ORCID Profile
0000-0002-4742-7015
Current Organisation
Australian Catholic University
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Publisher: Cold Spring Harbor Laboratory
Date: 06-03-2022
DOI: 10.1101/2022.03.03.22271839
Abstract: Approximately 65% of moderate-to-severe traumatic brain injury (m-sTBI) patients present with poor long-term behavioural outcomes, which can significantly impair activities of daily living. Numerous diffusion-weighted MRI studies have linked these poor outcomes to decreased white matter integrity of several commissural tracts, association fibres and projection fibres in the brain. However, these studies focused on group-based analyses, which are unable to deal with the substantial between-patient heterogeneity in m-sTBI. As a result, there is increasing interest in conducting in idualised neuroimaging analyses. Here, we generated a detailed subject-specific characterisation of microstructural organisation of white matter tracts in 5 chronic patients with m-sTBI (29 – 49y, 2 females). We developed an imaging analysis framework using fixel-based analysis and TractLearn to determine whether the values of fibre density of white matter tracts at the in idual patient level deviate from the healthy control group ( n = 12, 8F, M age =35.7y, age range 25 – 64y). Our in idualised analysis confirmed unique white matter profiles, and the heterogeneous nature of m-sTBI to properly characterise the extent of brain abnormality. Future studies incorporating clinical data, as well as utilising larger reference s les and examining the test-retest reliability of the fixel-wise metrics are warranted. This proof-of-concept study suggests that these resulting in idual profiles may assist clinicians in planning personalised training programs for chronic m-sTBI patients, which is necessary to achieve optimal behavioural outcomes and improved quality of life.
Publisher: Cold Spring Harbor Laboratory
Date: 04-03-2022
DOI: 10.1101/2022.03.02.22271654
Abstract: Graph theoretical analysis of the structural connectome has been employed successfully to characterise brain network alterations in patients with traumatic brain injury (TBI). However, heterogeneity in neuropathology is a well-known issue in the TBI population, such that group comparisons of patients against controls are confounded by within-group variability. Recently, novel single-subject profiling approaches have been developed to capture inter-patient heterogeneity. We present a personalised connectomics approach that examines structural brain alterations in six chronic patients with moderate-to-severe TBI who underwent anatomical and diffusion magnetic resonance imaging (MRI). We generated in idualised profiles of lesion characteristics and network measures (including personalised graph metric ‘GraphMe’ plots, and nodal and edge-based brain network alterations) and compared them against healthy reference cases (N=12) to assess brain damage qualitatively and quantitatively at the in idual level. Our findings revealed clinically significant alterations of brain networks with high variability between patients. Our profiling can be used by clinicians to formulate a neuroscience-guided integrative rehabilitation program for TBI patients, and for designing personalised rehabilitation protocols based on their unique lesion load and connectome.
Publisher: Wiley
Date: 06-06-2021
DOI: 10.1111/PSYP.13871
Abstract: Attentional lapses interfere with goal‐directed behaviors, which may result in harmless (e.g., not hearing instructions) or severe (e.g., fatal car accident) consequences. Task‐related functional MRI (fMRI) studies have shown a link between attentional lapses and activity in the frontoparietal network. Activity in this network is likely to be mediated by the organization of the white matter fiber pathways that connect the regions implicated in the network, such as the superior longitudinal fasciculus I (SLF‐I). In the present study, we investigate the relationship between susceptibility to attentional lapses and relevant white matter pathways in 36 healthy adults (23 females, M age = 31.56 years). Participants underwent a diffusion MRI (dMRI) scan and completed the global–local task to measure attentional lapses, similar to previous fMRI studies. Applying the fixel‐based analysis framework for fiber‐ specific analysis of dMRI data, we investigated the association between attentional lapses and variability in microstructural fiber density (FD) and macrostructural (morphological) fiber‐bundle cross section (FC) in the SLF‐I. Our results revealed a significant negative association between higher total number of attentional lapses and lower FD in the left SLF‐I. This finding indicates that the variation in the microstructure of a key frontoparietal white matter tract is associated with attentional lapses and may provide a trait‐like biomarker in the general population. However, SLF‐I microstructure alone does not explain propensity for attentional lapses, as other factors such as sleep deprivation or underlying psychological conditions (e.g., sleep disorders) may also lead to higher susceptibility in both healthy people and those with neurological disorders.
Publisher: MIT Press
Date: 2023
DOI: 10.1162/NETN_A_00277
Abstract: Graph theoretical analysis of the structural connectome has been employed successfully to characterize brain network alterations in patients with traumatic brain injury (TBI). However, heterogeneity in neuropathology is a well-known issue in the TBI population, such that group comparisons of patients against controls are confounded by within-group variability. Recently, novel single-subject profiling approaches have been developed to capture inter-patient heterogeneity. We present a personalized connectomics approach that examines structural brain alterations in five chronic patients with moderate to severe TBI who underwent anatomical and diffusion magnetic resonance imaging. We generated in idualized profiles of lesion characteristics and network measures (including personalized graph metric GraphMe plots, and nodal and edge-based brain network alterations) and compared them against healthy reference cases (N = 12) to assess brain damage qualitatively and quantitatively at the in idual level. Our findings revealed alterations of brain networks with high variability between patients. With validation and comparison to stratified, normative healthy control comparison cohorts, this approach could be used by clinicians to formulate a neuroscience-guided integrative rehabilitation program for TBI patients, and for designing personalized rehabilitation protocols based on their unique lesion load and connectome.
Publisher: SAGE Publications
Date: 22-01-2018
Abstract: Acquired brain injury (ABI) is associated with a range of cognitive and motor deficits, and poses a significant personal, societal, and economic burden. Rehabilitation programs are available that target motor skills or cognitive functioning. In this review, we summarize the existing evidence that training may enhance structural neuroplasticity in patients with ABI, as assessed using structural magnetic resonance imaging (MRI)–based techniques that probe microstructure or morphology. Twenty-five research articles met key inclusion criteria. Most trials measured relevant outcomes and had treatment benefits that would justify the risk of potential harm. The rehabilitation program included a variety of task-oriented movement exercises (such as facilitation therapy, postural control training), neurorehabilitation techniques (such as constraint-induced movement therapy) or computer-assisted training programs (eg, Cogmed program). The reviewed studies describe regional alterations in white matter architecture and/or gray matter volume with training. Only weak-to-moderate correlations were observed between improved behavioral function and structural changes. While structural MRI is a powerful tool for detection of longitudinal structural changes, specific measures about the underlying biological mechanisms are lacking. Continued work in this field may potentially see structural MRI metrics used as biomarkers to help guide treatment at the in idual patient level.
Publisher: Elsevier BV
Date: 10-2017
DOI: 10.1016/J.NEUROIMAGE.2016.12.003
Abstract: Traumatic brain injury (TBI) is associated with cognitive and motor deficits, and poses a significant personal, societal, and economic burden. One mechanism by which TBI is thought to affect cognition and behavior is through changes in functional connectivity. Graph theory is a powerful framework for quantifying topological features of neuroimaging-derived functional networks. The objective of this paper is to review studies examining functional connectivity in TBI with an emphasis on graph theoretical analysis that is proving to be valuable in uncovering network abnormalities in this condition. We review studies that have examined TBI-related alterations in different properties of the functional brain network, including global integration, segregation, centrality and resilience. We focus on functional data using task-related fMRI or resting-state fMRI in patients with TBI of different severity and recovery phase, and consider how graph metrics may inform rehabilitation and enhance efficacy. Moreover, we outline some methodological challenges associated with the examination of functional connectivity in patients with brain injury, including the s le size, parcellation scheme used, node definition and subgroup analyses. The findings suggest that TBI is associated with hyperconnectivity and a suboptimal global integration, characterized by increased connectivity degree and strength and reduced efficiency of functional networks. This altered functional connectivity, also evident in other clinical populations, is attributable to diffuse white matter pathology and reductions in gray and white matter volume. These functional alterations are implicated in post-concussional symptoms, posttraumatic stress and neurocognitive dysfunction after TBI. Finally, the effects of focal lesions have been found to depend critically on topological position and their role in the network. Graph theory is a unique and powerful tool for exploring functional connectivity in brain-injured patients. One limitation is that its results do not provide specific measures about the biophysical mechanism underlying TBI. Continued work in this field will hopefully see graph metrics used as biomarkers to provide more accurate diagnosis and help guide treatment at the in idual patient level.
Publisher: Springer Science and Business Media LLC
Date: 11-03-2021
Publisher: Elsevier BV
Date: 04-2019
DOI: 10.1016/J.NEUBIOREV.2019.01.002
Abstract: Although recent structural connectivity studies of traumatic brain injury (TBI) have used graph theory to evaluate alterations in global integration and functional segregation, pooled analysis is needed to examine the robust patterns of change in graph metrics across studies. Following a systematic search, 15 studies met the inclusion criteria for review. Of these, ten studies were included in a random-effects meta-analysis of global graph metrics, and subgroup analyses examined the confounding effects of severity and time since injury. The meta-analysis revealed significantly higher values of normalised clustering coefficient (gö=ö1.445, CI=[0.512, 2.378], pö=ö0.002) and longer characteristic path length (gö=ö0.514, CI=[0.190, 0.838], pö=ö0.002) in TBI patients compared with healthy controls. Our findings suggest that the TBI structural network has shifted away from the balanced small-world network towards a regular lattice. Therefore, these graph metrics may be useful markers of neurocognitive dysfunction in TBI. We conclude that the pattern of change revealed by our analysis should be used to guide hypothesis-driven research into the role of graph metrics as diagnostic and prognostic biomarkers.
No related grants have been discovered for Adam Clemente.