Author name cluster

Stephen Rose

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15 papers

1 author row

YNIMG Journal 2023 Journal Article

Early brain morphometrics from neonatal MRI predict motor and cognitive outcomes at 2-years corrected age in very preterm infants

Alex M. Pagnozzi
Liza van Eijk
Kerstin Pannek
Roslyn N. Boyd
Susmita Saha
Joanne George
Samudragupta Bora
DanaKai Bradford

Infants born very preterm face a range of neurodevelopmental challenges in cognitive, language, behavioural and/or motor domains. Early accurate identification of those at risk of adverse neurodevelopmental outcomes, through clinical assessment and Magnetic Resonance Imaging (MRI), enables prognostication of outcomes and the initiation of targeted early interventions. This study utilises a prospective cohort of 181 infants born <31 weeks gestation, who had 3T MRIs acquired at 29-35 weeks postmenstrual age and a comprehensive neurodevelopmental evaluation at 2 years corrected age (CA). Cognitive, language and motor outcomes were assessed using the Bayley Scales of Infant and Toddler Development – Third Edition and functional motor outcomes using the Neuro-sensory Motor Developmental Assessment. By leveraging advanced structural MRI pre-processing steps to standardise the data, and the state-of-the-art developing Human Connectome Pipeline, early MRI biomarkers of neurodevelopmental outcomes were identified. Using Least Absolute Shrinkage and Selection Operator (LASSO) regression, significant associations between brain structure on early MRIs with 2-year outcomes were obtained (r = 0. 51 and 0. 48 for motor and cognitive outcomes respectively) on an independent 25% of the data. Additionally, important brain biomarkers from early MRIs were identified, including cortical grey matter volumes, as well as cortical thickness and sulcal depth across the entire cortex. Adverse outcome on the Bayley-III motor and cognitive composite scores were accurately predicted, with an Area Under the Curve of 0. 86 for both scores. These associations between 2-year outcomes and patient prognosis and early neonatal MRI measures demonstrate the utility of imaging prior to term equivalent age for providing earlier commencement of targeted interventions for infants born preterm.

YNICL Journal 2022 Journal Article

Reduced cortical cholinergic innervation measured using [18F]-FEOBV PET imaging correlates with cognitive decline in mild cognitive impairment

Ying Xia
Eamonn Eeles
Jurgen Fripp
Donna Pinsker
Paul Thomas
Melissa Latter
Vincent Doré
Amir Fazlollahi

F]-florbetaben for amyloid-β imaging. The MCI group showed a significant global reduction of FEOBV retention in the cortex and in the parietal and occipital cortices specifically compared to the control group. The global cortical FEOBV retention of all participants positively correlated with the BF, hippocampus and grey matter volumes, but no association was found between the global FEOBV retention and amyloid-β status. Topographic profiles from voxel-wise analysis of FEOBV images revealed significant positive correlations with the cognitive domains associated with the underlying cortical areas. Overlapping profiles of decreased FEOBV were identified in correlation with impairment in executive function, attention and language, which covered the anterior cingulate gyrus, olfactory cortex, calcarine cortex, middle temporal gyrus and caudate nucleus. However, the absence of cortical atrophy in these areas suggested that reduced cholinergic terminal integrity in the cortex is an important factor underlying the observed cognitive decline in early dementia. Our results provide support for the utility and validity of FEOBV PET for quantitative assessment of region-specific cholinergic terminal integrity that could potentially be used for early detection of cholinergic dysfunction in dementia following further validation in larger cohorts.

YNICL Journal 2021 Journal Article

A prospective cohort study of prodromal Alzheimer’s disease: Prospective Imaging Study of Ageing: Genes, Brain and Behaviour (PISA)

Michelle K. Lupton
Gail A. Robinson
Robert J. Adam
Stephen Rose
Gerard J. Byrne
Olivier Salvado
Nancy A. Pachana
Osvaldo P. Almeida

This prospective cohort study, "Prospective Imaging Study of Ageing: Genes, Brain and Behaviour" (PISA) seeks to characterise the phenotype and natural history of healthy adult Australians at high future risk of Alzheimer's disease (AD). In particular, we are recruiting midlife and older Australians with high and low genetic risk of dementia to discover biological markers of early neuropathology, identify modifiable risk factors, and establish the very earliest phenotypic and neuronal signs of disease onset. PISA utilises genetic prediction to recruit and enrich a prospective cohort and follow them longitudinally. Online surveys and cognitive testing are used to characterise an Australia-wide sample currently totalling over 3800 participants. Participants from a defined at-risk cohort and positive controls (clinical cohort of patients with mild cognitive impairment or early AD) are invited for onsite visits for detailed functional, structural and molecular neuroimaging, lifestyle monitoring, detailed neurocognitive testing, plus blood sample donation. This paper describes recruitment of the PISA cohort, study methodology and baseline demographics.

YNICL Journal 2016 Journal Article

Automated, quantitative measures of grey and white matter lesion burden correlates with motor and cognitive function in children with unilateral cerebral palsy

Alex M. Pagnozzi
Nicholas Dowson
James Doecke
Simona Fiori
Andrew P. Bradley
Roslyn N. Boyd
Stephen Rose

White and grey matter lesions are the most prevalent type of injury observable in the Magnetic Resonance Images (MRIs) of children with cerebral palsy (CP). Previous studies investigating the impact of lesions in children with CP have been qualitative, limited by the lack of automated segmentation approaches in this setting. As a result, the quantitative relationship between lesion burden has yet to be established. In this study, we perform automatic lesion segmentation on a large cohort of data (107 children with unilateral CP and 18 healthy children) with a new, validated method for segmenting both white matter (WM) and grey matter (GM) lesions. The method has better accuracy (94%) than the best current methods (73%), and only requires standard structural MRI sequences. Anatomical lesion burdens most predictive of clinical scores of motor, cognitive, visual and communicative function were identified using the Least Absolute Shrinkage and Selection operator (LASSO). The improved segmentations enabled identification of significant correlations between regional lesion burden and clinical performance, which conform to known structure-function relationships. Model performance was validated in an independent test set, with significant correlations observed for both WM and GM regional lesion burden with motor function (p < 0.008), and between WM and GM lesions alone with cognitive and visual function respectively (p < 0.008). The significant correlation of GM lesions with functional outcome highlights the serious implications GM lesions, in addition to WM lesions, have for prognosis, and the utility of structural MRI alone for quantifying lesion burden and planning therapy interventions.

YNIMG Journal 2016 Journal Article

Statistical machine learning to identify traumatic brain injury (TBI) from structural disconnections of white matter networks

Jhimli Mitra
Kai-kai Shen
Soumya Ghose
Pierrick Bourgeat
Jurgen Fripp
Olivier Salvado
Kerstin Pannek
D. Jamie Taylor

Identifying diffuse axonal injury (DAI) in patients with traumatic brain injury (TBI) presenting with normal appearing radiological MRI presents a significant challenge. Neuroimaging methods such as diffusion MRI and probabilistic tractography, which probe the connectivity of neural networks, show significant promise. We present a machine learning approach to classify TBI participants primarily with mild traumatic brain injury (mTBI) based on altered structural connectivity patterns derived through the network based statistical analysis of structural connectomes generated from TBI and age-matched control groups. In this approach, higher order diffusion models were used to map white matter connections between 116 cortical and subcortical regions. Tracts between these regions were generated using probabilistic tracking and mean fractional anisotropy (FA) measures along these connections were encoded in the connectivity matrices. Network-based statistical analysis of the connectivity matrices was performed to identify the network differences between a representative subset of the two groups. The affected network connections provided the feature vectors for principal component analysis and subsequent classification by random forest. The validity of the approach was tested using data acquired from a total of 179 TBI patients and 146 controls participants. The analysis revealed altered connectivity within a number of intra- and inter-hemispheric white matter pathways associated with DAI, in consensus with existing literature. A mean classification accuracy of 68. 16%±1. 81% and mean sensitivity of 80. 0%±2. 36% were achieved in correctly classifying the TBI patients evaluated on the subset of the participants that was not used for the statistical analysis, in a 10-fold cross-validation framework. These results highlight the potential for statistical machine learning approaches applied to structural connectomes to identify patients with diffusive axonal injury.

YNICL Journal 2015 Journal Article

Basal forebrain atrophy correlates with amyloid β burden in Alzheimer's disease

Georg M Kerbler
Jürgen Fripp
Christopher C Rowe
Victor L Villemagne
Olivier Salvado
Stephen Rose
Elizabeth J Coulson

The brains of patients suffering from Alzheimer's disease (AD) have three classical pathological hallmarks: amyloid-beta (Aβ) plaques, tau tangles, and neurodegeneration, including that of cholinergic neurons of the basal forebrain. However the relationship between Aβ burden and basal forebrain degeneration has not been extensively studied. To investigate this association, basal forebrain volumes were determined from magnetic resonance images of controls, subjects with amnestic mild cognitive impairment (aMCI) and AD patients enrolled in the longitudinal Alzheimer's Disease Neuroimaging Initiative (ADNI) and Australian Imaging, Biomarkers and Lifestyle (AIBL) studies. In the AIBL cohort, these volumes were correlated within groups to neocortical gray matter retention of Pittsburgh compound B (PiB) from positron emission tomography images as a measure of Aβ load. The basal forebrain volumes of AD and aMCI subjects were significantly reduced compared to those of control subjects. Anterior basal forebrain volume was significantly correlated to neocortical PiB retention in AD subjects and aMCI subjects with high Aβ burden, whereas posterior basal forebrain volume was significantly correlated to neocortical PiB retention in control subjects with high Aβ burden. Therefore this study provides new evidence for a correlation between neocortical Aβ accumulation and basal forebrain degeneration. In addition, cluster analysis showed that subjects with a whole basal forebrain volume below a determined cut-off value had a 7 times higher risk of having a worse diagnosis within ~18 months.

YNIMG Journal 2015 Journal Article

Connectivity-based fixel enhancement: Whole-brain statistical analysis of diffusion MRI measures in the presence of crossing fibres

David A. Raffelt
Robert E. Smith
Gerard R. Ridgway
J-Donald Tournier
David N. Vaughan
Stephen Rose
Robert Henderson
Alan Connelly

In brain regions containing crossing fibre bundles, voxel-average diffusion MRI measures such as fractional anisotropy (FA) are difficult to interpret, and lack within-voxel single fibre population specificity. Recent work has focused on the development of more interpretable quantitative measures that can be associated with a specific fibre population within a voxel containing crossing fibres (herein we use fixel to refer to a specific fibre population within a single voxel ). Unfortunately, traditional 3D methods for smoothing and cluster-based statistical inference cannot be used for voxel-based analysis of these measures, since the local neighbourhood for smoothing and cluster formation can be ambiguous when adjacent voxels may have different numbers of fixels, or ill-defined when they belong to different tracts. Here we introduce a novel statistical method to perform whole-brain fixel-based analysis called connectivity-based fixel enhancement (CFE). CFE uses probabilistic tractography to identify structurally connected fixels that are likely to share underlying anatomy and pathology. Probabilistic connectivity information is then used for tract-specific smoothing (prior to the statistical analysis) and enhancement of the statistical map (using a threshold-free cluster enhancement-like approach). To investigate the characteristics of the CFE method, we assessed sensitivity and specificity using a large number of combinations of CFE enhancement parameters and smoothing extents, using simulated pathology generated with a range of test-statistic signal-to-noise ratios in five different white matter regions (chosen to cover a broad range of fibre bundle features). The results suggest that CFE input parameters are relatively insensitive to the characteristics of the simulated pathology. We therefore recommend a single set of CFE parameters that should give near optimal results in future studies where the group effect is unknown. We then demonstrate the proposed method by comparing apparent fibre density between motor neurone disease (MND) patients with control subjects. The MND results illustrate the benefit of fixel-specific statistical inference in white matter regions that contain crossing fibres.

YNICL Journal 2015 Journal Article

Motor pathway degeneration in young ataxia telangiectasia patients: A diffusion tractography study

Ishani Sahama
Kate Sinclair
Simona Fiori
James Doecke
Kerstin Pannek
Lee Reid
Martin Lavin
Stephen Rose

BACKGROUND: Our understanding of the effect of ataxia-telangiectasia mutated gene mutations on brain structure and function is limited. In this study, white matter motor pathway integrity was investigated in ataxia telangiectasia patients using diffusion MRI and probabilistic tractography. METHODS: Diffusion MRI were obtained from 12 patients (age range: 7-22 years, mean: 12 years) and 12 typically developing age matched participants (age range 8-23 years, mean: 13 years). White matter fiber tracking and whole tract statistical analyses were used to assess quantitative fractional anisotropy and mean diffusivity differences along the cortico-ponto-cerebellar, cerebellar-thalamo-cortical, somatosensory and lateral corticospinal tract length in patients using a linear mixed effects model. White matter tract streamline number and apparent fiber density in patient and control tracts were also assessed. RESULTS: Reduced fractional anisotropy along all analyzed patient tracts were observed (p < 0.001). Mean diffusivity was significantly elevated in anterior tract locations but was reduced within cerebellar peduncle regions of all patient tracts (p < 0.001). Reduced tract streamline number and tract volume in the left and right corticospinal and somatosensory tracts were observed in patients (p < 0.006). In addition, reduced apparent fiber density in the left and right corticospinal and right somatosensory tracts (p < 0.006) occurred in patients. CONCLUSIONS: Whole tract analysis of the corticomotor, corticospinal and somatosensory pathways in ataxia telangiectasia showed significant white matter degeneration along the entire length of motor circuits, highlighting that ataxia-telangiectasia gene mutation impacts the cerebellum and multiple other motor circuits in young patients.

YNICL Journal 2015 Journal Article

Validity of semi-quantitative scale for brain MRI in unilateral cerebral palsy due to periventricular white matter lesions: Relationship with hand sensorimotor function and structural connectivity

Simona Fiori
Andrea Guzzetta
Kerstin Pannek
Robert S. Ware
Giuseppe Rossi
Katrijn Klingels
Hilde Feys
Alan Coulthard

AIM: To provide first evidence of construct validity of a semi-quantitative scale for brain structural MRI (sqMRI scale) in children with unilateral cerebral palsy (UCP) secondary to periventricular white matter (PWM) lesions, by examining the relationship with hand sensorimotor function and whole brain structural connectivity. METHODS: Cross-sectional study of 50 children with UCP due to PWM lesions using 3 T (MRI), diffusion MRI and assessment of hand sensorimotor function. We explored the relationship of lobar, hemispheric and global scores on the sqMRI scale, with fractional anisotropy (FA), as a measure of brain white matter microstructure, and with hand sensorimotor measures (Assisting Hand Assessment, AHA; Jebsen-Taylor Test for Hand Function, JTTHF; Melbourne Assessment of Unilateral Upper Limb Function, MUUL; stereognosis; 2-point discrimination). RESULTS: Lobar and hemispheric scores on the sqMRI scale contralateral to the clinical side of hemiplegia correlated with sensorimotor paretic hand function measures and FA of a number of brain structural connections, including connections of brain areas involved in motor control (postcentral, precentral and paracentral gyri in the parietal lobe). More severe lesions correlated with lower sensorimotor performance, with the posterior limb of internal capsule score being the strongest contributor to impaired hand function. CONCLUSION: The sqMRI scale demonstrates first evidence of construct validity against impaired motor and sensory function measures and brain structural connectivity in a cohort of children with UCP due to PWM lesions. More severe lesions correlated with poorer paretic hand sensorimotor function and impaired structural connectivity in the hemisphere contralateral to the clinical side of hemiplegia. The quantitative structural MRI scoring may be a useful clinical tool for studying brain structure-function relationships but requires further validation in other populations of CP.

YNIMG Journal 2014 Journal Article

Distance informed Track-Weighted Imaging (diTWI): A framework for sensitising streamline information to neuropathology

Christopher Bell
Kerstin Pannek
Michael Fay
Paul Thomas
Pierrick Bourgeat
Olivier Salvado
Yaniv Gal
Alan Coulthard

Track-Weighted Imaging (TWI), where voxel intensity is based on image metrics encoded along streamline trajectories, provides a mechanism to study white matter disease. However, with generalised streamline weighting, it is difficult to localise the precise anatomical source and spread of injury or neuropathology. This limitation can be overcome by modulating the voxel weight based on the distance of the voxel from a given anatomical location along the tract, which we term diTWI: distance informed Track-Weighted Imaging. The location of known neuropathology can be delineated on any given imaging modality (e. g. MRI or PET). To demonstrate the clinical utility of this approach, we measured tumour cell infiltration along WM fibre tracts in 13 patients with newly diagnosed glioblastoma and 1 patient with Anaplastic Astrocytoma. TWI and diTWI maps were generated using information obtained from dynamic contrast enhanced MRI (area under the curve, AUC) and diffusivity maps (ADC and FA) with tumour boundaries automatically extracted using a logistic regression classifier. The accuracy of the derived tumour volumes was compared to those generated using 3, 4-dihydroxy-6-[18F]-fluoro-l-phenylalanine (FDOPA) PET imaging. The accuracy of the tumour volumes generated from the diTWI maps was superior to volumes derived from the TWI, geometric distance or baseline AUC, FA and ADC maps. The relative overlap and relative dissimilarity rates for the diTWI generated tumour volumes after classification were found to be 82. 3±15. 3% (range 69. 1–91. 9) and 16. 9±8. 8% (range 7. 9–37. 5), respectively. These findings show that diTWI maps provide a useful framework for localising neuropathological processes occurring along WM pathways.

YNIMG Journal 2014 Journal Article

Investigating brain connectivity heritability in a twin study using diffusion imaging data

Kai-kai Shen
Stephen Rose
Jurgen Fripp
Katie L. McMahon
Greig I. de Zubicaray
Nicholas G. Martin
Paul M. Thompson
Margaret J. Wright

Heritability of brain anatomical connectivity has been studied with diffusion-weighted imaging (DWI) mainly by modeling each voxel's diffusion pattern as a tensor (e. g. , to compute fractional anisotropy), but this method cannot accurately represent the many crossing connections present in the brain. We hypothesized that different brain networks (i. e. , their component fibers) might have different heritability and we investigated brain connectivity using High Angular Resolution Diffusion Imaging (HARDI) in a cohort of twins comprising 328 subjects that included 70 pairs of monozygotic and 91 pairs of dizygotic twins. Water diffusion was modeled in each voxel with a Fiber Orientation Distribution (FOD) function to study heritability for multiple fiber orientations in each voxel. Precision was estimated in a test–retest experiment on a sub-cohort of 39 subjects. This was taken into account when computing heritability of FOD peaks using an ACE model on the monozygotic and dizygotic twins. Our results confirmed the overall heritability of the major white matter tracts but also identified differences in heritability between connectivity networks. Inter-hemispheric connections tended to be more heritable than intra-hemispheric and cortico-spinal connections. The highly heritable tracts were found to connect particular cortical regions, such as medial frontal cortices, postcentral, paracentral gyri, and the right hippocampus.

YNIMG Journal 2014 Journal Article

Lesion segmentation from multimodal MRI using random forest following ischemic stroke

Jhimli Mitra
Pierrick Bourgeat
Jurgen Fripp
Soumya Ghose
Stephen Rose
Olivier Salvado
Alan Connelly
Bruce Campbell

Understanding structure–function relationships in the brain after stroke is reliant not only on the accurate anatomical delineation of the focal ischemic lesion, but also on previous infarcts, remote changes and the presence of white matter hyperintensities. The robust definition of primary stroke boundaries and secondary brain lesions will have significant impact on investigation of brain–behavior relationships and lesion volume correlations with clinical measures after stroke. Here we present an automated approach to identify chronic ischemic infarcts in addition to other white matter pathologies, that may be used to aid the development of post-stroke management strategies. Our approach uses Bayesian–Markov Random Field (MRF) classification to segment probable lesion volumes present on fluid attenuated inversion recovery (FLAIR) MRI. Thereafter, a random forest classification of the information from multimodal (T1-weighted, T2-weighted, FLAIR, and apparent diffusion coefficient (ADC)) MRI images and other context-aware features (within the probable lesion areas) was used to extract areas with high likelihood of being classified as lesions. The final segmentation of the lesion was obtained by thresholding the random forest probabilistic maps. The accuracy of the automated lesion delineation method was assessed in a total of 36 patients (24 male, 12 female, mean age: 64. 57±14. 23yrs) at 3months after stroke onset and compared with manually segmented lesion volumes by an expert. Accuracy assessment of the automated lesion identification method was performed using the commonly used evaluation metrics. The mean sensitivity of segmentation was measured to be 0. 53±0. 13 with a mean positive predictive value of 0. 75±0. 18. The mean lesion volume difference was observed to be 32. 32%±21. 643% with a high Pearson's correlation of r =0. 76 (p <0. 0001). The lesion overlap accuracy was measured in terms of Dice similarity coefficient with a mean of 0. 60±0. 12, while the contour accuracy was observed with a mean surface distance of 3. 06mm±3. 17mm. The results signify that our method was successful in identifying most of the lesion areas in FLAIR with a low false positive rate.

YNIMG Journal 2012 Journal Article

Apparent Fibre Density: A novel measure for the analysis of diffusion-weighted magnetic resonance images

David Raffelt
J.-Donald Tournier
Stephen Rose
Gerard R. Ridgway
Robert Henderson
Stuart Crozier
Olivier Salvado
Alan Connelly

This article proposes a new measure called Apparent Fibre Density (AFD) for the analysis of high angular resolution diffusion-weighted images using higher-order information provided by fibre orientation distributions (FODs) computed using spherical deconvolution. AFD has the potential to provide specific information regarding differences between populations by identifying not only the location, but also the orientations along which differences exist. In this work, analytical and numerical Monte-Carlo simulations are used to support the use of the FOD amplitude as a quantitative measure (i. e. AFD) for population and longitudinal analysis. To perform robust voxel-based analysis of AFD, we present and evaluate a novel method to modulate the FOD to account for changes in fibre bundle cross-sectional area that occur during spatial normalisation. We then describe a novel approach for statistical analysis of AFD that uses cluster-based inference of differences extended throughout space and orientation. Finally, we demonstrate the capability of the proposed method by performing voxel-based AFD comparisons between a group of Motor Neurone Disease patients and healthy control subjects. A significant decrease in AFD was detected along voxels and orientations corresponding to both the corticospinal tract and corpus callosal fibres that connect the primary motor cortices. In addition to corroborating previous findings in MND, this study demonstrates the clear advantage of using this type of analysis by identifying differences along single fibre bundles in regions containing multiple fibre populations.

YNIMG Journal 2012 Journal Article

Direct evidence of intra- and interhemispheric corticomotor network degeneration in amyotrophic lateral sclerosis: An automated MRI structural connectivity study

Stephen Rose
Kerstin Pannek
Christopher Bell
Fusun Baumann
Nicole Hutchinson
Alan Coulthard
Pamela McCombe
Robert Henderson

Although the pathogenesis of amyotrophic lateral sclerosis (ALS) is uncertain, there is mounting neuroimaging evidence to suggest a mechanism involving the degeneration of multiple white matter (WM) motor and extramotor neural networks. This insight has been achieved, in part, by using MRI Diffusion Tensor Imaging (DTI) and the voxelwise analysis of anisotropy indices, along with DTI tractography to determine which specific motor pathways are involved with ALS pathology. Automated MRI structural connectivity analyses, which probe WM connections linking various functionally discrete cortical regions, have the potential to provide novel information about degenerative processes within multiple white matter (WM) pathways. Our hypothesis is that measures of altered intra- and interhemispheric structural connectivity of the primary motor and somatosensory cortex will provide an improved assessment of corticomotor involvement in ALS. To test this hypothesis, we acquired High Angular Resolution Diffusion Imaging (HARDI) scans along with high resolution structural images (sMRI) on 15 patients with clinical evidence of upper and lower motor neuron involvement, and 20 matched control participants. Whole brain probabilistic tractography was applied to define specific WM pathways connecting discrete corticomotor targets generated from anatomical parcellation of sMRI of the brain. The integrity of these connections was interrogated by comparing the mean fractional anisotropy (FA) derived for each WM pathway. To assist in the interpretation of results, we measured the reproducibility of the FA summary measures over time (6months) in control participants. We also incorporated into our analysis pipeline the evaluation and replacement of outlier voxels due to head motion and physiological noise. When assessing corticomotor connectivity, we found a significant reduction in mean FA within a number of intra- and interhemispheric motor pathways in ALS patients. The abnormal intrahemispheric pathways include the corticospinal tracts involving the left and right precentral gyri (lh. preCG, rh. preCG) and brainstem (bs); right postcentral gyrus (rh. postCG) and bs; lh. preCG and left posterior cingulate gyrus (lh. PCG); rh. preCG and right posterior cingulate gyrus (rh. PCG); and the rh. preCG and right paracentral gyrus (rh. paraCG). The abnormal interhemispheric pathways included the lh. preCG and rh. preCG; lh. preCG and rh. paraCG; lh. preCG and right superior frontal gyrus (rh. supFG); lh. preCG and rh. postCG; rh. preCG and left paracentral gyrus (lh. paraCG); rh. preCG and left superior frontal gyrus (lh. supFG); and the rh. preCG and left caudal middle frontal gyrus (lh. caudMF). The reproducibility of the measurement of these pathways was high (variation less than 5%). Maps of the outlier rejection voxels, revealed clusters within the corpus callosum and corticospinal projections. This finding highlights the importance of correcting for motion artefacts and physiological noise when studying clinical populations. Our novel findings, many of which are consistent with known pathology, show extensive involvement and degeneration of multiple corticomotor pathways in patients with upper and lower motor neuron signs and provide support for the use of automated structural connectivity techniques for studying neurodegenerative disease processes.

YNIMG Journal 2010 Journal Article

An automated strategy for the delineation and parcellation of commissural pathways suitable for clinical populations utilising high angular resolution diffusion imaging tractography

Kerstin Pannek
Jane L. Mathias
Erin D. Bigler
Greg Brown
Jamie D. Taylor
Stephen Rose

There is a growing interest in understanding alterations to the interhemispheric transfer of information as a result of brain injury and neurological disease. To facilitate research, we have developed a fully automated method for the accurate extraction of commissural pathways (corpus callosum (CC) and anterior commissure (AC)) and functional parcellation of the CC using a high angular resolution diffusion imaging (HARDI) based probabilistic tractography approach that is applicable to clinical populations. The CC was divided into 33 functional divisions based on its connections to cortical parcellations derived from individual structural images in 8 healthy participants. Probabilistic CC population maps acquired at two different b-values (1000 s mm−2 and 3000 s mm−2) are presented. Topography of the CC was consistent with histology reports. We show that HARDI data acquired at a higher b-value reveals more callosal-temporal connections than low b-value data. With respect to intra-subject precision, data acquired using a higher b-value show superior reproducibility of the delineated CC area on the midsagittal plane (MSP), as well as the total number of callosal streamlines and the number of clustered callosal streamlines. The AC was delineated in all 8 participants using high b-value HARDI tractography. Cortical projections of the AC were analysed and are in agreement with known anatomy. We conclude that, while data acquired at a lower b-value may be used, this is associated with a loss in quality, both in the delineation of commissural pathways and, potentially, the reproducibility of results over time.