Structural and functional brain network correlates of depressive symptoms in premanifest Huntington's disease

McColgan, Peter; Razi, Adeel; Gregory, Sarah; Seunarine, Kiran K.; Dürr, Alexandra; Roos, Raymund A.C.; Leavitt, Blair R.; Scahill, Rachael I.; Clark, Chris A.; Langbehn, Douglas R.; Rees, Geraint; Tabrizi, Sarah J.; Investigators, Track On-HD

doi:10.1002/hbm.23527

Cited by 31 publications

(18 citation statements)

References 41 publications

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“…Finally, these measures captured functional alterations in brain regions overlapping with the posterior key areas of the DMN in both HD patients and pre‐HD individuals (Liu et al, ; McColgan, Razi, et al, ), suggesting that regional and graph measures might be more accurate to identify changes within this network.…”

Section: Resultsmentioning

confidence: 99%

Aberrant brain network connectivity in presymptomatic and manifest Huntington's disease: A systematic review

Pini

Jacquemot

Cagnin

et al. 2019

Human Brain Mapping

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Resting‐state functional magnetic resonance imaging (rs‐fMRI) has the potential to shed light on the pathophysiological mechanisms of Huntington's disease (HD), paving the way to new therapeutic interventions. A systematic literature review was conducted in three online databases according to PRISMA guidelines, using keywords for HD, functional connectivity, and rs‐fMRI. We included studies investigating connectivity in presymptomatic (pre‐HD) and manifest HD gene carriers compared to healthy controls, implementing seed‐based connectivity, independent component analysis, regional property, and graph analysis approaches. Visual network showed reduced connectivity in manifest HD, while network/areas underpinning motor functions were consistently altered in both manifest HD and pre‐HD, showing disease stage‐dependent changes. Cognitive networks underlying executive and attentional functions showed divergent anterior–posterior alterations, possibly reflecting compensatory mechanisms. The involvement of these networks in pre‐HD is still unclear. In conclusion, aberrant connectivity of the sensory‐motor network is observed in the early stage of HD while, as pathology spreads, other networks might be affected, such as the visual and executive/attentional networks. Moreover, sensory‐motor and executive networks exhibit hyper‐ and hypo‐connectivity patterns following different spatiotemporal trajectories. These findings could potentially help to implement future huntingtin‐lowering interventions.

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Section: Resultsmentioning

confidence: 99%

Aberrant brain network connectivity in presymptomatic and manifest Huntington's disease: A systematic review

Pini

Jacquemot

Cagnin

et al. 2019

Human Brain Mapping

View full text Add to dashboard Cite

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“…The indirect pathway mediates learning from loss, whereas the direct pathway mediates learning from reward . There is selective early degeneration of the indirect pathway at the level of the striatum compared with the direct pathway in HD, and apathy in HD has been correlated with altered resting‐state activity in a circuit centered on the dorsal striatum . Apathy in HD has also been correlated with amygdala atrophy and hypoperfusion .…”

Section: Discussionmentioning

confidence: 99%

“…25,28,[69][70][71][72] There is selective early degeneration of the indirect pathway at the level of the striatum compared with the direct pathway in HD, 19,26,27 and apathy in HD has been correlated with altered resting-state activity in a circuit centered on the dorsal striatum. 73 Apathy in HD has also been correlated with amygdala atrophy and hypoperfusion. 74 The amygdala is heavily involved in stimulus-response learning, and there is evidence to suggest distinct neuronal groups are involved with learning from loss and reward.…”

Section: Discussionmentioning

confidence: 99%

Insensitivity to loss predicts apathy in huntington's disease

et al. 2019

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Background Apathy is a deficit in goal‐directed behavior that significantly affects quality of life and function. It is common in Huntington's disease and other disorders affecting corticostriatal pathways. Deficits in processing of reward, altered effort, and executive dysfunction are associated with apathy in other disorders, but the cognitive processes leading to apathy in Huntington's disease remain largely unknown. A previously reported deficit in learning from losses in Huntington's disease raises the possibility of a hitherto unrecognized mechanism leading to apathy. This study's objective was to delineate the cognitive processes associated with apathy in HD. Methods We tested 51 Huntington's disease participants and 26 controls on a battery of novel and established measures to assess the contribution to apathy in Huntington's disease of executive function, reward value, reward‐effort calculations, instrumental learning, and response to reward and loss. Results Huntington's disase participants had deficits in instrumental learning with impaired response to loss, but no evidence to suggest altered reward‐related behavior or effort. We also saw an executive dysfunction contribution to apathy in Huntington's disease. Discussion We report the novel finding that apathy in Huntington's disease is associated with blunted responses to losses and impaired instrumental learning. This association is consistent with the known early degeneration of the indirect pathway and amygdala involvement in apathy in Huntington's disease, but is previously unreported in any disorder. In keeping with the comparative preservation of the ventral striatum and orbitofrontal cortex in Huntington's disease, reward valuation and reward‐effort calculations did not contribute to apathy. © 2019 International Parkinson and Movement Disorder Society

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“…One of the most frequently used edge‐wise methods is the network‐based statistic (NBS) (Zalesky et al, ). The NBS is designed to identify clustered effects in brain graphs, and has been used in different study settings (Abós et al, ; Cocchi et al, ; Conti et al, ; McColgan et al, ; Rigon, Voss, Turkstra, Mutlu, & Duff, ; Roberts et al, ). Specifically, the NBS is a technique that aims to identify connected components, consisting of neighboring edges that display statistical effects above a predetermined threshold (Zalesky et al, ).…”

Section: Introductionmentioning

confidence: 99%

Statistical inference in brain graphs using threshold‐free network‐based statistics

Baggio

Abós

Segura

et al. 2018

Human Brain Mapping

107

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The description of brain networks as graphs where nodes represent different brain regions and edges represent a measure of connectivity between a pair of nodes is an increasingly used approach in neuroimaging research. The development of powerful methods for edge-wise group-level statistical inference in brain graphs while controlling for multiple-testing associated false-positive rates, however, remains a difficult task. In this study, we use simulated data to assess the properties of threshold-free network-based statistics (TFNBS). The TFNBS combines threshold-free cluster enhancement, a method commonly used in voxel-wise statistical inference, and network-based statistic (NBS), which is frequently used for statistical analysis of brain graphs. Unlike the NBS, TFNBS generates edge-wise significance values and does not require the a priori definition of a hard cluster-defining threshold. Other test parameters, nonetheless, need to be set. We show that it is possible to find parameters that make TFNBS sensitive to strong and topologically clustered effects, while appropriately controlling false-positive rates. Our results show that the TFNBS is an adequate technique for the statistical assessment of brain graphs.

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Structural and functional brain network correlates of depressive symptoms in premanifest Huntington's disease

Cited by 31 publications

References 41 publications

Aberrant brain network connectivity in presymptomatic and manifest Huntington's disease: A systematic review

Aberrant brain network connectivity in presymptomatic and manifest Huntington's disease: A systematic review

Insensitivity to loss predicts apathy in huntington's disease

Statistical inference in brain graphs using threshold‐free network‐based statistics

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