The structural substrate of brain function: analysis of brain structural networks based on diffusion-weighted imaging

The brain is a complex neural network with a hierarchical organization and the mapping of its elements and connections is an important step towards the understanding of its function. Recent developments in diffusion-weighted imaging have provided the opportunity to reconstruct the whole-brain structural network in-vivo at a large scale level and to study the brain structural substrate in a framework that is close to the current understanding of brain function. However, methods to construct the connectome are still under development and they should be carefully evaluated. To this end, the first two studies included in my thesis aimed at improving the analytical tools specific to the methodology of brain structural networks. The first of these papers assessed the repeatability of the most common global and local network metrics used in literature to characterize the connectome, while in the second paper the validity of further metrics based on the concept of communicability was evaluated. Communicability is a broader measure of connectivity which accounts also for parallel and indirect connections. These additional paths may be important for reorganizational mechanisms in the presence of lesions as well as to enhance integration in the network. These studies showed good to excellent repeatability of global network metrics when the same methodological pipeline was applied, but more variability was detected when considering local network metrics or when using different thresholding strategies. In addition, communicability metrics have been found to add some insight into the integration properties of the network by detecting subsets of nodes that were highly interconnected or vulnerable to lesions. The other two studies used methods based on diffusion-weighted imaging to obtain knowledge concerning the relationship between functional and structural connectivity and about the etiology of schizophrenia. The third study integrated functional oscillations measured using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) as well as diffusion-weighted imaging data. The multimodal approach that was applied revealed a positive relationship between individual fluctuations of the EEG alpha-frequency and diffusion properties of specific connections of two resting-state networks. Finally, in the fourth study diffusion-weighted imaging was used to probe for a relationship between the underlying white matter tissue structure and season of birth in schizophrenia patients. The results are in line with the neurodevelopmental hypothesis of early pathological mechanisms as the origin of schizophrenia. The different analytical approaches selected in these studies also provide arguments for discussion of the current limitations in the analysis of brain structural networks. To sum up, the first studies presented in this thesis illustrated the potential of brain structural network analysis to provide useful information on features of brain functional segregation and integration using reliable network metrics. In the other two studies alternative approaches were presented. The common discussion of the four studies enabled us to highlight the benefits and possibilities for the analysis of the connectome as well as some current limitations.

Image processing methods for human brain connectivity analysis from in-vivo diffusion MRI

The structural connectivity of the brain is considered to encode species-wise and subject-wise patterns that will unlock large areas of understanding of the human brain. Currently, diffusion MRI of the living brain enables to map the microstructure of tissue, allowing to track the pathways of fiber bundles connecting the cortical regions across the brain. These bundles are summarized in a network representation called connectome that is analyzed using graph theory. The extraction of the connectome from diffusion MRI requires a large processing flow including image enhancement, reconstruction, segmentation, registration, diffusion tracking, etc. Although a concerted effort has been devoted to the definition of standard pipelines for the connectome extraction, it is still crucial to define quality assessment protocols of these workflows. The definition of quality control protocols is hindered by the complexity of the pipelines under test and the absolute lack of gold-standards for diffusion MRI data. Here we characterize the impact on structural connectivity workflows of the geometrical deformation typically shown by diffusion MRI data due to the inhomogeneity of magnetic susceptibility across the imaged object. We propose an evaluation framework to compare the existing methodologies to correct for these artifacts including whole-brain realistic phantoms. Additionally, we design and implement an image segmentation and registration method to avoid performing the correction task and to enable processing in the native space of diffusion data. We release PySDCev, an evaluation framework for the quality control of connectivity pipelines, specialized in the study of susceptibility-derived distortions. In this context, we propose Diffantom, a whole-brain phantom that provides a solution to the lack of gold-standard data. The three correction methodologies under comparison performed reasonably, and it is difficult to determine which method is more advisable. We demonstrate that susceptibility-derived correction is necessary to increase the sensitivity of connectivity pipelines, at the cost of specificity. Finally, with the registration and segmentation tool called regseg we demonstrate how the problem of susceptibility-derived distortion can be overcome allowing data to be used in their original coordinates. This is crucial to increase the sensitivity of the whole pipeline without any loss in specificity.

PCCP does exist

Neutral and cationic \[C-2,P-2] were investigated by a combination of mass spectrometry and electronic structure calculations. The cationic \[C-2,P-2](.+) potential energy surface including all relevant minima, transition states and fragmentation products was calculated at the B3LYP/6-311G(3df) level of theory. The most stable structures are linear PCCP.+ 1(.+) (E-rel=0 kcal mol(-1)), a three-membered ring with exocyclic phosphorus c-(PCC)-P 2(.+) (E-rel = 40.8 kcal mol(-1)), and the rhombic isomer 3(.+) (E-rel = 24.9 kcal mol(-1)). All fragmentation channels are significantly higher in energy than any of the \[C-2,P-2](.+) isomers. Experimentally, \[C-2,P-2](.+) ions are generated under high vacuum conditions by electron ionization of two different precursors. The fragmentation of \[C-2,P-2](.+) on collisional activation is preceded by rearrangement reactions which obscure the structural connectivity of the ions. The existence and the high stability of neutral \[C-2,P-2] were proved by a neutralization-reionization (NR) experiment. Although an unambiguous structural assignment of the neutral species cannot be drawn, both theory and experiment suggest that the long-sought neutral, linear PCCP 1 is generated using the NR technique.

Heritability of brain network topology in 853 twins and siblings

Anatomical brain networks change throughout life and with diseases. Genetic analysis of these networks may help identify processes giving rise to heritable brain disorders, but we do not yet know which network measures are promising for genetic analyses. Many factors affect the downstream results, such as the tractography algorithm used to define structural connectivity. We tested nine different tractography algorithms and four normalization methods to compute brain networks for 853 young healthy adults (twins and their siblings). We fitted genetic structural equation models to all nine network measures, after a normalization step to increase network consistency across tractography algorithms. Probabilistic tractography algorithms with global optimization (such as Probtrackx and Hough) yielded higher heritability statistics than 'greedy' algorithms (such as FACT) which process small neighborhoods at each step. Some global network measures (probtrackx-derived GLOB and ST) showed significant genetic effects, making them attractive targets for genome-wide association studies.

A novel brain partition highlights the modular skeleton shared by structure and function

Elucidating the intricate relationship between brain structure and function, both in healthy and pathological conditions, is a key challenge for modern neuroscience. Recent progress in neuroimaging has helped advance our understanding of this important issue, with diffusion images providing information about structural connectivity (SC) and functional magnetic resonance imaging shedding light on resting state functional connectivity (rsFC). Here, we adopt a systems approach, relying on modular hierarchical clustering, to study together SC and rsFC datasets gathered independently from healthy human subjects. Our novel approach allows us to find a common skeleton shared by structure and function from which a new, optimal, brain partition can be extracted. We describe the emerging common structure-function modules (SFMs) in detail and compare them with commonly employed anatomical or functional parcellations. Our results underline the strong correspondence between brain structure and resting-state dynamics as well as the emerging coherent organization of the human brain.

Synchrony between sensory and cognitive networks is associated with subclinical variation in autistic traits

© 2015 Young, Smith, Coutlee and Huettel.Individuals with autistic spectrum disorders exhibit distinct personality traits linked to attentional, social, and affective functions, and those traits are expressed with varying levels of severity in the neurotypical and subclinical population. Variation in autistic traits has been linked to reduced functional and structural connectivity (i.e., underconnectivity, or reduced synchrony) with neural networks modulated by attentional, social, and affective functions. Yet, it remains unclear whether reduced synchrony between these neural networks contributes to autistic traits. To investigate this issue, we used functional magnetic resonance imaging to record brain activation while neurotypical participants who varied in their subclinical scores on the Autism-Spectrum Quotient (AQ) viewed alternating blocks of social and nonsocial stimuli (i.e., images of faces and of landscape scenes). We used independent component analysis (ICA) combined with a spatiotemporal regression to quantify synchrony between neural networks. Our results indicated that decreased synchrony between the executive control network (ECN) and a face-scene network (FSN) predicted higher scores on the AQ. This relationship was not explained by individual differences in head motion, preferences for faces, or personality variables related to social cognition. Our findings build on clinical reports by demonstrating that reduced synchrony between distinct neural networks contributes to a range of subclinical autistic traits.

Mind wandering away from pain dynamically engages antinociceptive and default mode brain networks

Human minds often wander away from their immediate sensory environment. It remains unknown whether such mind wandering is unsystematic or whether it lawfully relates to an individual’s tendency to attend to salient stimuli such as pain and their associated brain structure/function. Studies of pain–cognition interactions typically examine explicit manipulation of attention rather than spontaneous mind wandering. Here we sought to better represent natural fluctuations in pain in daily life, so we assessed behavioral and neural aspects of spontaneous disengagement of attention from pain. We found that an individual’s tendency to attend to pain related to the disruptive effect of pain on his or her cognitive task performance. Next, we linked behavioral findings to neural networks with strikingly convergent evidence from functional magnetic resonance imaging during pain coupled with thought probes of mind wandering, dynamic resting state activity fluctuations, and diffusion MRI. We found that (i) pain-induced default mode network (DMN) deactivations were attenuated during mind wandering away from pain; (ii) functional connectivity fluctuations between the DMN and periaqueductal gray (PAG) dynamically tracked spontaneous attention away from pain; and (iii) across individuals, stronger PAG–DMN structural connectivity and more dynamic resting state PAG–DMN functional connectivity were associated with the tendency to mind wander away from pain. These data demonstrate that individual tendencies to mind wander away from pain, in the absence of explicit manipulation, are subserved by functional and structural connectivity within and between default mode and antinociceptive descending modulation networks.

The impact of fragmentation and density regulation on forest succession in the Atlantic rain forest

The Atlantic Rain Forest, an important biodiversity hot spot, has faced severe habitat loss since the last century which has resulted in a highly fragmented landscape with a large number of small forest patches (<100 ha). For conservation planning it is essential to understand how current and future forest regeneration depends on ecological processes, fragment size and the connection to the regional seed pool. We have investigated the following questions by applying the forest growth simulation model FORMIND to the situation of the Atlantic Forest in the state of Sao Paulo, SE Brazil: (1) which set of parameters describing the local regeneration and level of density regulation can reproduce the biomass distribution and stem density of an old growth forest in a reserve? (2) Which additional processes apart from those describing the dynamics of an old growth forest, drive forest succession of small isolated fragments? (3) Which role does external seed input play during succession? Therefore, more than 300 tree species have been classified into nine plant functional types (PFTs), which are characterized by maximum potential height and shade tolerance. We differentiate between two seed dispersal modes: (i) local dispersal, i.e. all seedlings originated from fertile trees within the simulated area and (ii) external seed rain. Local seed dispersal has been parameterized following the pattern oriented approach, using biomass estimates of old growth forest. We have found that moderate density regulation is essential to achieve coexistence for a broad range of regeneration parameters. Considering the expected uncertainty and variability in the regeneration processes it is important that the forest dynamics are robust to variations in the regeneration parameters. Furthermore, edge effects such as increased mortality at the border and external seed rain have been necessary to reproduce the patterns for small isolated fragments. Overall, simulated biomass is much lower in the fragments compared to the continuous forest, whereas shade tolerant species are affected most strongly by fragmentation. Our simulations can supplement empirical studies by extrapolating local knowledge on edge effects of fragments to larger temporal and spatial scales. In particular our results show the importance of external seed rain and therefore highlight the importance of structural connectivity between regenerating fragments and mature forest stands. (C) 2009 Elsevier B.V. All rights reserved.

The influence of landscape characteristics and home-range size on the quantification of landscape-genetics relationships

A common approach used to estimate landscape resistance involves comparing correlations of ecological and genetic distances calculated among individuals of a species. However, the location of sampled individuals may contain some degree of spatial uncertainty due to the natural variation of animals moving through their home range or measurement error in plant or animal locations. In this study, we evaluate the ways that spatial uncertainty, landscape characteristics, and genetic stochasticity interact to influence the strength and variability of conclusions about landscape-genetics relationships. We used a neutral landscape model to generate 45 landscapes composed of habitat and non-habitat, varying in percent habitat, aggregation, and structural connectivity (patch cohesion). We created true and alternate locations for 500 individuals, calculated ecological distances (least-cost paths), and simulated genetic distances among individuals. We compared correlations between ecological distances for true and alternate locations. We then simulated genotypes at 15 neutral loci and investigated whether the same influences could be detected in simple Mantel tests and while controlling for the effects of isolation-by distance using the partial Mantel test. Spatial uncertainty interacted with the percentage of habitat in the landscape, but led to only small reductions in correlations. Furthermore, the strongest correlations occurred with low percent habitat, high aggregation, and low to intermediate levels of cohesion. Overall genetic stochasticity was relatively low and was influenced by landscape characteristics.

Legislação e geotecnologias na definição das áreas de preservação permanente e das reservas legais: aplicação à bacia do Córrego das Posses, município de Extrema - MG

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Alterations of white matter integrity related to motor activity in schizophrenia

Altered structural connectivity is a key finding in schizophrenia, but the meaning of white matter alterations for behavior is rarely studied. In healthy subjects, motor activity correlated with white matter integrity in motor tracts. To explore the relation of motor activity and fractional anisotropy (FA) in schizophrenia, we investigated 19 schizophrenia patients and 24 healthy control subjects using Diffusion Tensor Imaging (DTI) and actigraphy on the same day. Schizophrenia patients had lower activity levels (AL). In both groups linear relations of AL and FA were detected in several brain regions. Schizophrenia patients had lower FA values in prefrontal and left temporal clusters. Furthermore, using a general linear model, we found linear negative associations of FA and AL underneath the right supplemental motor area (SMA), the right precentral gyrus and posterior cingulum in patients. This effect within the SMA was not seen in controls. This association in schizophrenia patients may contribute to the well known dysfunctions of motor control. Thus, structural disconnectivity could lead to disturbed motor behavior in schizophrenia.

Cortico-cortical white matter motor pathway microstructure is related to psychomotor retardation in major depressive disorder

Alterations of brain structure and function have been associated with psychomotor retardation in major depressive disorder (MDD). However, the association of motor behaviour and white matter integrity of motor pathways in MDD is unclear. The aim of the present study was to first investigate structural connectivity of white matter motor pathways in MDD. Second, we explore the relation of objectively measured motor activity and white matter integrity of motor pathways in MDD. Therefore, 21 patients with MDD and 21 healthy controls matched for age, gender, education and body mass index underwent diffusion tensor imaging and 24 hour actigraphy (measure of the activity level) the same day. Applying a probabilistic fibre tracking approach we extracted connection pathways between the dorsolateral prefrontal cortex (dlPFC), the rostral anterior cingulate cortex (rACC), the pre-supplementary motor area (pre-SMA), the SMA-proper, the primary motor cortex (M1), the caudate nucleus, the putamen, the pallidum and the thalamus. Patients had lower activity levels and demonstrated increased mean diffusivity (MD) in pathways linking left pre-SMA and SMA-proper, and right SMA-proper and M1. Exploratory analyses point to a positive association of activity level and mean-fractional anisotropy in the right rACC-pre-SMA connection in MDD. Only MDD patients with low activity levels had a negative linear association of activity level and mean-MD in the left dlPFC-pre-SMA connection. Our results point to structural alterations of cortico-cortical white matter motor pathways in MDD. Altered white matter organisation of rACC-pre-SMA and dlPFC-pre-SMA pathways may contribute to movement initiation in MDD.

Altered cortico-basal ganglia motor pathways reflect reduced volitional motor activity in schizophrenia

Little is known about the neurobiology of hypokinesia in schizophrenia. Therefore, the aim of this study was to investigate alterations of white matter motor pathways in schizophrenia and to relate our findings to objectively measured motor activity. We examined 21 schizophrenia patients and 21 healthy controls using diffusion tensor imaging and actigraphy. We applied a probabilistic fibre tracking approach to investigate pathways connecting the dorsolateral prefrontal cortex (dlPFC), the rostral anterior cingulate cortex (rACC), the pre-supplementary motor area (pre-SMA), the supplementary motor area proper (SMA-proper), the primary motor cortex (M1), the caudate nucleus, the striatum, the pallidum and the thalamus. Schizophrenia patients had lower activity levels than controls. In schizophrenia we found higher probability indices forming part of a bundle of interest (PIBI) in pathways connecting rACC, pre-SMA and SMA-proper as well as in pathways connecting M1 and pre-SMA with caudate nucleus, putamen, pallidum and thalamus and a reduced spatial extension of motor pathways in schizophrenia. There was a positive correlation between PIBI and activity level in the right pre-SMA-pallidum and the left M1-thalamus connection in healthy controls, and in the left pre-SMA-SMA-proper pathway in schizophrenia. Our results point to reduced volitional motor activity and altered motor pathway organisation in schizophrenia. The identified associations between the amount of movement and structural connectivity of motor pathways suggest dysfunction of cortico-basal ganglia pathways in the pathophysiology of hypokinesia in schizophrenia. Schizophrenia patients may use cortical pathways involving the supplementary motor area to compensate for basal ganglia dysfunction.

White matter microstructure alterations of the medial forebrain bundle in melancholic depression

BACKGROUND The medial forebrain bundle (MFB) is a key structure of the reward system and connects the ventral tegmental area (VTA) with the nucleus accumbens (NAcc), the medial and lateral orbitofrontal cortex (mOFC, lOFC) and the dorsolateral prefrontal cortex (dlPFC). Previous diffusion tensor imaging (DTI) studies in major depressive disorder point to white matter alterations of regions which may be incorporated in the MFB. Therefore, it was the aim of our study to probe white matter integrity of the MFB using a DTI-based probabilistic fibre tracking approach. METHODS 22 patients with major depressive disorder (MDD) (12 melancholic-MDD patients, 10 non-melancholic-MDD patients) and 21 healthy controls underwent DTI scans. We used a bilateral probabilistic fibre tracking approach to extract pathways between the VTA and NACC, mOFC, lOFC, dlPFC respectively. Mean fractional anisotropy (FA) values were used to compare structural connectivity between groups. RESULTS Mean-FA did not differ between healthy controls and all MDD patients. Compared to healthy controls melancholic MDD-patients had reduced mean-FA in right VTA-lOFC and VTA-dlPFC connections. Furthermore, melancholic-MDD patients had lower mean-FA than non-melancholic MDD-patients in the right VTA-lOFC connection. Mean-FA of these pathways correlated negatively with depression scale rating scores. LIMITATIONS Due to the small sample size and heterogeneous age group comparisons between melancholic and non-melancholic MDD-patients should be regarded as preliminary. CONCLUSIONS Our results suggest that the melancholic subtype of MDD is characterized by white matter microstructure alterations of the MFB. White matter microstructure is associated with both depression severity and anhedonia.

White matter matters: a multimodal approach to investigate white matter microstructure and metabolism

White matter connects different brain areas and applies electrical insulation to the neuron’s axons with myelin sheaths in order to enable quick signal transmission. Due to its modulatory properties in signal conduction, white matter plays an essential role in learning, cognition and psychiatric disorders (Fields, 2008a). In respect thereof, the non-invasive investigation of white matter anatomy and function in vivo provides the unique opportunity to explore the most complex organ of our body. Thus, the present thesis aimed to apply a multimodal neuroimaging approach to investigate different white matter properties in psychiatric and healthy populations. On the one hand, white matter microstructural properties were investigated in a psychiatric population; on the other hand, white matter metabolic properties were assessed in healthy adults providing basic information about the brain’s wiring entity. As a result, three research papers are presented here. The first paper assessed the microstructural properties of white matter in relation to a frequent epidemiologic finding in schizophrenia. As a result, reduced white matter integrity was observed in patients born in summer and autumn compared to patients born in winter and spring. Despite the large genetic basis of schizophrenia, accumulating evidence indicates that environmental exposures may be implicated in the development of schizophrenia (A. S. Brown, 2011). Notably, epidemiologic studies have shown a 5–8% excess of births during winter and spring for patients with schizophrenia on the Northern Hemisphere at higher latitudes (Torrey, Miller, Rawlings, & Yolken, 1997). Although the underlying mechanisms are unclear, the seasonal birth effect may indicate fluctuating environmental risk factors for schizophrenia. Thus, exposure to harmful factors during foetal development may result in the activation of pathologic neural circuits during adolescence or young adulthood, increasing the risk of schizophrenia (Fatemi & Folsom, 2009). While white matter development starts during the foetal period and continues until adulthood, its major development is accomplished by the age of two years (Brody, Kinney, Kloman, & Gilles, 1987; Huang et al., 2009). This indicates a vulnerability period of white matter that may coincide with the fluctuating environmental risk factors for schizophrenia. Since microstructural alterations of white matter in schizophrenia are frequently observed, the current study provided evidence for the neurodevelopmental hypothesis of schizophrenia. In the second research paper, the perfusion of white matter showed a positive correlation between white matter microstructure and its perfusion with blood across healthy adults. This finding was in line with clinical studies indicating a tight coupling between cerebral perfusion and WM health across subjects (Amann et al., 2012; Chen, Rosas, & Salat, 2013; Kitagawa et al., 2009). Although relatively little is known about the metabolic properties of white matter, different microstructural properties, such as axon diameter and myelination, might be coupled with the metabolic demand of white matter. Furthermore, the ability to detect perfusion signal in white matter was in accordance with a recent study showing that technical improvements, such as pseudo-continuous arterial spin labeling, enabled the reliable detection of white matter perfusion signal (van Osch et al., 2009). The third paper involved a collaboration within the same department to assess the interrelation between functional connectivity networks and their underlying structural connectivity.