Structural Connectivity Mapping Research Articles

The structural connectivity mapping of the intralaminar thalamic nuclei

The intralaminar nuclei of the thalamus play a pivotal role in awareness, conscious experience, arousal, sleep, vigilance, as well as in cognitive, sensory, and sexual processing. Nonetheless, in humans, little is known about the direct involvement of these nuclei in such multifaceted functions and their structural connections in the brain. Thus, examining the versatility of structural connectivity of the intralaminar nuclei with the rest of the brain seems reasonable. Herein, we attempt to show the direct structural connectivity of the intralaminar nuclei to diencephalic, mesencephalic, and cortical areas using probabilistic tracking of the diffusion data from the human connectome project. The intralaminar nuclei fiber distributions span a wide range of subcortical and cortical areas. Moreover, the central medial and parafascicular nucleus reveal similar connectivity to the temporal, visual, and frontal cortices with only slight variability. The central lateral nucleus displays a refined projection to the superior colliculus and fornix. The centromedian nucleus seems to be an essential component of the subcortical somatosensory system, as it mainly displays connectivity via the medial and superior cerebellar peduncle to the brainstem and the cerebellar lobules. The subparafascicular nucleus projects to the somatosensory processing areas. It is interesting to note that all intralaminar nuclei have connections to the brainstem. In brief, the structural connectivity of the intralaminar nuclei aligns with the structural core of various functional demands for arousal, emotion, cognition, sensory, vision, and motor processing. This study sheds light on our understanding of the structural connectivity of the intralaminar nuclei with cortical and subcortical structures, which is of great interest to a broader audience in clinical and neuroscience research.

Imaging crossing fibers in mouse, pig, monkey, and human brain using small-angle X-ray scattering.

Myelinated axons (nerve fibers) efficiently transmit signals throughout the brain via action potentials. Multiple methods that are sensitive to axon orientations, from microscopy to magnetic resonance imaging, aim to reconstruct the brain's structural connectome. As billions of nerve fibers traverse the brain with various possible geometries at each point, resolving fiber crossings is necessary to generate accurate structural connectivity maps. However, doing so with specificity is a challenging task because signals originating from oriented fibers can be influenced by brain (micro)structures unrelated to myelinated axons. X-ray scattering can specifically probe myelinated axons due to the periodicity of the myelin sheath, which yields distinct peaks in the scattering pattern. Here, we show that small-angle X-ray scattering (SAXS) can be used to detect myelinated, axon-specific fiber crossings. We first demonstrate the capability using strips of human corpus callosum to create artificial double- and triple-crossing fiber geometries, and we then apply the method in mouse, pig, vervet monkey, and human brains. We compare results to polarized light imaging (3D-PLI), tracer experiments, and to outputs from diffusion MRI that sometimes fails to detect crossings. Given its specificity, capability of 3-dimensional sampling and high resolution, SAXS could serve as a ground truth for validating fiber orientations derived using diffusion MRI as well as microscopy-based methods. STATEMENT OF SIGNIFICANCE: To study how the nerve fibers in our brain are interconnected, scientists need to visualize their trajectories, which often cross one another. Here, we show the unique capacity of small-angle X-ray scattering (SAXS) to study these fiber crossings without use of labeling, taking advantage of SAXS's specificity to myelin - the insulating sheath that is wrapped around nerve fibers. We use SAXS to detect double and triple crossing fibers and unveil intricate crossings in mouse, pig, vervet monkey, and human brains. This non-destructive method can uncover complex fiber trajectories and validate other less specific imaging methods (e.g., MRI or microscopy), towards accurate mapping of neuronal connectivity in the animal and human brain.

High-fidelity, high-spatial-resolution diffusion magnetic resonance imaging of ex vivo whole human brain at ultra-high gradient strength with structured low-rank echo-planar imaging ghost correction.

Diffusion magnetic resonance imaging (dMRI) of whole ex vivo human brain specimens enables three-dimensional (3D) mapping of structural connectivity at the mesoscopic scale, providing detailed evaluation of fiber architecture and tissue microstructure at a spatial resolution that is difficult to access in vivo. To account for the short T2 and low diffusivity of fixed tissue, ex vivo dMRI is often acquired using strong diffusion-sensitizing gradients and multishot/segmented 3D echo-planar imaging (EPI) sequences to achieve high spatial resolution. However, the combination of strong diffusion-sensitizing gradients and multishot/segmented EPI readout can result in pronounced ghosting artifacts incurred by nonlinear spatiotemporal variations in the magnetic field produced by eddy currents. Such ghosting artifacts cannot be corrected with conventional correction solutions and pose a significant roadblock to leveraging human MRI scanners with ultrahigh gradients for ex vivo whole-brain dMRI. Here, we show that ghosting-correction approaches that correct for either polarity-related ghosting or shot-to-shot variations in a separate manner are suboptimal for 3D multishot diffusion-weighted EPI experiments in fixed human brain specimens using strong diffusion-sensitizing gradients on the 3-T Connectom MRI scanner, resulting in orientationally biased dMRI estimates. We apply a recently developed advanced k-space reconstruction method based on structured low-rank matrix (SLM) modeling that handles both polarity-related ghosting and shot-to-shot variation simultaneously, to mitigate artifacts in high-angular resolution multishot dMRI data acquired in several fixed human brain specimens at 0.7-0.8-mm isotropic spatial resolution using b-values up to 10,000 s/mm2 and gradient strengths up to 280 mT/m. We demonstrate the improved mapping of diffusion tensor imaging and fiber orientation distribution functions in key neuroanatomical areas distributed across the whole brain using SLM-based EPI ghost correction compared with alternative techniques.

Deep brain stimulation for extreme behaviors associated with autism spectrum disorder converges on a common pathway: a systematic review and connectomic analysis.

Individuals with autism spectrum disorder (ASD) may display extreme behaviors such as self-injury or aggression that often become refractory to psychopharmacology or behavioral intervention. Deep brain stimulation (DBS) is a surgical alternative that modulates brain circuits that have yet to be clearly elucidated. In the current study the authors performed a connectomic analysis to identify brain circuitry engaged by DBS for extreme behaviors associated with ASD. A systematic review was performed to identify prior reports of DBS as a treatment for extreme behaviors in patients with ASD. Individual patients' perioperative imaging was collected from corresponding authors. DBS electrode localization and volume of tissue activated modeling were performed. Volumes of tissue activated were used as seed points in high-resolution normative functional and structural imaging templates. The resulting individual functional and structural connectivity maps were pooled to identify networks and pathways that are commonly engaged by all targets. Nine patients with ASD who were receiving DBS for symptoms of aggression or self-injurious behavior were identified. All patients had some clinical improvement with DBS. Connectomic analysis of 8 patients (from the systematic review and unpublished clinical data) demonstrated a common anatomical area of shared circuitry within the anterior limb of the internal capsule. Functional analysis of 4 patients identified a common network of distant brain areas including the amygdala, insula, and anterior cingulate engaged by DBS. This study presents a comprehensive synopsis of the evidence for DBS in the treatment of extreme behaviors associated with ASD. Using network mapping, the authors identified key circuitry common to DBS targets.

201 Tractography Analysis of Deep Brain Stimulation for Obsessive-Compulsive Disorder

INTRODUCTION: Deep brain stimulation of the anterior limb of the internal capsule (ALIC-DBS) is an effective treatment for refractory obsessive-compulsive disorder (OCD). Animal tracing studies and human tractography suggest that the putative ALIC target for OCD impacts brain circuits for reward, affect and inhibitory control, including projections to orbitofrontal (OFC) and ventromedial (vmPFC) cortex, ventrolateral cortex (vlPFC), dorsal anterior cingulate cortex (dACC), dorsal medial cortex (dmPFC) and dorsolateral prefrontal cortex (dlPFC). The recent development of directional segmented DBS leads may allow for selective stimulation of these ALIC-projections. METHODS: We generated structural connectivity maps of therapeutic stimulation using probabilistic tractography in OCD patients treated with ALIC-DBS. Response was defined as = 35% improvement in YBOCS after routine clinical parameter optimization. We also generated the volume of tissue activation (VTA) based on the stimulation parameters at the last follow-up. We estimated probability of connections between the VTA and each projection to the prefrontal regions (vmPFC/OFC, vlPFC, dlPFC, dmPFC, and dACC) in responders and non-responders. To further explore how connectivity would explain clinical improvement, we correlated connectivity values from stimulation sites to cortical regions to the percentage of change in YBOCS using Spearman correlation. Finally, we developed directional tractography activation models and explored personalized circuit-specific targeting and stimulation for patients with directional DBS-leads. RESULTS: All patients with ALIC DBS displayed stimulation of OFC/vmPFC and dmPFC connections. However, non-responders had incomplete stimulation of vlPFC, dACC and dlPFC compared to responders. Correlation analysis revealed that fibers from the dorsolateral ALIC to vlPFC were positively associated with improvement in Y-BOCS scores, while connections from ventral ALIC to vmPFC were negatively associated. We were able to successfully construct a stimulation tractography model of directional segmented lead configurations along the medial-lateral axis, which we used for personalized DBS targeting and programming in our most recent patients. Early observations suggest that this directional precision-strategy allows for fast improvement in patient-specific symptom domains for reward, affect and inhibitory control. CONCLUSION: Our findings suggest that the therapeutic benefit of ALIC DBS for OCD is associated with stimulation of a subset of specific ALIC WM pathways, which can be selectively targeted using directional DBS-systems.

Detection of Juvenile Myoclonic Epilepsy Based on Deep Transferred CNN and Structural Connectivity Maps

Acoustic emission offers various data concerning the fracture behavior of different materials. In this paper AE study was conducted on 14 numbers of tensile specimens made of Al6061 reinforced with silicon carbide particles in which tensile loading is done up to failure with a 100 kN universal testing machine. AE parameters are filtered from the specimens and solely 60 % of actual failure loading parameters are considered for further analysis. Parameters like count, energy, duration, rise time and amplitude are used to characterize the fractures occurred on metal matrix composites due to the low matrix cavitations, particle cracking, interfacial debonding and the transition of mode from tensile to shear. The three individual artificial neural networks generated with the AE parameters like hits, felicity ratio and rise angle were trained properly towards the ultimate strength as the anticipated output. The network was able to predict the worst case error of – 3.51 %,-4.73 %, and -2.73 %. The failure prediction accuracy by using rise angle as input found to be better, however the three trained artificial neural networks have also proven its significance towards the prediction exercise.

Structural connectivity of the human massa intermedia: A probabilistic tractography study.

The role of massa intermedia (MI) is poorly understood in humans. Recent studies suggest its presence may play a role in normal human neurocognitive function while prior studies have shown the absence of MI correlated with psychiatric disorders. There is growing evidence that MI is likely a midline white matter conduit, responsible for interhemispheric connectivity, similar to other midline commissures. MI presence was identified in an unrelated sample using the Human Connectome Project database. MI structural connectivity maps were created and gray matter target regions were identified using probabilistic tractography of the whole brain. Probabilistic tractography revealed an extensive network of connections between MI and limbic, frontal and temporal lobes as well as insula and pericalcarine cortices. Women compared to men had stronger connectivity via their MI. The presented results support the role of MI as a midline commissure with strong connectivity to the amygdala, hippocampus, and entorhinal cortex.

Non-negative data-driven mapping of structural connections with application to the neonatal brain

Mapping connections in the neonatal brain can provide insight into the crucial early stages of neurodevelopment that shape brain organisation and lay the foundations for cognition and behaviour. Diffusion MRI and tractography provide unique opportunities for such explorations, through estimation of white matter bundles and brain connectivity. Atlas-based tractography protocols, i.e. a priori defined sets of masks and logical operations in a template space, have been commonly used in the adult brain to drive such explorations. However, rapid growth and maturation of the brain during early development make it challenging to ensure correspondence and validity of such atlas-based tractography approaches in the developing brain. An alternative can be provided by data-driven methods, which do not depend on predefined regions of interest. Here, we develop a novel data-driven framework to extract white matter bundles and their associated grey matter networks from neonatal tractography data, based on non-negative matrix factorisation that is inherently suited to the non-negative nature of structural connectivity data. We also develop a non-negative dual regression framework to map group-level components to individual subjects. Using in-silico simulations, we evaluate the accuracy of our approach in extracting connectivity components and compare with an alternative data-driven method, independent component analysis. We apply non-negative matrix factorisation to whole-brain connectivity obtained from publicly available datasets from the Developing Human Connectome Project, yielding grey matter components and their corresponding white matter bundles. We assess the validity and interpretability of these components against traditional tractography results and grey matter networks obtained from resting-state fMRI in the same subjects. We subsequently use them to generate a parcellation of the neonatal cortex using data from 323 new-born babies and we assess the robustness and reproducibility of this connectivity-driven parcellation.

Mapping the human subcortical auditory system using histology, postmortem MRI and in vivo MRI at 7T.

Studying the human subcortical auditory system non-invasively is challenging due to its small, densely packed structures deep within the brain. Additionally, the elaborate three-dimensional (3-D) structure of the system can be difficult to understand based on currently available 2-D schematics and animal models. Wfe addressed these issues using a combination of histological data, post mortem magnetic resonance imaging (MRI), and in vivo MRI at 7 Tesla. We created anatomical atlases based on state-of-the-art human histology (BigBrain) and postmortem MRI (50 µm). We measured functional MRI (fMRI) responses to natural sounds and demonstrate that the functional localization of subcortical structures is reliable within individual participants who were scanned in two different experiments. Further, a group functional atlas derived from the functional data locates these structures with a median distance below 2 mm. Using diffusion MRI tractography, we revealed structural connectivity maps of the human subcortical auditory pathway both in vivo (1050 µm isotropic resolution) and post mortem (200 µm isotropic resolution). This work captures current MRI capabilities for investigating the human subcortical auditory system, describes challenges that remain, and contributes novel, openly available data, atlases, and tools for researching the human auditory system.

Divergent network properties that predict early surgical failure versus late recurrence in temporal lobe epilepsy.

The objectives of this study were to identify functional and structural network properties that are associated with early versus long-term seizure outcomes after mesial temporal lobe epilepsy (mTLE) surgery and to determine how these compare to current clinically used methods for seizure outcome prediction. In this case-control study, 26 presurgical mTLE patients and 44 healthy controls were enrolled to undergo 3-T MRI for functional and structural connectivity mapping across an 8-region network of mTLE seizure propagation, including the hippocampus (left and right), insula (left and right), thalamus (left and right), one midline precuneus, and one midline mid-cingulate. Seizure outcome was assessed annually for up to 3 years. Network properties and current outcome prediction methods related to early and long-term seizure outcome were investigated. A network model was previously identified across 8 patients with seizure-free mTLE. Results confirmed that whole-network propagation connectivity patterns inconsistent with the mTLE model predict early surgical failure. In those patients with networks consistent with the mTLE network, specific bilateral within-network hippocampal to precuneus impairment (rather than unilateral impairment ipsilateral to the seizure focus) was associated with mild seizure recurrence. No currently used clinical variables offered the same ability to predict long-term outcome. It is known that there are important clinical differences between early surgical failure that lead to frequent disabling seizures and late recurrence of less frequent mild seizures. This study demonstrated that divergent network connectivity variability, whole-network versus within-network properties, were uniquely associated with these disparate outcomes.

Function-specific and Enhanced Brain Structural Connectivity Mapping via Joint Modeling of Diffusion and Functional MRI

A joint structural-functional brain network model is presented, which enables the discovery of function-specific brain circuits, and recovers structural connections that are under-estimated by diffusion MRI (dMRI). Incorporating information from functional MRI (fMRI) into diffusion MRI to estimate brain circuits is a challenging task. Usually, seed regions for tractography are selected from fMRI activation maps to extract the white matter pathways of interest. The proposed method jointly analyzes whole brain dMRI and fMRI data, allowing the estimation of complete function-specific structural networks instead of interactively investigating the connectivity of individual cortical/sub-cortical areas. Additionally, tractography techniques are prone to limitations, which can result in erroneous pathways. The proposed framework explicitly models the interactions between structural and functional connectivity measures thereby improving anatomical circuit estimation. Results on Human Connectome Project (HCP) data demonstrate the benefits of the approach by successfully identifying function-specific anatomical circuits, such as the language and resting-state networks. In contrast to correlation-based or independent component analysis (ICA) functional connectivity mapping, detailed anatomical connectivity patterns are revealed for each functional module. Results on a phantom (Fibercup) also indicate improvements in structural connectivity mapping by rejecting false-positive connections with insufficient support from fMRI, and enhancing under-estimated connectivity with strong functional correlation.

Revisiting the Functional and Structural Connectivity of Large-Scale Cortical Networks.

Multimodal neuroimaging research has become increasingly popular, and structure-function correspondence is tacitly assumed. Researchers have not yet adequately assessed whether the functional connectivity (FC) and structural connectivity (SC) of large-scale cortical networks are in agreement. Structural magnetic resonance imaging (sMRI), resting-state functional MRI (rfMRI), and diffusion-weighted imaging (DWI) data sets from 36 healthy subjects (age 27.4) were selected from a Rockland sample (Enhanced Nathan Kline Institute). The cerebral cortex was parcellated into 62 regions according to the Desikan-Killiany atlas for FC and SC analyses. Thresholded correlations in rfMRI and tractography derived from DWI were used to construct FC and SC maps, respectively. A community detection algorithm was applied to reveal the underlying organization, and modular consistency was quantified to bridge cross-modal comparisons. The distributions of correlation coefficients in FC and SC maps were significantly different. Approximately one-fourth of the connections in the SC map were located at a correlation level below 0.2 (df 253). The index of modular consistency in the within-modality interindividual condition (either FC or SC) was considerably greater than that in the between-modality intraindividual analog. In addition, the SC-FC differential map (SC connections with lower correlations) revealed reliable modular structures. Based on these results, the hypothesized FC-SC agreement is partially valid. Contingent on extant neuroimaging tools and analytical conventions, the neural informatics of FC and SC should be regarded as complementary rather than concordant. Furthermore, the results verify the physiological significance of moderately (or mildly) correlated brain signals in rfMRI, which are often discarded by stringent thresholding.

S192 The role of the cerebellum in motion control

Objectives To assess the roles of the cerebellum in motion control on the basis of clinical, anatomical, functional imaging and electrophysiological investigations. Methods (a) Review of experimental findings starting from works of pioneers of the 19th century until 2017, (b) assessment of the theory of internal models. Results Anatomically, the cerebellum has a modular organization. Parasagittal bands of Purkinje cells (PCs) project to specific areas of cerebellar nuclei (CN). Functionally, cerebellar cortex (CC) is composed of microzones gathering groups of about 1000 PCs having the same somatotopic receptive field. The intrinsic connectivity networks (ICNs) derived from fMRI studies overlap with maps of structural connectivity. Transsynaptic tracer studies reveal disynaptic pathways linking the cerebellum and basal ganglia. Neurophysiologically, a very robust property of CN is their ability to fire rebound spike bursts following strong hyperpolarization, turning inhibition in CN output spiking. The timing of spiking is critical for the CC and is a pre-requisite for internal models. Anticipation of action is mandatory to plan the sequential movements on the basis of internal/external constraints. Cerebellar dysmetria can now be explained by biased internal models of limb dynamics. Similarly, in Schmahmann’s syndrome a mismatch between reality and perceived reality is suspected. Conclusions The leading theory of forward models is now embracing not only the motor symptoms but also the cognitive deficits observed in cerebellar ataxias. Significance The redundant architecture of the CC makes of the cerebellum an ideal structure to convey signals across microzones in a time-dependent fashion.

White matter maturation in the neonatal brain is predictive of school age cognitive capacities in children born very preterm

To investigate the association between white matter organization in the neonatal brain and cognitive capacities at early school age in children born very preterm. Thirty children born very preterm (gestational age median 27.5wks, interquartile range [IQR] 25.5-29.5; 18 males, 12 females) were included in this retrospective observational cohort study. Diffusion-weighted imaging (DWI) had been performed on a 3T system in the neonatal period (median 41.3 [IQR 40.0-42.6]wks) and cognitive functioning was formally assessed at age 5 years and 7 months (IQR 5.4-5.9y) using the Wechsler Preschool and Primary Scale of Intelligence. Structural connectivity maps were reconstructed from the DWI data using deterministic streamline tractography. Network metrics of global and local communication and mean fractional anisotropy of white matter pathways were related to IQ and processing speed at age 5 years using linear regression analyses. Mean fractional anisotropy was significantly related to Performance IQ at age 5 years (F=8.48, p=0.007). Findings persisted after adjustment for maternal education level. Our findings provide evidence that the blueprint of later cognitive achievement is already present at term-equivalent age and suggest that white matter connectivity strength may be a valuable predictor for long-term cognitive functioning.

White Matter Abnormalities and Structural Hippocampal Disconnections in Amnestic Mild Cognitive Impairment and Alzheimer’s Disease

The purpose of this project was to evaluate white matter degeneration and its impact on hippocampal structural connectivity in patients with amnestic mild cognitive impairment, non-amnestic mild cognitive impairment and Alzheimer’s disease. We estimated white matter fractional anisotropy, mean diffusivity and hippocampal structural connectivity in two independent cohorts. The ADNI cohort included 108 subjects [25 cognitively normal, 21 amnestic mild cognitive impairment, 47 non-amnestic mild cognitive impairment and 15 Alzheimer’s disease]. A second cohort included 34 subjects [15 cognitively normal and 19 amnestic mild cognitive impairment] recruited in Montreal. All subjects underwent clinical and neuropsychological assessment in addition to diffusion and T1 MRI. Individual fractional anisotropy and mean diffusivity maps were generated using FSL-DTIfit. In addition, hippocampal structural connectivity maps expressing the probability of connectivity between the hippocampus and cortex were generated using a pipeline based on FSL-probtrackX. Voxel-based group comparison statistics of fractional anisotropy, mean diffusivity and hippocampal structural connectivity were estimated using Tract-Based Spatial Statistics. The proportion of abnormal to total white matter volume was estimated using the total volume of the white matter skeleton. We found that in both cohorts, amnestic mild cognitive impairment patients had 27-29% white matter volume showing higher mean diffusivity but no significant fractional anisotropy abnormalities. No fractional anisotropy or mean diffusivity differences were observed between non-amnestic mild cognitive impairment patients and cognitively normal subjects. Alzheimer’s disease patients had 66.3% of normalized white matter volume with increased mean diffusivity and 54.3% of the white matter had reduced fractional anisotropy. Reduced structural connectivity was found in the hippocampal connections to temporal, inferior parietal, posterior cingulate and frontal regions only in the Alzheimer’s group. The severity of white matter degeneration appears to be higher in advanced clinical stages, supporting the construct that these abnormalities are part of the pathophysiological processes of Alzheimer’s disease.

Whole-Brain Mapping of Structural Connectivity in Infants Reveals Altered Connection Strength Associated with Growth and Preterm Birth

Cerebral white-matter injury is common in preterm-born infants and is associated with neurocognitive impairments. Identifying the pattern of connectivity changes in the brain following premature birth may provide a more comprehensive understanding of the neurobiology underlying these impairments. Here, we characterize whole-brain, macrostructural connectivity following preterm delivery and explore the influence of age and prematurity using a data-driven, nonsubjective analysis of diffusion magnetic resonance imaging data. T1- and T2-weighted and -diffusion MRI were obtained between 11 and 31 months postconceptional age in 49 infants, born between 25 and 35 weeks postconception. An optimized processing pipeline, combining anatomical, and tissue segmentations with probabilistic diffusion tractography, was used to map mean tract anisotropy. White-matter tracts where connection strength was related to age of delivery or imaging were identified using sparse-penalized regression and stability selection. Older children had stronger connections in tracts predominantly involving frontal lobe structures. Increasing prematurity at birth was related to widespread reductions in connection strength in tracts involving all cortical lobes and several subcortical structures. This nonsubjective approach to mapping whole-brain connectivity detected hypothesized changes in the strength of intracerebral connections during development and widespread reductions in connectivity strength associated with premature birth.

Rebuttal from David Gozal

The arguments set forth by Rosenzweig and colleagues (2012) are extremely valuable since they point out (i) the complexity of the effects on CNS as a result of the sleep and gas exchange perturbations accompanying sleep apnoea disease, and (ii) the possibility that the conventional imaging technologies used in the studies published to date may not have specifically investigated unique structural properties of the CNS. Indeed, novel functional and structural connectivity maps have revealed complex interactions between a priori undetectable or very subtle anatomical deficits that impinge upon the functional integrity of the neural systems (Alexander et al. 2011; Fornito et al. 2012; Segall et al. 2012). Therefore to deny that there is CNS injury, simply because some of the symptoms can be putatively attributed to a phenotypic constellation that is not fully associated with specific lesions of the CNS (as defined in the context of conventional brain imaging), is tantamount to surmising that sleep apnoea elicits functional perturbations in a neural network without affecting any of its integral anatomical components. To move the field forward, I would argue for the use of integrative approaches that combine more advanced connectivity-based imaging technologies, and couple them to pertinent functional reporters that integrate pathway analyses with exquisitely sensitive cellular and molecular stimulus-triggered responses (Saygin et al. 2011). The conceptual framework of the cerebellum (Rosenzweig et al. 2012) as one of the putative targets and central sites of vulnerability is, however, important. Indeed, outputs from the cerebellum have been identified in non-motor areas in the prefrontal and posterior parietal cortex, and the spectrum of tasks associated with cerebellar activation encompasses tasks designed to assess attention, executive control, language, working memory, learning and emotion (Strick et al. 2009). All of these functions are potentially affected by sleep apnoea. In fact, evidence for the cerebellum as a neural site that is uniquely vulnerable to intermittent hypoxia has been previously identified (Pae et al. 2005; Ramanathan et al. 2005). Similarly, the analogy of CNS perturbations with those of thalamo-cortical dysrhythmia may not fully capture the complexity of neuronal cellular losses imposed by the presence of sleep apnoea, along with the compensatory cellular processes recruited over time to preserve higher level functioning (Goghari et al. 2010; Schulman et al. 2011). Thus, the cumulative animal and human evidence supports the underlying concepts of initial cellular losses in the context of sleep apnoea as the major underlying driver for potential alterations in higher level brain functioning.

Insights into the Connectivity of the Human Brain Using DTI

Diffusion tensor imaging (DTI) is a neuroimaging MR technique, which allows in vivo and non-destructive visualization of myeloarchitectonics in the neural tissue and provides quantitative estimates of WM integrity by measuring molecular diffusion. It is based on the phenomenon of diffusion anisotropy in the nerve tissue, in that water molecules diffuse faster along the neural fibre direction and slower in the fibre-transverse direction. On the basis of their topographic location, trajectory, and areas that interconnect the various fibre systems of the mammalian brain are divided into commissural, projectional and association fibre systems. DTI has opened an entirely new window on the white matter anatomy with both clinical and scientific applications. Its utility is found in both the localization and the quantitative assessment of specific neuronal pathways. The potential of this technique to address connectivity in the human brain is not without a few methodological limitations. A wide spectrum of diffusion imaging paradigms and computational tractography algorithms has been explored in recent years, which established DTI as promising new avenue, for the non-invasive in vivo mapping of structural connectivity at the macroscale level. Further improvements in the spatial resolution of DTI may allow this technique to be applied in the near future for mapping connectivity also at the mesoscale level. DOI: http://dx.doi.org/10.3126/njr.v1i1.6330 Nepalese Journal of Radiology Vol.1(1): 78-91

MR connectomics: Principles and challenges

MR connectomics is an emerging framework in neuro-science that combines diffusion MRI and whole brain tractography methodologies with the analytical tools of network science. In the present work we review the current methods enabling structural connectivity mapping with MRI and show how such data can be used to infer new information of both brain structure and function. We also list the technical challenges that should be addressed in the future to achieve high-resolution maps of structural connectivity. From the resulting tremendous amount of data that is going to be accumulated soon, we discuss what new challenges must be tackled in terms of methods for advanced network analysis and visualization, as well data organization and distribution. This new framework is well suited to investigate key questions on brain complexity and we try to foresee what fields will most benefit from these approaches.

Noninvasive Functional and Structural Connectivity Mapping of the Human Thalamocortical System

Relating structural connectivity with functional activity is fundamentally important to understanding the brain's physiology. The thalamocortical system serves as a good model system for exploring structure/function relationships because of its well-documented anatomical connectivity. Here we performed functional and structural magnetic resonance mapping of the human thalamocortical system using intrinsic brain activity and diffusion-weighted imaging. The accuracy of these imaging techniques is tested by comparison with human histology registered to common anatomical space and connectional anatomy derived from nonhuman primates. In general, there is good overall concordance among structural, functional, and histological results which suggests that a simple model of direct anatomical connectivity between the cerebral cortex and the thalamus is capable of explaining much of the observed correlations in neuronal activity. However, important differences between structural and functional mapping results are also manifest which suggests a more complex interpretation and emphasizes the unique contributions from structural and functional mapping.