Resting-State Functional Connectivity in Late-Life Depression: Higher Global Connectivity and More Long Distance Connections

Bohr, Iwo; Kenny, Eva; Blamire, Andrew M.; O’Brien, John; Thomas, Alan; Richardson, Jonathan L.; Kaiser, Marcus

doi:10.3389/fpsyt.2012.00116

Cited by 64 publications

(60 citation statements)

References 77 publications

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“…Older depressed individuals, regardless of their cognitive impairment statuses, showed reduced local efficiency, which is reflective of deficient segregation of functional neural processing and lower levels of local connectedness. Our results of similar functional integration measures between non-depressed aMCI, cognitively normal LLD and control participants are consistent with recent investigations 21 23. However, Wang et al 24 revealed a longer characteristic path length in patients with aMCI; their sample was younger and more cognitively impaired than ours.…”

Section: Discussionsupporting

confidence: 92%

“…The regions with high betweenness centrality that are identified in our control group (posterior cingulate, insula, inferior frontal gyrus, VMPFC and cuneus) are in accordance with the highly connected hubs reported in previous functional network studies (see online supplementary table S1) 21 23. These hubs, which are essential for effective communication and robust network performance, are also critical nodes of the default mode and SNs, as extensively described in the literature 38.…”

Section: Discussionsupporting

confidence: 89%

“…As a function of normal ageing, the efficient network organisation is lost and community structure becomes less modular 20. 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.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionsupporting

confidence: 92%

Section: Discussionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

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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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“…Together, these additional hippocampal networks for past and future thinking are broadly consistent with the changes in restingstate connectivity commonly observed in depression (e.g., Bohr et al, 2013;Zhou et al, 2010;Zhu et al, 2012). Our findings suggest that, in depressed individuals, enhanced connectivity of both task-relevant networks (i.e., the default network) and recruitment of supplementary networks (i.e., dorsal attention network) can support successful autobiographical event generation.…”

Section: Discussionsupporting

confidence: 75%

Neural changes associated with the generation of specific past and future events in depression

Hach¹,

Tippett²,

Addis³

2014

Neuropsychologia

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“…Functional connectivity reductions between the mediodorsal nucleus and cerebellum were observed in SCZ but not in BD. Graph metric properties of the brain have also recently been assessed in late life depression [337]: Here connections spanning longer euclidean distances were increased after the onset of late life depression. Interestingly, this change did not influence the small world index.…”

Section: Chaptermentioning

confidence: 99%

Biometric markers for affective disorders

Lord¹

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Effective treatment of affective disorders is often challenged by incorrect or delayed diagnosis. Patients with bipolar disorder (BD) typically receive between 1 and 4 prior diagnoses of other mental health disorders preceding correct diagnosis, commonly with a delay from illness onset of 10 years, highlighting the difficulty of accurate diagnosis in a clinical setting. Most commonly, bipolar disorder is misdiagnosed as unipolar major depressive disorder (MDD), with between 25 and 50% of people diagnosed with depression being later re-classified as bipolar. The current problem of misdiagnosis is compounded by the lack of laboratory based quantitative testing for affective disorders available in clinical situations.The overarching objectives of this thesis are to demonstrate the utility of machine learning algorithms to neuroimaging data in order to provide a computational platform for classification, diagnosis and prediction of mood disorders. The thesis hence aims to unify three main themes -namely the clinical psychiatry of affective (mood) disorders, analytic principles of neuroimaging data (such as networkbased platforms) and machine learning.I first combine network theory and machine learning to address the issue of diagnostic classification using resting state functional magnetic resonance imaging (fMRI). I find that key metrics describing functional organization of the brain can be harnessed to yield accurate diagnostic boundaries in the data set I examine. In particular, the organization of ongoing brain activity into communities provides key information which distinguishes MDD from healthy controls.I next investigate a core group of brain regions which modulate network efficiency at a global scale, known as the rich club. Although this has previously been examined in structural brain networks, I apply it to resting state functional images. Furthermore, I investigate the robustness of the functional rich club following the correction of systematic artifacts in fMRI scans. Here I conclude a densely connected rich club is present in resting state fMRI across using all processing styles. However, the composition of members changes depending on key choices in the preprocessing of data. Further validation of the rich club should assess the impact of using different scanners or brain atlases.From chapter 4 onward, I shift the focus from depression to bipolar disorder. Here I start by assessing the functional connectivity of the inferior frontal gyrus, a region which is down regulated during an ii emotional recognition task in first degree relatives of bipolar subjects. Using a technique capable of identifying subtle alterations in functional networks I identified a community of connected brain regions with substantially reduced functional connectivity in bipolar subjects. I used machine learning to distinguish people with bipolar from first degree relatives and a healthy cohort. This was most effective between bipolar subjects and control subjects, while identifying the first degree relatives from eith...

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Resting-State Functional Connectivity in Late-Life Depression: Higher Global Connectivity and More Long Distance Connections

Cited by 64 publications

References 77 publications

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

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

Neural changes associated with the generation of specific past and future events in depression

Biometric markers for affective disorders

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