The connectivity of the superior longitudinal fasciculus: a tractography DTI study

Bernal, Byron; Altman, Nolan

doi:10.1016/j.mri.2009.07.008

Cited by 143 publications

(104 citation statements)

References 37 publications

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“…The superior longitudinal fasciculus (SLF) is a fiber tract that spans from the inferior fontal gyrus to the superior temporal gyrus and temporoparietal junction, terminating close to Broca's Area, Wernicke's Area, precentral gyrus, and the supramarginal gyrus (Bernal and Altman, 2010;Catani and Jones, 2005;Makris et al, 2005;Wakana et al, 2004). It is a major association fiber pathway related to regulation of motor behavior, spatial attention, and language (Makris et al, 2005).…”

Section: Discussionmentioning

confidence: 99%

“…Second, cortical connectivity theories of autism have postulated aberrations in long-distance connectivity in the brain in ASD, particularly with frontal brain regions holding most of the blame for alterations in function and connectivity (Kana et al, 2011). The anterior portion of the SLF projects to precentral gyrus (Bernal and Altman, 2010;Martino et al, 2013) and is delineated at superior, middle, and inferior frontal gyri (Makris et al, 2005). With terminations in frontal cortex, reduced FA in the anterior cluster of the LSLF supports the disrupted connectivity hypothesis of autism and suggests that the alterations in functional connectivity may be stemming from alterations in diffusion at anterior LSLF.…”

Section: Discussionmentioning

confidence: 99%

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White Matter Diffusion of Major Fiber Tracts Implicated in Autism Spectrum Disorder

et al. 2016

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Autism spectrum disorder (ASD) is a neurodevelopmental disorder found to have widespread alterations in the function and synchrony of brain regions. These differences may underlie alterations in microstructural organization, such as in white matter pathways. To investigate the diffusion of major white matter tracts, the current study examined multiple indices of white matter diffusion in 42 children and adults with ASD and 44 typically developing (TD) age-and IQ-matched peers using diffusion tensor imaging. Diffusivity measures were compared between groups for the following tracts: bilateral cingulum bundle, corpus callosum, inferior longitudinal fasciculus, superior longitudinal fasciculus, and uncinate fasciculus. Results indicate a significant reduction in fractional anisotropy (FA) for the left superior longitudinal fasciculus (LSLF) in ASD children and adults compared with TD peers. A significant increase in radial diffusivity for ASD participants was also found in the same cluster along the LSLF. In addition, a significant positive correlation emerged for all subjects between FA for the LSLF and age, with FA increasing with age. These findings point to a significant alteration in long-distance white matter connectivity in children and adults with ASD, potentially underscoring the relationship between alterations in white matter diffusion and the ASD phenotype. These results also suggest that the white matter alterations in autism may be subtle and related to the developmental trajectory.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

White Matter Diffusion of Major Fiber Tracts Implicated in Autism Spectrum Disorder

et al. 2016

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“…Including information related to the timing and type of the insults, which cannot be automatically determined using the proposed approach, would further increase the amount of variance explained by these models. The anatomical regions with significant relationships to clinical function (p < 0.05) concurred with previous studies of the roles of individual regions (p < 0.05) including the thalamus [Haber and Calzavara, 2009], lenticular [Middleton and Strick, 2000] and caudate nuclei [Grahn et al, 2008], corona radiata [Kraus et al, 2007], cerebral peduncle [Cho et al, 2007], PLIC [Kinnunen et al, 2011], external capsule [Fazio et al, 2009] and superior-longitudinal fasciculus [Bernal and Altman, 2010]. Patient age and gender were not observed to be significant predictors of outcome in any trained model.…”

Section: Discussionsupporting

confidence: 87%

Automated injury segmentation to assist in the treatment of children with cerebral palsy

Pagnozzi¹

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Cerebral palsy (CP) describes a group of permanent disorders of posture and movement caused by disturbances in the developing brain. It is the most common physical disability of children worldwide and can lead to a wide range of functional impairments. Accurate diagnosis and prognosis, in terms of the type and severity of functional impairment, is difficult due to the wide range of injuries that may occur and the variable effect of plasticity; which leads to inconsistency in the clinical outcomes of children with CP. The use of Magnetic resonance imaging (MRI) to identify and locate brain lesions has facilitated the diagnosis and qualitative classification of children with CP.Currently, the quantification of image findings from structural MRIs is not automated and remains labour intensive, hence is not widely performed for clinical assessment. Automated brain image segmentation techniques could reduce the clinical time required toprovide an accurate and reproducible quantification of injury. Although such approaches have been used to study other neurological disorders, the heterogeneous appearance of injury and the large anatomical distortion that occurs in CP require modification of existing algorithms to be sufficiently reliable. As a result, they have not been widely applied toMRIs of children with CP.In this thesis, a series of automated image quantification techniques are presented for analysing the MRI data obtained from children with CP. These techniques identify and quantify the severity of the three main types of injury observed in children with CP; including ventricular enlargement, cortical malformations and white and grey matter injury.The developed automated pipeline involves a number of technical developments and contributions to the automated analysis of CP MRI data. A brain tissue segmentation approach based on the Expectation Maximisation (EM)/Markov Random Field (MRF) approach was developed, with a modified MRF implementation that expects different tissue labels within a given neighbourhood to have a corresponding intensity gradient.Following this segmentation step, three different approaches were used to identify and quantify the three distinct types of injury observed from children with CP, which are all important for clinical assessment. Biomarkers from each type of injury obtained from these approaches were used as independent variables in a devised statistical methodology designed to elucidate significant and generalisable correlations between image-derived measures of injury and patient function.ii Ventricular enlargement was quantified using a model of lateral ventricular shape that encapsulates healthy variation observed in ventricular shape. The residual of this model to a target shape reveals a volume of injury, allowing a measure of involvement of critical adjacent anatomies, such as the thalamus, caudate nucleus and lenticular nucleus, to be computed. This measure of involvement was devised as a way to translate the indirect injury of ventricular enlargement to the direct in...

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“…For example, the method of tract tracing used in this study follows the value of FA from pixel to pixel to trace out fiber tracts. Advances in DTI have enriched our understanding about how particular fiber pathways connect regions to support speech, language and literacy in adults (Bernal & Altman, 2010;Catani, Jones, & ffytche, 2005;Glasser & Rilling, 2008;Thiebaut de Schotten, Dell'Acqua, Valabregue, & Catani, 2012). However, in part because the methodology is relatively new, little is known about the development of these pathways, and their corresponding function in young children remains understudied.…”

Section: Literature Review Investigating White Matter Development Inmentioning

confidence: 99%

“…AV speech, predominately the inferior frontal/ventral premotor, inferior parietal, and posterior temporal regions, and these regions have been proposed to be connected by the SLF/AF (Bernal & Altman, 2010;Catani et al, 2005;Dick & Tremblay, 2012;Martino et al, 2013;. Two processing streams, namely the auditory-visual and auditory-motor pathways, ground functional interactions among this frontal-parietal-temporal network.…”

Section: Neural Components Of Speech Several Brain Regions Have Beenmentioning

confidence: 99%

Fiber Pathways for Language in the Developing Brain: A Diffusion Tensor Imaging (DTI) Study

Broce¹

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The present study characterized two fiber pathways important for language, the superior longitudinal fasciculus/arcuate fasciculus (SLF/AF) and the frontal aslant tract (FAT), and related these tracts to speech, language, and literacy skill in children five to eight years old. We used Diffusion Tensor Imaging (DTI) to characterize the fiber pathways and administered several language assessments. The FAT was identified for the first time in children. Results showed no age-related change in integrity of the FAT, but did show age-related change in the left (but not right) SLF/AF. Moreover, only the integrity of the right FAT was related to phonology but not audiovisual speech perception, articulation, language, or literacy. Both the left and right SLF/AF related to language measures, specifically receptive and expressive language, and language content.These findings are important for understanding the neurobiology of language in the developing brain, and can be incorporated within contemporary dorsal-ventral-motor models for language.

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The connectivity of the superior longitudinal fasciculus: a tractography DTI study

Cited by 143 publications

References 37 publications

White Matter Diffusion of Major Fiber Tracts Implicated in Autism Spectrum Disorder

White Matter Diffusion of Major Fiber Tracts Implicated in Autism Spectrum Disorder

Automated injury segmentation to assist in the treatment of children with cerebral palsy

Fiber Pathways for Language in the Developing Brain: A Diffusion Tensor Imaging (DTI) Study

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