Alistair Perry

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).

Consistency-based thresholding of the human connectome

Abstract Densely seeded probabilistic tractography yields weighted networks that are nearly fully connected, hence containing many spurious fibers. It is thus necessary to prune spurious connections from probabilistically‐derived networks to obtain a more reliable overall estimate of the connectivity. A standard method is to threshold by weight, keeping only the strongest edges. Here, by measuring the consistency of edge weights across subjects, we propose a new thresholding method that aims to reduce the rate of false‐positives in group‐averaged connectivity matrices. Close inspection of the relationship between consistency, weight, and distance suggests that the most consistent edges are in fact those that are strong for their length, rather than simply strong overall. Hence retaining the most consistent edges preserves more long‐distance connections than traditional weight‐based thresholding, which penalizes long connections for being weak regardless of anatomy. By comparing our thresholded networks to mouse and macaque tracer data, we also show that consistency‐based thresholding exhibits the species‐invariant exponential decay of connection weights with distance, while weight‐based thresholding does not. We also show that consistency‐based thresholding can be used to identify highly consistent and highly inconsistent subnetworks across subjects, enabling more nuanced analyses of group‐level connectivity than just the mean connectivity. HighlightsThresholding is a common method for pruning networks.We developed a novel method for thresholding networks by intersubject consistency.Consistent edges tend to be those that are strong for their length, rather than strong overall.Our method replicates in an independent dataset.Consistent edges have similar distance dependence to mouse and macaque tracer data.

Whole-brain analytic measures of network communication reveal increased structure-function correlation in right temporal lobe epilepsy.

The in vivo structure-function relationship is key to understanding brain network reorganization due to pathologies. This relationship is likely to be particularly complex in brain network diseases such as temporal lobe epilepsy, in which disturbed large-scale systems are involved in both transient electrical events and long-lasting functional and structural impairments. Herein, we estimated this relationship by analyzing the correlation between structural connectivity and functional connectivity in terms of analytical network communication parameters. As such, we targeted the gradual topological structure-function reorganization caused by the pathology not only at the whole brain scale but also both in core and peripheral regions of the brain. We acquired diffusion (dMRI) and resting-state fMRI (rsfMRI) data in seven right-lateralized TLE (rTLE) patients and fourteen healthy controls and analyzed the structure-function relationship by using analytical network communication metrics derived from the structural connectome. In rTLE patients, we found a widespread hypercorrelated functional network. Network communication analysis revealed greater unspecific branching of the shortest path (search information) in the structural connectome and a higher global correlation between the structural and functional connectivity for the patient group. We also found evidence for a preserved structural rich-club in the patient group. In sum, global augmentation of structure-function correlation might be linked to a smaller functional repertoire in rTLE patients, while sparing the central core of the brain which may represent a pathway that facilitates the spread of seizures.

Structural dysconnectivity of key cognitive and emotional hubs in young people at high genetic risk for bipolar disorder

Emerging evidence suggests that psychiatric disorders are associated with disturbances in structural brain networks. Little is known, however, about brain networks in those at high risk (HR) of bipolar disorder (BD), with such disturbances carrying substantial predictive and etiological value. Whole-brain tractography was performed on diffusion-weighted images acquired from 84 unaffected HR individuals with at least one first-degree relative with BD, 38 young patients with BD and 96 matched controls (CNs) with no family history of mental illness. We studied structural connectivity differences between these groups, with a focus on highly connected hubs and networks involving emotional centres. HR participants showed lower structural connectivity in two lateralised sub-networks centred on bilateral inferior frontal gyri and left insular cortex, as well as increased connectivity in a right lateralised limbic sub-network compared with CN subjects. BD was associated with weaker connectivity in a small right-sided sub-network involving connections between fronto-temporal and temporal areas. Although these sub-networks preferentially involved structural hubs, the integrity of the highly connected structural backbone was preserved in both groups. Weaker structural brain networks involving key emotional centres occur in young people at genetic risk of BD and those with established BD. In contrast to other psychiatric disorders such as schizophrenia, the structural core of the brain remains intact, despite the local involvement of network hubs. These results add to our understanding of the neurobiological correlates of BD and provide predictions for outcomes in young people at high genetic risk for BD.

The independent influences of age and education on functional brain networks and cognition in healthy older adults

Healthy aging is accompanied by a constellation of changes in cognitive processes and alterations in functional brain networks. The relationships between brain networks and cognition during aging in later life are moderated by demographic and environmental factors, such as prior education, in a poorly understood manner. Using multivariate analyses, we identified three latent patterns (or modes) linking resting‐state functional connectivity to demographic and cognitive measures in 101 cognitively normal elders. The first mode (P = 0.00043) captures an opposing association between age and core cognitive processes such as attention and processing speed on functional connectivity patterns. The functional subnetwork expressed by this mode links bilateral sensorimotor and visual regions through key areas such as the parietal operculum. A strong, independent association between years of education and functional connectivity loads onto a second mode (P = 0.012), characterized by the involvement of key hub regions. A third mode (P = 0.041) captures weak, residual brain–behavior relations. Our findings suggest that circuits supporting lower level cognitive processes are most sensitive to the influence of age in healthy older adults. Education, and to a lesser extent, executive functions, load independently onto functional networks—suggesting that the moderating effect of education acts upon networks distinct from those vulnerable with aging. This has important implications in understanding the contribution of education to cognitive reserve during healthy aging. Hum Brain Mapp 38:5094–5114, 2017.

Fronto-limbic dysconnectivity leads to impaired brain network controllability in young people with bipolar disorder and those at high genetic risk

Recent investigations have used diffusion-weighted imaging to reveal disturbances in the neurocircuitry that underlie cognitive-emotional control in bipolar disorder (BD) and in unaffected siblings or children at high genetic risk (HR). It has been difficult to quantify the mechanism by which structural changes disrupt the superimposed brain dynamics, leading to the emotional lability that is characteristic of BD. Average controllability is a concept from network control theory that extends structural connectivity data to estimate the manner in which local neuronal fluctuations spread from a node or subnetwork to alter the state of the rest of the brain. We used this theory to ask whether structural connectivity deficits previously observed in HR individuals (n = 84, mean age 22.4), patients with BD (n = 38, mean age 23.9), and age- and gender-matched controls (n = 96, mean age 22.6) translate to differences in the ability of brain systems to be manipulated between states. Localized impairments in network controllability were seen in the left parahippocampal, left middle occipital, left superior frontal, right inferior frontal, and right precentral gyri in BD and HR groups. Subjects with BD had distributed deficits in a subnetwork containing the left superior and inferior frontal gyri, postcentral gyrus, and insula (p = 0.004). HR participants had controllability deficits in a right-lateralized subnetwork involving connections between the dorsomedial and ventrolateral prefrontal cortex, the superior temporal pole, putamen, and caudate nucleus (p = 0.008). Between-group controllability differences were attenuated after removal of topological factors by network randomization. Some previously reported differences in network connectivity were not associated with controllability-differences, likely reflecting the contribution of more complex brain network properties. These analyses highlight the potential functional consequences of altered brain networks in BD, and may guide future clinical interventions.

Differentiation of Alzheimer's disease based on local and global parameters in personalized Virtual Brain models

Alzheimers disease (AD) is marked by cognitive dysfunction emerging from neuropathological processes impacting brain function. AD affects brain dynamics at the local level, such as changes in the balance of inhibitory and excitatory neuronal populations, as well as long-range changes to the global network. Individual differences in these changes as they relate to behaviour are poorly understood. Here, we use a multi-scale neurophysiological model, “The Virtual Brain (TVB)”, based on empirical multi-modal neuroimaging data, to study how local and global dynamics correlate with individual differences in cognition. In particular, we modeled individual resting-state functional activity of 124 individuals across the behavioural spectrum from healthy aging, to amnesic Mild Cognitive Impairment (MCI), to AD. The model parameters required to accurately simulate empirical functional brain imaging data correlated significantly with cognition, and exceeded the predictive capacity of empirical connectomes.

The site of stimulation moderates neuropsychiatric symptoms after subthalamic deep brain stimulation for Parkinson's disease

Deep brain stimulation of the subthalamic nucleus for Parkinsons disease is an established advanced therapy that addresses motor symptoms and improves quality of life. However, it has also been associated with neuropsychiatric symptoms such as impulsivity and hypomania. When significant, these symptoms can be distressing, necessitating psychiatric intervention. However, a comprehensive analysis of neurocognitive and neuropsychiatric outcomes with reference to the site of subthalamic stimulation has not been undertaken. We examined this matter in a consecutive sample of 64 persons with Parkinsons disease undertaking subthalamic deep brain stimulation. Participants were assessed with a battery of neuropsychiatric instruments at baseline and at repeated postoperative intervals. A psychiatrist identified patients with emergent, clinically-significant symptoms due to stimulation. The site of the active electrode contact and a simulated volume of activated tissue were evaluated with reference to putative limbic, associative and motor subregions of the subthalamic nucleus. We studied anatomical correlates of longitudinal neuropsychiatric change and delineated specific subthalamic regions associated with neuropsychiatric impairment. We tested the ability of these data to predict clinically-significant symptoms. Subthalamic stimulation within the right associative subregion was associated with inhibitory errors on the Excluded Letter Fluency task at 6-weeks (p = 0.023) and 13-weeks postoperatively (p = 0.0017). A cluster of subthalamic voxels associated with inhibitory errors was identified in the right associative and motor subregions. At 6-weeks, clinically-significant neuropsychiatric symptoms were associated with the distance of the active contact to the right associative subregion (p = 0.0026) and stimulation within the right associative subregion (p = 0.0009). At 13-weeks, clinically-significant symptoms were associated with the distance to the right (p = 0.0027) and left (p = 0.0084) associative subregions and stimulation within the right associative subregion (p = 0.0026). Discrete clusters of subthalamic voxels associated with high and low likelihood of postoperative neuropsychiatric symptoms were identified in ventromedial and dorsolateral zones, respectively. When a classifier was trained on these data, clinically-significant symptoms were predicted with an accuracy of 79%. These data underscore the importance of accurate electrode targeting, contact selection and device programming to reduce postoperative neuropsychiatric impairment. The ability to predict neuropsychiatric symptoms based on subthalamic data may permit anticipation and prevention of these occurrences, improving safety and tolerability.

White matter alterations in the internal capsule and psychomotor impairment in melancholic depression

Emerging evidence suggests that structural brain abnormalities may play a role in the pathophysiology of melancholic depression. We set out to test whether diffusion-derived estimates of white matter structure were disrupted in melancholia in regions underpinning psychomotor function. We hypothesized that those with melancholia (and evidencing impaired psychomotor function) would show disrupted white matter organization in internal capsule subdivisions. Diffusion magnetic resonance imaging (dMRI) data were acquired from 22 melancholic depressed, 23 non-melancholic depressed, and 29 healthy control participants. Voxel-wise fractional anisotropy (FA), radial diffusivity (RD), and axial diffusivity (AD) values were derived for anterior, posterior, and retrolenticular limbs of the internal capsule and compared between groups. Neuropsychological (reaction time) and psychomotor functioning were assessed and correlated against FA. Fractional anisotropy was distinctly increased, whilst RD was decreased, in the right anterior internal capsule in those with melancholia, compared to controls. The right anterior limb of the internal capsule correlated with clinical ratings of psychomotor disturbance, and reduced psychomotor speed was associated with increased FA values in the right retrolenticular limb in those with melancholia. Our findings highlight a distinct disturbance in the local white matter arrangement in specific regions of the internal capsule in melancholia, which in turn is associated with psychomotor dysfunction. This study clarifies the contribution of structural brain integrity to the phenomenology of melancholia, and may assist future efforts seeking to integrate neurobiological markers into depression subtyping.