Anton Lord

Changes in Community Structure of Resting State Functional Connectivity in Unipolar Depression

Major depression is a prevalent disorder that imposes a significant burden on society, yet objective laboratory-style tests to assist in diagnosis are lacking. We employed network-based analyses of “resting state” functional neuroimaging data to ascertain group differences in the endogenous cortical activity between healthy and depressed subjects. We additionally sought to use machine learning techniques to explore the ability of these network-based measures of resting state activity to provide diagnostic information for depression. Resting state fMRI data were acquired from twenty two depressed outpatients and twenty two healthy subjects matched for age and gender. These data were anatomically parcellated and functional connectivity matrices were then derived using the linear correlations between the BOLD signal fluctuations of all pairs of cortical and subcortical regions. We characterised the hierarchical organization of these matrices using network-based matrics, with an emphasis on their mid-scale “modularity” arrangement. Whilst whole brain measures of organization did not differ between groups, a significant rearrangement of their community structure was observed. Furthermore we were able to classify individuals with a high level of accuracy using a support vector machine, primarily through the use of a modularity-based metric known as the participation index. In conclusion, the application of machine learning techniques to features of resting state fMRI network activity shows promising potential to assist in the diagnosis of major depression, now suggesting the need for validation in independent data sets.

Disruption of structure–function coupling in the schizophrenia connectome

Neuroimaging studies have demonstrated that the phenomenology of schizophrenia maps onto diffuse alterations in large-scale functional and structural brain networks. However, the relationship between structural and functional deficits remains unclear. To answer this question, patients with established schizophrenia and matched healthy controls underwent resting-state functional and diffusion weighted imaging. The network-based statistic was used to characterize between-group differences in whole-brain functional connectivity. Indices of white matter integrity were then estimated to assess the structural correlates of the functional alterations observed in patients. Finally, group differences in the relationship between indices of functional and structural brain connectivity were determined. Compared to controls, patients with schizophrenia showed decreased functional connectivity and impaired white matter integrity in a distributed network encompassing frontal, temporal, thalamic, and striatal regions. In controls, strong interregional coupling in neural activity was associated with well-myelinated white matter pathways in this network. This correspondence between structure and function appeared to be absent in patients with schizophrenia. In two additional disrupted functional networks, encompassing parietal, occipital, and temporal cortices, the relationship between function and structure was not affected. Overall, results from this study highlight the importance of considering not only the separable impact of functional and structural connectivity deficits on the pathoaetiology of schizophrenia, but also the implications of the complex nature of their interaction. More specifically, our findings support the core nature of fronto-striatal, fronto-thalamic, and fronto-temporal abnormalities in the schizophrenia connectome.

The contribution of geometry to the human connectome.

The human connectome is a topologically complex, spatially embedded network. While its topological properties have been richly characterized, the constraints imposed by its spatial embedding are poorly understood. By applying a novel resampling method to tractography data, we show that the brains spatial embedding makes a major, but not definitive, contribution to the topology of the human connectome. We first identify where the brains structural hubs would likely be located if geometry was the sole determinant of brain topology. Empirical networks show a widespread shift away from this geometric center toward more peripheral interconnected skeletons in each hemisphere, with discrete clusters around the anterior insula, and the anterior and posterior midline regions of the cortex. A relatively small number of strong inter-hemispheric connections assimilate these intra-hemispheric structures into a rich club, whose connections are locally more clustered but globally longer than predicted by geometry. We also quantify the extent to which the segregation, integration, and modularity of the human brain are passively inherited from its geometry. These analyses reveal novel insights into the influence of spatial geometry on the human connectome, highlighting specific topological features that likely confer functional advantages but carry an additional metabolic cost.

The organisation of the elderly connectome

Investigations of the human connectome have elucidated core features of adult structural networks, particularly the crucial role of hub-regions. However, little is known regarding network organisation of the healthy elderly connectome, a crucial prelude to the systematic study of neurodegenerative disorders. Here, whole-brain probabilistic tractography was performed on high-angular diffusion-weighted images acquired from 115 healthy elderly subjects (age 76-94 years; 65 females). Structural networks were reconstructed between 512 cortical and subcortical brain regions. We sought to investigate the architectural features of hub-regions, as well as left-right asymmetries, and sexual dimorphisms. We observed that the topology of hub-regions is consistent with a young adult population, and previously published adult connectomic data. More importantly, the architectural features of hub connections reflect their ongoing vital role in network communication. We also found substantial sexual dimorphisms, with females exhibiting stronger inter-hemispheric connections between cingulate and prefrontal cortices. Lastly, we demonstrate intriguing left-lateralized subnetworks consistent with the neural circuitry specialised for language and executive functions, whilst rightward subnetworks were dominant in visual and visuospatial streams. These findings provide insights into healthy brain ageing and provide a benchmark for the study of neurodegenerative disorders such as Alzheimers disease (AD) and frontotemporal dementia (FTD).

Dissociable effects of local inhibitory and excitatory theta-burst stimulation on large-scale brain dynamics

Normal brain function depends on a dynamic balance between local specialization and large-scale integration. It remains unclear, however, how local changes in functionally specialized areas can influence integrated activity across larger brain networks. By combining transcranial magnetic stimulation with resting-state functional magnetic resonance imaging, we tested for changes in large-scale integration following the application of excitatory or inhibitory stimulation on the human motor cortex. After local inhibitory stimulation, regions encompassing the sensorimotor module concurrently increased their internal integration and decreased their communication with other modules of the brain. There were no such changes in modular dynamics following excitatory stimulation of the same area of motor cortex nor were there changes in the configuration and interactions between core brain hubs after excitatory or inhibitory stimulation of the same area. These results suggest the existence of selective mechanisms that integrate local changes in neural activity, while preserving ongoing communication between brain hubs.

Dissociation of glutamate and cortical thickness is restricted to regions subserving trait but not state markers in major depressive disorder

BACKGROUND The anterior cingulate cortex (ACC) plays an important role in the neuropathology of major depressive disorder (MDD). So far, the effect of local cortical alteration on metabolites in multiple subdivisions of ACC has not been studied. We aimed to investigate structural and biochemical changes and their relationship in the pregenual ACC (pgACC), dorsal ACC (dACC) in MDD. METHODS We obtained magnetic resonance spectroscopy (MRS) in two investigated regions for 24 depressed patients and matched controls. In each region, cortical thickness (CTh) was calculated within a template mask based on its MRS voxel. We investigated neurotransmitter concentrations of Glx, N-acetyl aspartate (NAA), and myo-inositol (m-Ins) in two investigated regions, as well as their relationships with CTh in depressed individuals and healthy controls. RESULTS Patients showed significantly lower cortical thickness in dACC compared to controls. Glx in dACC significantly correlated with CTh in healthy controls but not MDD patients, while NAA and CTh in dACC significantly correlated in both groups. A marginal decrease of Glx in pgACC was found in the subgroup of more severely depressive patients, compared to the mildly depressed patients. LIMITATIONS Modest sample size and lack of episodes of depression may limit the generalizability of our findings. CONCLUSION Our results indicate an abolished CTh-MRS relation in dACC-associated with structural decline-but not in pgACC, where acute MRS alterations prevailed. Our study provides the first evidence of a neurochemical basis explaining some of the inter-individual variability in CTh in MDD.

Functional Dysconnection of the Inferior Frontal Gyrus in Young People With Bipolar Disorder or at Genetic High Risk

BACKGROUND Bipolar disorder (BD) is characterized by a dysregulation of affect and impaired integration of emotion with cognition. These traits are also expressed in probands at high genetic risk of BD. The inferior frontal gyrus (IFG) is a key cortical hub in the circuits of emotion and cognitive control, and it has been frequently associated with BD. Here, we studied resting-state functional connectivity of the left IFG in participants with BD and in those at increased genetic risk. METHODS Using resting-state functional magnetic resonance imaging we compared 49 young BD participants, 71 individuals with at least one first-degree relative with BD (at-risk), and 80 control subjects. We performed between-group analyses of the functional connectivity of the left IFG and used graph theory to study its local functional network topology. We also used machine learning to study classification based solely on the functional connectivity of the IFG. RESULTS In BD, the left IFG was functionally dysconnected from a network of regions, including bilateral insulae, ventrolateral prefrontal gyri, superior temporal gyri, and the putamen (p < .001). A small network incorporating neighboring insular regions and the anterior cingulate cortex showed weaker functional connectivity in at-risk than control participants (p < .006). These constellations of regions overlapped with frontolimbic regions that a machine learning classifier selected as predicting group membership with an accuracy significantly greater than chance. CONCLUSIONS Functional dysconnectivity of the IFG from regions involved in emotional regulation may represent a trait abnormality for BD and could potentially aid clinical diagnosis.

Reduced functional connectivity in the thalamo‐insular subnetwork in patients with acute anorexia nervosa

The neural underpinnings of anorexia nervosa (AN) are poorly understood. Results from existing functional brain imaging studies using disorder‐relevant food‐ or body‐stimuli have been heterogeneous and may be biased due to varying compliance or strategies of the participants. In this study, resting state functional connectivity imaging was used. To explore the distributed nature and complexity of brain function we characterized network patterns in patients with acute AN. Thirty‐five unmedicated female acute AN patients and 35 closely matched healthy female participants underwent resting state functional magnetic resonance imaging. We used a network‐based statistic (NBS) approach [Zalesky et al., 2010a] to identify differences between groups by isolating a network of interconnected nodes with a deviant connectivity pattern. Group comparison revealed a subnetwork of connections with decreased connectivity including the amygdala, thalamus, fusiform gyrus, putamen and the posterior insula as the central hub in the patient group. Results were not driven by changes in intranodal or global connectivity. No network could be identified where AN patients had increased coupling. Given the known involvement of the identified thalamo‐insular subnetwork in interoception, decreased connectivity in AN patients in these nodes might reflect changes in the propagation of sensations that alert the organism to urgent homeostatic imbalances and pain‐processes that are known to be severely disturbed in AN and might explain the striking discrepancy between patients actual and perceived internal body state. Hum Brain Mapp 36:1772–1781, 2015.

Richness in Functional Connectivity Depends on the Neuronal Integrity within the Posterior Cingulate Cortex

The brains connectivity skeleton—a rich club of strongly interconnected members—was initially shown to exist in human structural networks, but recent evidence suggests a functional counterpart. This rich club typically includes key regions (or hubs) from multiple canonical networks, reducing the cost of inter-network communication. The posterior cingulate cortex (PCC), a hub node embedded within the default mode network, is known to facilitate communication between brain networks and is a key member of the “rich club.” Here, we assessed how metabolic signatures of neuronal integrity and cortical thickness influence the global extent of a functional rich club as measured using the functional rich club coefficient (fRCC). Rich club estimation was performed on functional connectivity of resting state brain signals acquired at 3T in 48 healthy adult subjects. Magnetic resonance spectroscopy was measured in the same session using a point resolved spectroscopy sequence. We confirmed convergence of functional rich club with a previously established structural rich club. N-acetyl aspartate (NAA) in the PCC is significantly correlated with age (p = 0.001), while the rich club coefficient showed no effect of age (p = 0.106). In addition, we found a significant quadratic relationship between fRCC and NAA concentration in PCC (p = 0.009). Furthermore, cortical thinning in the PCC was correlated with a reduced rich club coefficient after accounting for age and NAA. In conclusion, we found that the fRCC is related to a marker of neuronal integrity in a key region of the cingulate cortex. Furthermore, cortical thinning in the same area was observed, suggesting that both cortical thinning and neuronal integrity in the hub regions influence functional integration of at a whole brain level.

Preprocessing strategy influences graph-based exploration of altered functional networks in major depression

Resting‐state fMRI studies have gained widespread use in exploratory studies of neuropsychiatric disorders. Graph metrics derived from whole brain functional connectivity studies have been used to reveal disease‐related variations in many neuropsychiatric disorders including major depression (MDD). These techniques show promise in developing diagnostics for these often difficult to identify disorders. However, the analysis of resting‐state datasets is increasingly beset by a myriad of approaches and methods, each with underlying assumptions. Choosing the most appropriate preprocessing parameters a priori is difficult. Nevertheless, the specific methodological choice influences graph‐theoretical network topologies as well as regional metrics. The aim of this study was to systematically compare different preprocessing strategies by evaluating their influence on group differences between healthy participants (HC) and depressive patients. We thus investigated the effects of common preprocessing variants, including global mean‐signal regression (GMR), temporal filtering, detrending, and network sparsity on group differences between brain networks of HC and MDD patients measured by global and nodal graph theoretical metrics. Occurrence of group differences in global metrics was absent in the majority of tested preprocessing variants, but in local graph metrics it is sparse, variable, and highly dependent on the combination of preprocessing variant and sparsity threshold. Sparsity thresholds between 16 and 22% were shown to have the greatest potential to reveal differences between HC and MDD patients in global and local network metrics. Our study offers an overview of consequences of methodological decisions and which neurobiological characteristics of MDD they implicate, adding further caution to this rapidly growing field. Hum Brain Mapp 37:1422‐1442, 2016.