.83) to 7 males individually matched in age +/- 2 years (Age in years: mean=27.23, SE=5.31, Range=10.17-42.5). Comparison subjects had no history of neurological illness or cognitive impairment. Statistical analysis indicated no significant difference in age between the groups (Welch-corrected t-test: t(11.91)=-0.265, p=0.796).

For detailed description of clinical and cognitive characteristics of the \textit{ZDHHC9} group see Baker et al. 2015. In summary, all individuals with a \textit{ZDHHC9} mutation had mild to moderate intellectual disability (full-scale IQ: mean=64.86, SE=2.32, Range=57-73). 5 individuals had a history of epilepsy, with seizure characteristics and EEG features similar to the Rolandic epilepsy spectrum. At the time of MRI acquisition, 1 participant reported seizures within the previous 3 months, and 3 currently received anti-epileptic medication (carbemazapine n=1, carbemazapine and lamotrigine n=1, phenytoin n=1). ). 
Vineland scores \cite{Sparrow_2005} indicated stronger receptive language abilities compared to expressive and written language abilities in the \textit{ZDHHC9} group.  The Verbal Motor Production Assessment for Children (VMPAC) \cite{Hayden_1999}. indicated significant oromotor difficulties in the \textit{ZDHHC9} group, including deficits in oral control, sequencing, voice characteristics, and connected speech. Inhibitory control was also reduced in the \textit{ZDHHC9} group on a visual attention task. These specific features differentiated with \textit{ZDHHC9} group from age and IQ matched controls \cite{Baker_2015}. 





\subsection{MRI data acquisition}
Magnetic resonance imaging data was acquired at the MRC Cognition and Brain Sciences Unit, Cambridge U.K. All scans were obtained on the Siemens 3 T Tim Trio system (Siemens Healthcare, Erlangen, Germany) , using a 32-channel quadrature head coil. The imaging protocol consisted of two sequences: T1-weighted MRI and a diffusion-weighted sequence.

T1-weighted volume scans for surface analysis were acquired using a whole brain coverage 3D Magnetisation Prepared Rapid Acquisition Gradient Echo (MP RAGE) sequence acquired using 1mm isometric image resolution. Echo time was 2.98 ms, and repetition time was 2250 ms. T1-weighted volumes were visually inspected for motion artefacts and were deemed sufficient by a radiographer not involved in the study. 

Diffusion scans were acquired using echo-planar diffusion-weighted images with an isotropic set of 60 non-collinear directions, using a weighting factor of b = 1000 s mm-2, interleaved with 4 T2-weighted (b = 0) volumes. Whole brain coverage was obtained with 60 contiguous axial slices and isometric image resolution of 2mm. Echo time was 90 ms and repetition time was 8400 ms. 

Motion was quantified as the root-mean-square difference between volumes and the first volume in the diffusion sequence using the FSL's rmsdiff tool. The maximum displacement was under 3mm for both \textit{ZDHHC9} cases and controls (\textit{ZDHHC9}: mean=2.1, SE=0.292, median=1.97, mad=0.648; control: mean=1.8, SE=0.304, median=1.53, mad=0.314) and there was no significant difference between groups (Paired t-test: t(6)=-0.59, $p$=0.58; Wilcoxon signed-rank test: W=10, $p$=0.499). There were also no differences in the number of outliers identified by FSL eddy  \cite{Andersson_2016} (\textit{ZDHHC9}: mean=12.71, SE=2.254, median=11.0, mad=5.93; control: mean=10.0, SE=3.078, median=8.0, mad=5.93; paired t-test: t(6)=-1.09, $p$=0.317; Wilcoxon signed-rank test: W=8, $p$=0.311). 



\subsection{Structural connectome analysis}
The white-matter connectome reconstruction followed the general procedure of estimating the most probably white matter connections for each individual, and then obtaining measures of fractional anisotropy (FA) between  regions (see Figure \ref{figure:pipeline_overview} and \cite{Horn_2016}). The details of the procedure are described in the following paragraphs. 

In the current study, MRI scans were converted from the native DICOM to compressed NIfTI-1 format using the dcm2nii tool \url{http://www.mccauslandcenter.sc.edu/mricro/mricron/dcm2nii.html}. Subsequently, a brain mask was derived from the b0-weighted volume of the diffusion-weighted sequence and the entire sequence was submitted for correction for participant movement and eddy current distortions through FSL's eddy tool. Next, non-local means de-noising \cite{Coupe_2008} was applied using the Diffusion Imaging in Python (DiPy) v0.11 package \cite{Garyfallidis_2014} to boost signal to noise ratio. The diffusion tensor model was fitted to the pre-processed images to derive maps of fractional anisotropy (FA) using dtifit from the FMRIB Software Library (FSL) v.5.0.6 \cite{Behrens_2003}. 
A spherical constrained deconvolution (CSD) model \cite{Tournier_2008} was fitted to the 60 gradient direction diffusion-weighted images using a maximum harmonic order of 8 using DiPy. An alternative analysis with a constant solid angle (CSA) model is present in the Supplementary Materials section. Next, probablistic whole-brain tractography was performed based on the CSD model with 8 seeds in any voxel with a General FA value higher than 0.1. The step size was set to 0.5 and the maximum number of crossing fibres per voxel to 2. 

For ROI definition, T1-weighted images were preprocessed by adjusting the field of view using FSL's robustfov, non-local means denoising in DiPy, deriving a robust brain mask using the brain extraction algorithm of the Advanced Normalization Tools (ANTs) v1.9 \cite{avants2009advanced}, and submitting the images to recon-all pipeline in FreeSurfer v5.3 (\url{http://surfer.nmr.mgh.harvard.edu}). Regions of interests (ROIs) were based on the Desikan-Killiany parcellation of the MNI template \cite{Desikan_2006} with 34 cortical ROIs per hemisphere and  17 subcortical ROIs (brain stem, and bilateral cerebellum, thalamus, caudate, putamen, pallidum, hippocampus, amygdala, nucleus accumbens). The surface parcellation of the cortex was transformed to a volume using the aparc2aseg tool in FreeSurfer. Further, the cortical parcellation was expanded by 2mm into the subcortical white matter using in-house software. In order to move the parcellation into diffusion space, a transformation based on the T1-weighted volume and the b0-weighted image of the diffusion sequence was calculated using FreeSurfer's bbregister and applied to volume parcellation.  

For each pairwise combination of ROIs, the number of streamlines intersecting both ROIs was estimated and transformed to a density map. A symmetric intersection was used, i.e. streamlines starting and ending in each ROI were averaged. Spurious connections in streamline tractography are a common problem in structural connectome studies \cite{Zalesky_2016}. Typically, a threshold is applied to remove false positive streamlines. However, the choice of this cut-off is largely arbitrary. In order to remove the effect of setting any particular threshold, a range of thresholds was applied and the area under the curve for each metric was compared in subsequent analyses \cite{van_Wijk_2010}. 

The weight of the connection matrices was based on fractional anisotropy (FA). To obtain FA-weighted matrices, the streamline density maps were binarized after thresholding and multiplied with the FA map and averaged over voxels to obtain the FA value corresponding to the connection between the ROIs. This procedure was implemented in-house based on DiPy v0.11 functions \cite{Garyfallidis_2014}. Edge weights may be defined in different ways \cite{Qi_2015}, which may considerably influence the results of the analysis \cite{Fornito_2013}. Therefore, additional analyses were carried out with alternative edge weight definitions, i.e. streamline count, streamline count normalized by ROI size, and streamline count normalized by streamline length. These analyses confirmed the results of the main analysis. A detailed description can be found in the Supplementary Materials\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.7\columnwidth]{figures/Pipeline-Overview4/Pipeline-Overview4}
\caption{{\label{figure:pipeline_overview} Overview of the processing steps to derive the diffusion-weighted structural connectome%
}}
\end{center}
\end{figure}

\subsection{Graph theory}
Graph theory was employed to investigate differences in network architecture between the \textit{ZDHHC9} and control group. To this end, graph metrics were calculated in the python implementation of the Brain Connectivity Toolbox \url{https://sites.google.com/site/bctnet/}. Weighted undirected networks were used for all analyses. The weight represented the FA value in the structural connectome. A detailed description of commonly used graph theory metrics can be found elsewhere \cite{Bullmore_2009,Rubinov_2010}. 






\subsection{Allen Brain Atlas data}
Gene expression data were obtained from the Allen Brain Atlas Human Brain public database (\url{http://human.brain-map.org}). Gene expression data were based on microarray analysis of post-mortem tissue samples from 6 human donors between 18 and 68 years with no known history of neuropsychiatric or neurological conditions (see online documentation). MRIs and transformations from individual donors MR space to MNI coordinates were also obtained from the Allen Brain Atlas website. For the current investigation, expression values were averaged across donors and mapped onto areas of the Desikan-Killiany parcellation of the MNI brain as described by French and colleagues \cite{French_2015}. The current investigation focussed on the expression of \textit{ZDHHC9}. In order to investigate the specificity of the link of \textit{ZDHHC9} expression and structural connectome organisation, we compared \textit{ZDHHC9} to a number of other genes: First, \textit{GAPDH} was added as a control gene that is not associated with any known neurological or cognitive phenotype \cite{Nicholls_2012}. We then assessed genes that are associated with a similar mutation phenotype. For overlap with language deficits, \textit{FOXP2} was included \cite{Vargha_Khadem_2005}. \textit{FMR1} was selected as an X-linked intellectual disability gene \cite{Bourgeois_2009}. \textit{GRIN2A} was included for the association with Rolandic Epilepsy \cite{McTague_2016}. 




\subsection{Statistical analysis}
\subsubsection{Comparison of participant groups}
Participants in the \textit{ZDHHC9} and control group were matched on age ($\pm$2 years). Therefore, statistical comparisons were based on paired sample tests. Due to the rarity of single-gene disorders, the size of the sample was limited. Some controversy exists regarding optimal statistical procedures in small samples. Paired t-test comparisons are both robust to some violation of the normality assumption and to small sample sizes \cite{Fritz_2012, Campbell_1995, Bridge_1999}. In all cases we also tested for any deviation from the normality assumption, using the Shapiro-Wilk test, which provides the best sensitivity \cite{Razali_2011}. Bonferroni correction was also applied to correct for multiple comparisons. For topographical analysis, false discovery rate (FDR) correction using the Benjamini-Hochberg method was applied. This  maximizes power in the presence of a very large number of comparisons.

\subsubsection{Regional variation in graph measures and  association with gene expression and group-average graph metrics}
Differences in node-level graph metrics were compared between groups. Deviations from the normality assumption were very rare, being present for only 3 to 5\% of regions (Node degree: 3.53, Node strength: 5.88, Clustering coefficient: 3.53, Efficiency: 3.52). For this reason, we retained the paired-sample t-tests as our primary means of comparison -- the statistical sensitivity of this method is superior to the non-parametric alternatives -- but we disregarded those few instances where the normality assumptions were violated. 

The linear association between gene expression and group-average graph metrics was investigated with linear regression models. Separate simple regression models were fitted with the graph metric as the outcome and gene expression and an intercept term as the predictor (model: $Y_{GraphMetric} = \beta_{GeneExpression}x_{GeneExpression} + \beta_{Intercept}$). Bonferroni correction was used to correct for multiple comparisons arising from the number of groups (\textit{ZDHHC9}, control), the number of genes, and the number of graph metrics entered into the analysis. 

\section{Results}

The following section describes the results of the structural connectome comparison between the \textit{ZDHHC9} group and controls. The analysis first focused on regional differences in edge weight between the groups as a basic property of the network. Next, graph theory was employed to characterise connectivity principles of the networks. Last, the relationship between regional variation of \textit{ZDHHC9} expression and these connectivity properties was investigated. Illustrations of the topography of the structural network are presented in Figure \ref{figure:connectome_overview}). These illustrations were thresholded at a high cut-off to make the figure more readable. Unthresholded adjacency matrices of the group average networks can be found in the Supplementary Materials.\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.7\columnwidth]{figures/Connectome-Figure1/Connectome-Figure1}
\caption{{\label{figure:connectome_overview} Illustration of the FA-weighted structural connectome in the \textit{ZDHHC9} and control group. The connection matrix was thresholded at a high cut-off at FA$>$0.15 for illustration purposes.%
}}
\end{center}
\end{figure}

\subsection{Reduced regional edge weight in the \textit{ZDHHC9}}
Comparison of edge weights by region indicated a significantly lower edge weight in the \textit{ZDHHC9} group for subcortical-cortical, left hemisphere cortical, right hemisphere cortical, and interhemispheric connections (see Table \ref{figure:regional_node_strength} and Figure \ref{figure:regional_node_strength}).\selectlanguage{english}
\begin{table}
\begin{tabular}{lrrrrrrrrlllll}
\label{table:regional_node_strength}
\fontsize{6}
\toprule
{} & \multicolumn{4}{c}{\textit{ZDHHC9}} & \multicolumn{4}{c}{control} & & \\
{} & mean & SE & Shapiro-W & Shapiro-$p$ & mean & SE & Shapiro-W & Shapiro-$p$ & t(6) & $p$ & corr.-$p$ \\
\midrule
subcort. &         0.08 &      0.011 &      0.97 &      0.92 &          0.18 &       0.018 &       0.93 &       0.54 &        5.32 &    0.002 &        0.007 \\
               lh &         0.19 &      0.012 &      0.98 &      0.95 &          0.33 &       0.012 &       0.82 &       0.07 &       11.68 &    $<$0.001 &        $<$0.001 \\
               rh &         0.16 &      0.010 &      0.82 &      0.06 &          0.30 &       0.022 &       0.90 &       0.36 &        7.02 &    $<$0.001 &        0.002 \\
 interhem. &         0.01 &      0.004 &      0.84 &      0.11 &          0.06 &       0.005 &       0.91 &       0.38 &        8.76 &    $<$0.001 &        $<$0.001 \\
\bottomrule
\end{tabular}
\caption{{Comparison of edge weight for subcortical-cortical (subcort.), left-hemispheric (lh), right hemispheric (rh), and interhemispheric (interhem.) connections in the \textit{ZDHHC9} and control group.}}
\end{table}\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.7\columnwidth]{figures/regional-node-strength/regional-node-strength}
\caption{{\label{figure:regional_node_strength} Comparison of node strength between the \textit{ZDHHC9} and control group for left and right hemisphere connections, subcortical-cortical connections, and interhemisphere connections. The line indicates the median in each group. The error bars indicate the bootstrapped 95\% confidence interval around the median. The area under the curve across thresholds was used for statistical comparison between the groups to avoid the potential biasing effects of an arbitrarily selected threshold \protect\cite{van_Wijk_2010}.%
}}
\end{center}
\end{figure}

\subsection{Reductions in global graph metrics in the \textit{ZDHHC9} group}
Statistical comparison indicated significant differences in mean node degree and mean node strength. Global clustering coefficient and global efficiency are significantly influenced by node degree. In order to adjust for differences in network density, group-level consensus thresholding was applied, such that we only retained connections that were found in each participant \cite{de_Reus_2013, 2016}. Analysis of global clustering coefficient and global efficiency in the consensus-thresholded networks indicated a significant reduction in both metrics in the \textit{ZDHHC9} group (see Table \ref{table:graph_theory} and Figure \ref{figure:Global_graph_measures}).\selectlanguage{english}
\begin{table}
\label{table:graph_theory}
\begin{tabular}{lrrrrrrrrrllll}
\fontsize{6}
\toprule
{} & \multicolumn{4}{c}{\textit{ZDHHC9}} & \multicolumn{4}{c}{control} & & \\
{} & mean & SE & Shapiro-W & Shapiro-$p$ & mean & SE & Shapiro-W & Shapiro-$p$ & t(6) & $p$ & corr.-$p$ \\
\midrule
degree &        22.23 &      0.921 &      0.96 &      0.85 &         33.18 &       1.056 &        0.9 &       0.34 &       10.14 &        $<$0.001 &            $<$0.001 \\
strength &         9.57 &      0.487 &      0.97 &      0.86 &         16.34 &       0.645 &        0.9 &       0.31 &       13.08 &        $<$0.001 &            $<$0.001 \\
clust.coef. &         0.20 &      0.003 &      0.90 &      0.34 &          0.21 &       0.003 &       0.95 &       0.75 &        5.91 &    0.001 &        0.004 \\
efficiency &         0.18 &      0.003 &      0.85 &      0.12 &          0.20 &       0.002 &       0.90 &       0.32 &        5.81 &    0.001 &        0.005 \\
\bottomrule
\end{tabular}
\caption{{Comparison of graph metric between the \textit{ZDHHC9} and control group. Statistical comparison based on the area under the curve over different streamline count thresholds indicated lower node degree and node strength in the \textit{ZDHHC9} group. Comparison in group-consensus-thresholded networks also indicated a lower global clustering coefficient and lower global efficiency in the \textit{ZDHHC9} group.}}
\end{table}\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.7\columnwidth]{figures/GlobalGraphMeasures1/GlobalGraphMeasures1}
\caption{{\label{figure:Global_graph_measures} Comparison of global graph metrics between the \textit{ZDHHC9} (orange) and control group (blue) across a range of streamline thresholds for \textbf{a} mean node degree, \textbf{b} mean node strength, \textbf{c} clustering coefficient, \textbf{d} global efficiency. The line indicates the median value for each group. The error bars indicate the bootstrapped 95\% confidence itnerval around the median. Panels c and d show the results for the native networks (sold line) and for networks after groups consensus thresholding (dashed line).%
}}
\end{center}
\end{figure}

\subsection{Regional reductions in graph metrics in the \textit{ZDHHC9} group}
Regional comparison of node degree and node strength indicated reduction in the \textit{ZDHHC9} group for the brain stem, caudate and putamen (see Table \label{table:regional_graph_results} a and b). Cortical differences were found in areas of the left and right temporal lobe, parietal lobe, and frontal lobe (see Figure \ref{figure:graph_topography} a and b). Reductions in the local clustering coefficient in the \textit{ZDHCC9} group were found for the left inferior frontal gyrus right isthmus cingulate cortex (see Table \label{table:regional_graph_results} c and Figure \ref{figure:graph_topography} c). Local efficiency was found to be reduced around the right superior frontal cortex (see Table \label{table:regional_graph_results} d and Figure \ref{figure:graph_topography} d). There were no significant increases for any region or measure in the \textit{ZDHHC9} group compared to controls.\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.7\columnwidth]{figures/Regional-graph2/Regional-graph2}
\caption{{\label{figure:graph_topography}: Comparison between the \textit{ZDHHC9} and control group in node measures of \textbf{a} node degree, \textbf{b} node strength, \textbf{c} clustering coefficient, and \textbf{d} local efficiency. The maps show p-values of paired-sample t-tests corrected for multiple comparison using false discovery rate (FDR).%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.7\columnwidth]{figures/Regional-graph1/Regional-graph}

\end{center}
\end{figure}\selectlanguage{english}
\begin{table}
\begin{tabular}{lrrrrrrrrrrr}
Node degree \\
\toprule
{} & \multicolumn{4}{c}{\textit{ZDHHC9}} & \multicolumn{4}{c}{control} & & \\
{} & mean & SE & Shapiro-W & Shapiro-p & mean & SE & Shapiro-W & Shapiro-p & t(6) & $p$ & corr.-$p$ \\
\midrule
           Left-Caudate &        26.71 &      3.220 &      0.87 &     0.193 &         44.57 &       2.861 &       0.88 &      0.241 &     4.14 &    0.006 &        0.031 \\
          Right-Caudate &        19.29 &      2.661 &      0.83 &     0.088 &         33.86 &       2.586 &       0.92 &      0.472 &     4.85 &    0.003 &        0.020 \\
          Right-Putamen &        17.71 &      4.927 &      0.91 &     0.392 &         36.86 &       5.938 &       0.92 &      0.493 &     4.34 &    0.005 &        0.028 \\
    lh-inferiortemporal &        14.29 &      2.466 &      0.90 &     0.336 &         28.57 &       1.850 &       0.86 &      0.156 &     4.33 &    0.005 &        0.028 \\
 lh-medialorbitofrontal &        23.43 &      1.412 &      0.96 &     0.800 &         38.00 &       2.837 &       0.91 &      0.405 &     3.83 &    0.009 &        0.035 \\
      lh-middletemporal &        10.71 &      1.248 &      0.86 &     0.157 &         18.71 &       1.267 &       0.87 &      0.195 &     4.00 &    0.007 &        0.034 \\
     lh-parsopercularis &        11.43 &      2.369 &      0.92 &     0.499 &         22.14 &       1.752 &       0.90 &      0.331 &     5.16 &    0.002 &        0.018 \\
       lh-pericalcarine &        22.00 &      2.000 &      0.82 &     0.070 &         36.14 &       2.198 &       0.97 &      0.913 &     3.87 &    0.008 &        0.035 \\
          lh-precentral &        60.86 &      4.803 &      0.95 &     0.723 &        107.43 &       4.064 &       0.83 &      0.078 &     7.36 &    0.000 &        0.008 \\
     lh-superiorfrontal &        76.29 &      5.402 &      0.86 &     0.140 &        135.71 &       6.643 &       0.91 &      0.425 &     8.55 &    0.000 &        0.006 \\
    lh-superiorparietal &        50.14 &      5.049 &      0.83 &     0.079 &         85.29 &       5.126 &       0.83 &      0.088 &     5.15 &    0.002 &        0.018 \\
    lh-superiortemporal &        50.57 &      2.125 &      0.95 &     0.728 &         65.29 &       2.222 &       0.93 &      0.569 &     4.39 &    0.005 &        0.028 \\
             rh-lingual &        13.43 &      2.742 &      0.96 &     0.819 &         34.86 &       3.453 &       0.91 &      0.366 &     6.51 &    0.001 &        0.009 \\
      rh-middletemporal &        22.00 &      2.370 &      0.93 &     0.525 &         39.14 &       4.636 &       0.96 &      0.800 &     3.75 &    0.010 &        0.035 \\
     rh-parahippocampal &         7.43 &      1.232 &      0.88 &     0.249 &         20.43 &       1.395 &       0.87 &      0.205 &     7.17 &    0.000 &        0.008 \\
       rh-parsorbitalis &         7.29 &      0.837 &      0.93 &     0.555 &         13.71 &       1.822 &       0.83 &      0.089 &     3.72 &    0.010 &        0.035 \\
           rh-precuneus &        54.00 &      3.748 &      0.95 &     0.735 &         98.86 &       6.296 &       0.91 &      0.423 &     9.40 &    0.000 &        0.006 \\
    rh-superiorparietal &        62.71 &      5.317 &      0.97 &     0.923 &        125.57 &       5.559 &       0.86 &      0.151 &     6.93 &    0.000 &        0.008 \\
    rh-superiortemporal &        40.57 &      4.418 &      0.82 &     0.063 &         71.29 &       2.917 &       0.96 &      0.844 &     5.31 &    0.002 &        0.018 \\
\bottomrule
\end{tabular}
\end{table}

\newpage\selectlanguage{english}
\begin{table}
\begin{tabular}{lrrrrrrrrrrr}
Node strength \\
\toprule
{} & \multicolumn{4}{c}{\textit{ZDHHC9}} & \multicolumn{4}{c}{control} & & \\
{} & mean & SE & Shapiro-W & Shapiro-p & mean & SE & Shapiro-W & Shapiro-p & t(6) & $p$ & corr.-$p$ \\
\midrule
                  Brain-Stem &       117.17 &      5.576 &      0.94 &     0.594 &        144.22 &       5.829 &       0.91 &      0.401 &     4.37 &    0.005 &        0.018 \\
               Right-Caudate &         9.20 &      1.258 &      0.89 &     0.262 &         17.23 &       1.552 &       0.97 &      0.920 &     4.43 &    0.004 &        0.018 \\
               Right-Putamen &         7.09 &      2.028 &      0.90 &     0.336 &         16.58 &       3.006 &       0.95 &      0.717 &     4.21 &    0.006 &        0.020 \\
      lh-caudalmiddlefrontal &         9.15 &      0.583 &      0.97 &     0.910 &         13.81 &       1.210 &       0.99 &      0.989 &     3.48 &    0.013 &        0.035 \\
                 lh-fusiform &        14.26 &      2.449 &      0.92 &     0.495 &         23.41 &       1.717 &       0.94 &      0.626 &     3.39 &    0.015 &        0.037 \\
         lh-inferiortemporal &         6.38 &      1.339 &      0.90 &     0.341 &         14.66 &       0.787 &       0.97 &      0.925 &     5.38 &    0.002 &        0.011 \\
         lh-isthmuscingulate &         5.23 &      1.205 &      0.96 &     0.855 &         14.28 &       1.174 &       0.92 &      0.502 &     4.84 &    0.003 &        0.014 \\
                  lh-lingual &         8.22 &      0.383 &      0.87 &     0.175 &         13.08 &       0.790 &       0.91 &      0.368 &     6.28 &    0.001 &        0.008 \\
      lh-medialorbitofrontal &         8.76 &      0.465 &      0.91 &     0.406 &         17.92 &       2.140 &       0.86 &      0.139 &     3.68 &    0.010 &        0.031 \\
           lh-middletemporal &         4.72 &      0.576 &      0.81 &     0.051 &          9.90 &       0.613 &       0.85 &      0.121 &     5.40 &    0.002 &        0.011 \\
          lh-parsopercularis &         5.17 &      1.095 &      0.94 &     0.632 &         10.51 &       1.149 &       0.92 &      0.482 &     4.69 &    0.003 &        0.015 \\
            lh-pericalcarine &        10.18 &      1.085 &      0.82 &     0.058 &         20.07 &       1.291 &       0.94 &      0.618 &     4.99 &    0.002 &        0.014 \\
               lh-precentral &        30.02 &      2.804 &      0.93 &     0.515 &         57.94 &       2.438 &       0.86 &      0.138 &     9.04 &    0.000 &        0.003 \\
 lh-rostralanteriorcingulate &         6.98 &      1.126 &      0.93 &     0.531 &         15.70 &       1.952 &       0.92 &      0.466 &     4.13 &    0.006 &        0.021 \\
          lh-superiorfrontal &        36.07 &      3.302 &      0.93 &     0.576 &         70.83 &       3.858 &       0.98 &      0.939 &     9.98 &    0.000 &        0.002 \\
         lh-superiortemporal &        22.68 &      0.827 &      0.92 &     0.489 &         33.89 &       1.677 &       0.87 &      0.195 &     4.87 &    0.003 &        0.014 \\
                  rh-lingual &         5.38 &      1.233 &      0.97 &     0.932 &         18.49 &       1.984 &       0.92 &      0.499 &     6.99 &    0.000 &        0.005 \\
      rh-medialorbitofrontal &         5.76 &      0.739 &      0.93 &     0.561 &         11.10 &       0.872 &       0.99 &      0.990 &     4.39 &    0.005 &        0.018 \\
           rh-middletemporal &         8.15 &      0.933 &      0.91 &     0.364 &         19.10 &       2.706 &       0.98 &      0.943 &     4.39 &    0.005 &        0.018 \\
          rh-parahippocampal &         3.27 &      0.613 &      0.84 &     0.100 &         10.74 &       0.728 &       0.89 &      0.288 &     8.32 &    0.000 &        0.003 \\
              rh-postcentral &         3.99 &      1.483 &      0.86 &     0.168 &         14.66 &       2.370 &       0.93 &      0.528 &     3.36 &    0.015 &        0.037 \\
                rh-precuneus &        26.26 &      2.302 &      0.94 &     0.637 &         52.14 &       3.591 &       0.96 &      0.807 &    12.42 &    0.000 &        0.001 \\
 rh-rostralanteriorcingulate &         5.60 &      0.727 &      0.95 &     0.761 &         11.87 &       2.223 &       0.82 &      0.069 &     3.17 &    0.019 &        0.044 \\
         rh-superiorparietal &        29.04 &      2.939 &      0.96 &     0.789 &         64.64 &       3.058 &       0.87 &      0.169 &     7.39 &    0.000 &        0.004 \\
         rh-superiortemporal &        18.20 &      2.316 &      0.89 &     0.258 &         35.87 &       1.937 &       0.95 &      0.716 &     6.02 &    0.001 &        0.008 \\
              rh-frontalpole &         2.40 &      0.683 &      0.81 &     0.054 &          4.45 &       0.565 &       0.85 &      0.111 &     3.74 &    0.010 &        0.031 \\
                   rh-insula &         2.73 &      0.208 &      0.83 &     0.077 &          5.68 &       0.951 &       0.88 &      0.214 &     3.13 &    0.020 &        0.044 \\
\bottomrule
\end{tabular}
\end{table}

\newpage\selectlanguage{english}
\begin{table}
\begin{tabular}{lrrrrrrrrrrr}
Clustering coefficient \\
\toprule
{} & \multicolumn{4}{c}{\textit{ZDHHC9}} & \multicolumn{4}{c}{control} & & \\
{} & mean & SE & Shapiro-W & Shapiro-p & mean & SE & Shapiro-W & Shapiro-p & t(6) & $p$ & corr.-$p$ \\
\midrule
 lh-isthmuscingulate &         1.07 &      0.433 &      0.84 &     0.098 &          3.62 &       0.287 &       0.90 &      0.333 &    10.01 &    0.000 &        0.005 \\
  lh-parsopercularis &         0.75 &      0.157 &      0.90 &     0.341 &          1.81 &       0.162 &       0.91 &      0.409 &     6.39 &    0.001 &        0.027 \\
\bottomrule
\end{tabular}
\end{table}

\newpage\selectlanguage{english}
\begin{table}
\begin{tabular}{lrrrrrrrrrrr}
Efficiency \\
\toprule
{} & \multicolumn{4}{c}{\textit{ZDHHC9}} & \multicolumn{4}{c}{control} & & \\
{} & mean & SE & Shapiro-W & Shapiro-p & mean & SE & Shapiro-W & Shapiro-p & t(6) & $p$ & corr.-$p$ \\
\midrule
  rh-caudalmiddlefrontal &        76.81 &      5.653 &      0.90 &     0.345 &        120.94 &       4.369 &       0.91 &      0.401 &    10.00 &    0.000 &        0.005 \\
 rh-rostralmiddlefrontal &        83.09 &      4.541 &      0.92 &     0.504 &        124.14 &       6.102 &       0.95 &      0.725 &     6.56 &    0.001 &        0.017 \\
\bottomrule
\end{tabular}
\end{table}

\subsection{\textit{ZDHHC9} expression and structural connectome properties}
Normalized gene expression obtained from the Allen Brain Institute Human Brain database indicated higher expression of \textit{ZDHHC9} in the left compared to the right hemisphere (see \ref{figure:ClustCoeff_and_ZDHHC9_expression}). Local maxima were found in the left postcentral gyrus, inferior frontal cortex, anterior cingulate cortex, inferior parietal lobule, and right lingual gyrus. Low expression was observed in the right posterior and isthmus cingulate cortex, and left superior temporal gyrus.

Next, the relationship between node-level graph metrics and gene expression in each region was investigated. The analysis indicated a significant positive association between node-level clustering coefficient and \textit{ZDHHC9} expression in the \textit{ZDHHC9} (F(1,66)=15.62, R\textsuperscript{2}=0.191, \beta=0.0052, $p$$<$0.001, corrected-$p$=0.008, see Figure \ref{figure:ClustCoeff_and_ZDHHC9_expression}), but not in the control group (F(1,66)=5.486, R\textsuperscript{2}=0.077, \beta=0.002, $p$=0.022, corrected-$p$=0.888). No significant association between graph measures and expression of other genes were found. Cook's distance indicated the presence of three influential data points, i.e. the left (c=0.14) and right banks of the superior temporal sulcus (c=0.18), and the left caudal anterior cingulate (c=0.11). However, the association between \textit{ZDHHC9} expression and clustering coefficient in the \textit{ZDHHC9} group was also present when these regions were removed (F(1,63)=23.68, R\textsuperscript{2}=0.273, \beta=0.006, $p<$0.001, corrected-$p$=0.001).\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.7\columnwidth]{figures/ClustCoeff-and-ZDHHC9-expression1/ClustCoeff-and-ZDHHC9-expression1}
\caption{{\label{figure:ClustCoeff_and_ZDHHC9_expression}: \textbf{a} Normalized expression of \textit{ZDHHC9} across the cortex \textbf{b} Relationship between average node clustering coefficient in the \textbf{ZDHHC9} and control group and normalized regional expression of \textit{ZDHHC9}. Regression analysis indicated a significant positive relationship between clustering coefficient and \textit{ZDHHC9} expression in the \textit{ZDHHC9} group (Bonferroni-corrected $p$=0.003), but not the control group (Bonferroni-corrected $p$=0.444)%
}}
\end{center}
\end{figure}

\section{Discussion}
Loss of function mutations in \textit{ZDHHC9} result in pervasive differences in white matter \cite{Bathelt_2016}, alongside a cognitive profile that includes profound expressive language deficits \cite{Baker_2015}. The current study demonstrates that these mutations also result in systematic differences in structural brain organization, with reductions in node strength and node degree in both cortical and subcortical regions. Furthermore, graph metrics of clustering and efficiency indicated significant reductions in the \textit{ZDHHC9} case group indicating that \textit{ZDHHC9} mutation impacts on white matter network topology.  


The current study demonstrates that these mutations also result in systematic differences in structural brain organization, with reductions in node strength and node degree in both cortical and subcortical regions.. Furthermore, graph metrics of clustering and efficiency indicated significant reductions in the \textit{ZDHHC9} case group \cite{Collin_2013a}. Any differences in genetic predisposition affecting this processes are likely to impose early constraints with general effects on the later trajectory of neural and cognitive development and may contribute to the mild-moderate intellectual disability observed in this group \cite{Di_Martino_2014}. 



Topographical analysis of clustering coefficient and local efficiency indicated differences in nodes of the frontal, left parietal, and right temporal lobe. These results indicate that these nodes are less integrated with the rest of the network in the \textit{ZDHHC9} group. These regionally specific effects may provide a basis for the cognitive profile that these individuals show. For instance, reduced connectivity between nodes of a network involving frontal and temporo-parietal nodes is consistent with the previously described language deficits in this group \cite{Baker_2015}. Deficits in inhibitory control \cite{Baker_2015} may arise from the reduced integration of nodes of the anterior cingulate and prefrontal cortex. 

Furthermore, the regional variation in clustering coefficient within the \textit{ZDHHC9} group is predicted by regional expression levels of \textit{ZDHHC9}. The higher the expression patterns of \textit{ZDHHC9}, the higher the clustering coefficient in this group. For example, expression of \textit{ZDHHC9} is highest in left frontal and temporo-parietal regions. The largest reduction in regional comparison of clustering coefficient in the \textit{ZDHHC9} group is also found in a left frontal region. This convergent finding supports the suggestion that \textit{ZDHHC9} may play a critical role in shaping long-range white matter connectivity.



The influence of \textit{ZDHHC9} mutation on structural brain organisation shows both similarities and differences when compared with other  groups with a similar phenotype or genetic mechanisms. Like \textit{ZDHHC9} mutation \cite{Raymond_2007}, Fragile-X syndrome is a cause of X-linked intellectual disability. Leow et al. investigated local and global properties of the white matter connectome in Fragile-X syndrome (FXS) \cite{Leow_2014}. FXS is caused by CGG trinucleotide repeats in the Fragile-X mental retardiation 1 (\textit{FMR1}) gene on the X chromosome \cite{Belmonte_2006}. Leow and colleagues reported an association between the number of trinucleotide repeats in the \textit{FMR1} gene and global network efficiency in male premutation carriers as well as local differences in efficiency and clustering coefficient in left temporal nodes (also see \cite{Bruno_2016}). Our results for \textit{ZDHHC9} also indicated a reduction in global efficiency of the structural network similar to that reported for FXS, suggesting that this observation relates non-specifically to low IQ. However, topographical analysis of clustering coefficient and local efficiency indicated reductions in the parietal and frontal lobe in the \textit{ZDHHC9} group, whereas reductions in temporal areas were less pronounced or were statistically indistinguishable from the control group. In other words, mutations in \textit{ZDHHC9} and \textit{FXS} show a convergent reduction in global network efficiency, but different local patterns of efficiency and clustering coefficient that distinguish the groups. 

Rolandic epilepsy is another relevant neurodevelopmental condition for comparison due to the overlapping phenotype of expressive language deficits and epilepsy with centro-temporal spikes that were also observed in the carriers of ZDHHC9 mutation \cite{Baker_2015}. A study by Besseling and colleagues identified a reduction in structural white matter connectivity of the Perisylvian system, including the left inferior frontal, supramarginal, and postcentral gyrus \cite{Besseling_2013}. Studies of functional connectivity indicated reduced integration of these areas and delayed convergence of structural and functional connectivity in RE \cite{Besseling_2013a, Besseling_2014}. Further, graph theoretical analysis of the functional connectome indicated reduced clustering coefficient and local efficiency in areas of the parietal and frontal lobe in RE similar to the findings of structural connectivity differences in the current study \cite{Xia_2013}. In summary, studies of functional and structural connectivity in a neurodevelopmental condition of mixed aetiology with a similar phenotype to \textit{ZDHHC9} mutation showed reduced connectivity in areas of the parietal and frontal lobe akin to the structural connectivity changes observed in the current investigation. We are not aware of another connectome analysis of a developmental language disorder (either of known or unknown origin) against which to compare the results of our study.


\subsection{Limitations}
The findings of the current investigation are associated with some limitations. In addition to general limitation of diffusion-weighted imaging, such as low signal-to-noise ratio, issues regarding the fit of the diffusion model, presence of crossing fibres \cite{Jones_2013}, there are specific limitations related to structural connectome approaches \cite{Fornito_2013}. A multitude of methods for structural connectome analysis of diffusion-weighted MR data have been reported in the literature \cite{Qi_2015} and there is currently no consensus on best practices or published rigorous comparisons across different methods \cite{Qi_2015, Zalesky_2010}. One major issue is the presence of false positive and false negative connections associated with the tractography algorithm \cite{Zalesky_2016, Garrison_2015}. In view of this ongoing debate within the field, we conducted various control analyses based on deterministic tracking along the maximum direction of an alternative diffusion model shown in the.  These are included in the Supplementary Materials section, and show similar results as reported in the main analysis.

The connection weights in the current analysis were based on the average FA along the entire tract, which is likely to minimize some of the problems associated with streamline measures. However, this measure is also less sensitive to localized effects and may be influenced by crossing fibres. To address this, control analyses using a metric that is able to incorporate crossing fibres, i.e. Generalized FA  \cite{Cohen_Adad_2008},), were conducted. The results of these converged with the findings of the main analysis, and are included in the Supplementary Materials.

Another limitation concerns the node definitions. The current study used a relatively coarse anatomical parcellation of the cortex and subcortical areas, because this enabled us to explore relationships with gene expression, which was only available in this parcellation scheme. Given the pervasive differences in diffusion tensor metrics at voxel resolution \cite{Bathelt_2016}, we think that is unlikely that a more detailed parcellation would provide much additional information. 


\subsubsection{Conclusion}
The current investigation aimed to elucidate the effects of mutations in a single-gene disorder on white matter structural connectome organisation. Mutations in the \textit{ZDHHC9} gene were associated with reductions in connection weight of within-hemisphere connections that resulted in altered network properties, including reduction in mean clustering coefficient and global efficiency. Topological analysis of these differences indicated that reductions in the \textit{ZDHHC9} group were most pronounced for frontal and temporo-parietal nodes. Furthermore, comparison of graph theory metrics with \textit{ZDHHC9} expression data obtained from the Allen Brain Human Brain repository indicated that higher expression of \textit{ZDHHC9} related to higher local clustering. The results of the study suggest that mutations in the palmitoylation gene \textit{ZDHHC9} impact on large-scale white matter organisation. The organisation of white matter networks may represent an important intermediate phenotype to understand the effect of genetic mutations on cognitive development.

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