Aberrant topographical organization in gray matter structural network in late life depression: a graph theoretical analysis

Lim, Hyun Kook; Jung, Wonjin; Aizenstein, Howard J.

doi:10.1017/s104161021300149x

Cited by 42 publications

(47 citation statements)

References 42 publications

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“…Using inter-regional correlations of gray matter volumes, another study in LLD also reported higher clustering in addition to longer path length (Ajilore et al., 2014), although no network differences were revealed by the same group in a subsequent analysis of white matter network on the same sample (Charlton et al., 2014). Preserved network organization in LLD has been reported in other studies using gray matter volumes (Lim et al., 2013) and functional data (Bohr et al., 2013). Furthermore, no study has performed a combined analysis of regional cortical thickness and network properties in the same sample, which will allow us to directly investigate the macro-level impact of cortical atrophy beyond the potentially affected regions.…”

Section: Introductionsupporting

confidence: 76%

“…To the best of our knowledge, this was the first study to compare regional cortical thickness and network measures in the same sample. Unlike the Voxel-based morphometry (VBM) approach in previous studies (Lim et al., 2013), we used regional cortical thickness to construct our structural covariance networks, thereby overcoming the main limitation of VBM, providing a mixed measure of the cortical gray matter, including surface area, cortical folding, as well as cortical thickness (Hutton et al., 2009). The structural covariance approach also offers another alternative approach that could sidestep the limited validity of DTI-based approaches to map cortico-cortical connectivity due to the multitude of branching and crisscrossing fibers.…”

Section: Discussionmentioning

confidence: 99%

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The segregated connectome of late-life depression: a combined cortical thickness and structural covariance analysis

Mak

Colloby

Thomas

et al. 2016

Neurobiology of Aging

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Late-life depression (LLD) has been associated with both generalized and focal neuroanatomical changes including gray matter atrophy and white matter abnormalities. However, previous literature has not been consistent and, in particular, its impact on the topology organization of brain networks remains to be established. In this multimodal study, we first examined cortical thickness, and applied graph theory to investigate structural covariance networks in LLD. Thirty-three subjects with LLD and 25 controls underwent T1-weighted, fluid-attenuated inversion recovery and clinical assessments. Freesurfer was used to perform vertex-wise comparisons of cortical thickness, whereas the Graph Analysis Toolbox (GAT) was implemented to construct and analyze the structural covariance networks. LLD showed a trend of lower thickness in the left insular region (p < 0.001 uncorrected). In addition, the structural network of LLD was characterized by greater segregation, particularly showing higher transitivity (i.e., measure of clustering) and modularity (i.e., tendency for a network to be organized into subnetworks). It was also less robust against random failure and targeted attacks. Despite relative cortical preservation, the topology of the LLD network showed significant changes particularly in segregation. These findings demonstrate the potential for graph theoretical approaches to complement conventional structural imaging analyses and provide novel insights into the heterogeneous etiology and pathogenesis of LLD.

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Section: Introductionsupporting

confidence: 76%

Section: Discussionmentioning

confidence: 99%

The segregated connectome of late-life depression: a combined cortical thickness and structural covariance analysis

Mak

Colloby

Thomas

et al. 2016

Neurobiology of Aging

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“…Genetic, demographic and host factors play an important role in AD development. Presence of APOE-ɛ4 allele has been associated with increased rate of cognitive decline and increased neuropathology including greater β-amyloid deposition and medial temporal lobe atrophy (Ohm et al, 1995; Dal Forno et al, 1996; Geroldi et al, 1999; Martins et al, 2005; Lim et al, 2013). Higher educational attainment has also been associated with reduced risk of AD in multiple studies (McDowell et al, 2007; Roe et al, 2007; Meng and D’Arcy, 2012) potentially through cognitive reserve mechanism (Stern, 2006; Brayne et al, 2010).…”

Section: Assessing Network-level Changes Associated With Cognitive Trmentioning

confidence: 99%

Neural correlates of cognitive intervention in persons at risk of developing Alzheimerâ€™s disease

Hosseini

Kramer

Kesler

2014

Front. Aging Neurosci.

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Cognitive training is an emergent approach that has begun to receive increased attention in recent years as a non-pharmacological, cost-effective intervention for Alzheimer’s disease (AD). There has been increasing behavioral evidence regarding training-related improvement in cognitive performance in early stages of AD. Although these studies provide important insight about the efficacy of cognitive training, neuroimaging studies are crucial to pinpoint changes in brain structure and function associated with training and to examine their overlap with pathology in AD. In this study, we reviewed the existing neuroimaging studies on cognitive training in persons at risk of developing AD to provide an overview of the overlap between neural networks rehabilitated by the current training methods and those affected in AD. The data suggest a consistent training-related increase in brain activity in medial temporal, prefrontal, and posterior default mode networks, as well as increase in gray matter structure in frontoparietal and entorhinal regions. This pattern differs from the observed pattern in healthy older adults that shows a combination of increased and decreased activity in response to training. Detailed investigation of the data suggests that training in persons at risk of developing AD mainly improves compensatory mechanisms and partly restores the affected functions. While current neuroimaging studies are quite helpful in identifying the mechanisms underlying cognitive training, the data calls for future multi-modal neuroimaging studies with focus on multi-domain cognitive training, network level connectivity, and individual differences in response to training.

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“…Altered topological properties of functional networks are associated with depression,21 22 and preclinical, prodromal and clinical AD 20 23–25. Recent graph theoretical applications to assess the grey matter structural covariance networks in LLD demonstrated vulnerabilities in the cortical-subcortical topological properties 26 27. Using diffusion tensor imaging, distinct changes in the network efficiency measures were associated with depression severity and remission in those with LLD 28 29.…”

Section: Introductionmentioning

confidence: 99%

Disrupted small world topology and modular organisation of functional networks in late-life depression with and without amnestic mild cognitive impairment

Ward

Liu

et al. 2014

J Neurol Neurosurg Psychiatry

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Background The topological architecture of the whole-brain functional networks in those with and without late-life depression (LLD) and amnestic mild cognitive impairment (aMCI) are unknown. Aims To investigate the differences in the small-world measures and the modular community structure of the functional networks between patients with LLD and aMCI when occurring alone or in combination and cognitively healthy nondepressed controls. Methods Seventy-nine elderly participants [LLD (n = 23), aMCI (n = 18), comorbid LLD and aMCI (n = 13), and controls (n = 25)] completed neuropsychiatric assessments. Graph theoretical methods were employed on resting-state functional connectivity magnetic resonance imaging data. Results LLD and aMCI comorbidity was associated with the greatest disruptions in functional integration measures (decreased global efficiency and increased path length); both LLD groups showed abnormal functional segregation (reduced local efficiency). The modular network organization was most variable in the comorbid group, followed by LLD-only patients. Decreased mean global, local and nodal efficiency metrics were associated with greater depressive symptom severity but not memory performance. Conclusions Consider the whole brain as a complex network may provide unique insights on the neurobiological underpinnings of LLD with and without cognitive impairment.

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Aberrant topographical organization in gray matter structural network in late life depression: a graph theoretical analysis

Abstract: Our findings suggest that the gray matter structural networks are not globally but regionally altered in LLD patients. This multivariate structural analysis using graph theory might provide a more appropriate paradigm for understanding complicated neurobiological mechanism of LLD.

Cited by 42 publications

References 42 publications

The segregated connectome of late-life depression: a combined cortical thickness and structural covariance analysis

The segregated connectome of late-life depression: a combined cortical thickness and structural covariance analysis

Neural correlates of cognitive intervention in persons at risk of developing Alzheimerâ€™s disease

Disrupted small world topology and modular organisation of functional networks in late-life depression with and without amnestic mild cognitive impairment

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