Diffusion MRI tractography has emerged as a useful and popular tool for mapping connections between brain regions. In this study, we examined the performance of quantitative anisotropy (QA) in ...facilitating deterministic fiber tracking. Two phantom studies were conducted. The first phantom study examined the susceptibility of fractional anisotropy (FA), generalized factional anisotropy (GFA), and QA to various partial volume effects. The second phantom study examined the spatial resolution of the FA-aided, GFA-aided, and QA-aided tractographies. An in vivo study was conducted to track the arcuate fasciculus, and two neurosurgeons blind to the acquisition and analysis settings were invited to identify false tracks. The performance of QA in assisting fiber tracking was compared with FA, GFA, and anatomical information from T1-weighted images. Our first phantom study showed that QA is less sensitive to the partial volume effects of crossing fibers and free water, suggesting that it is a robust index. The second phantom study showed that the QA-aided tractography has better resolution than the FA-aided and GFA-aided tractography. Our in vivo study further showed that the QA-aided tractography outperforms the FA-aided, GFA-aided, and anatomy-aided tractographies. In the shell scheme (HARDI), the FA-aided, GFA-aided, and anatomy-aided tractographies have 30.7%, 32.6%, and 24.45% of the false tracks, respectively, while the QA-aided tractography has 16.2%. In the grid scheme (DSI), the FA-aided, GFA-aided, and anatomy-aided tractographies have 12.3%, 9.0%, and 10.93% of the false tracks, respectively, while the QA-aided tractography has 4.43%. The QA-aided deterministic fiber tracking may assist fiber tracking studies and facilitate the advancement of human connectomics.
Modification of spatial attention via reinforcement learning (Lee and Shomstein, 2013) requires the integration of reward, attention, and executive processes. Corticostriatal pathways are an ideal ...neural substrate for this integration because these projections exhibit a globally parallel (Alexander et al., 1986), but locally overlapping (Haber, 2003), topographical organization. Here we explore whether there are unique striatal regions that exhibit convergent anatomical connections from orbitofrontal cortex, dorsolateral prefrontal cortex, and posterior parietal cortex. Deterministic fiber tractography on diffusion spectrum imaging data from neurologically healthy adults (N = 60) was used to map frontostriatal and parietostriatal projections. In general, projections from cortex were organized according to both a medial-lateral and a rostral-caudal gradient along the striatal nuclei. Within rostral aspects of the striatum, we identified two bilateral convergence zones (one in the caudate nucleus and another in the putamen) that consisted of voxels with unique projections from orbitofrontal cortex, dorsolateral prefrontal cortex, and parietal regions. The distributed cortical connectivity of these striatal convergence zones was confirmed with follow-up functional connectivity analysis from resting state fMRI data, in which a high percentage of structurally connected voxels also showed significant functional connectivity. The specificity of this convergent architecture to these regions of the rostral striatum was validated against control analysis of connectivity within the motor putamen. These results delineate a neurologically plausible network of converging corticostriatal projections that may support the integration of reward, executive control, and spatial attention that occurs during spatial reinforcement learning.
Quantifying differences or similarities in connectomes has been a challenge due to the immense complexity of global brain networks. Here we introduce a noninvasive method that uses diffusion MRI to ...characterize whole-brain white matter architecture as a single local connectome fingerprint that allows for a direct comparison between structural connectomes. In four independently acquired data sets with repeated scans (total N = 213), we show that the local connectome fingerprint is highly specific to an individual, allowing for an accurate self-versus-others classification that achieved 100% accuracy across 17,398 identification tests. The estimated classification error was approximately one thousand times smaller than fingerprints derived from diffusivity-based measures or region-to-region connectivity patterns for repeat scans acquired within 3 months. The local connectome fingerprint also revealed neuroplasticity within an individual reflected as a decreasing trend in self-similarity across time, whereas this change was not observed in the diffusivity measures. Moreover, the local connectome fingerprint can be used as a phenotypic marker, revealing 12.51% similarity between monozygotic twins, 5.14% between dizygotic twins, and 4.51% between none-twin siblings, relative to differences between unrelated subjects. This novel approach opens a new door for probing the influence of pathological, genetic, social, or environmental factors on the unique configuration of the human connectome.
A network of multiple brain regions is recruited in face perception. Our understanding of the functional properties of this network can be facilitated by explicating the structural white matter ...connections that exist between its functional nodes. We accomplished this using functional MRI (fMRI) in combination with fiber tractography on high angular resolution diffusion weighted imaging data. We identified the three nodes of the core face network: the "occipital face area" (OFA), the "fusiform face area" (mid-fusiform gyrus or mFus), and the superior temporal sulcus (STS). Additionally, a region of the anterior temporal lobe (aIT), implicated as being important for face perception was identified. Our data suggest that we can further divide the OFA into multiple anatomically distinct clusters - a partitioning consistent with several recent neuroimaging results. More generally, structural white matter connectivity within this network revealed: 1) Connectivity between aIT and mFus, and between aIT and occipital regions, consistent with studies implicating this posterior to anterior pathway as critical to normal face processing; 2) Strong connectivity between mFus and each of the occipital face-selective regions, suggesting that these three areas may subserve different functional roles; 3) Almost no connectivity between STS and mFus, or between STS and the other face-selective regions. Overall, our findings suggest a re-evaluation of the "core" face network with respect to what functional areas are or are not included in this network.
Accurately making a decision in the face of incongruent options increases the efficiency of making similar congruency decisions in the future. Contextual factors like reward can modulate this ...adaptive process, suggesting that networks associated with monitoring previous success and failure outcomes might contribute to this form of behavioral updating. To evaluate this possibility, a group of healthy adults (n = 30) were tested with functional MRI (fMRI) while they performed a color-word Stroop task. In a conflict-related region of the medial orbitofrontal cortex (mOFC), stronger BOLD responses predicted faster response times (RTs) on the next trial. More importantly, the degree of behavioral adaptation of RTs was correlated with the magnitude of mOFC-RT associations on the previous trial, but only after accounting for network-level interactions with prefrontal and striatal regions. This suggests that congruency sequencing effects may rely on interactions between distributed corticostriatal circuits. This possibility was evaluated by measuring the convergence of white matter projections from frontal areas into the striatum with diffusion-weighted imaging. In these pathways, greater convergence of corticostriatal projections correlated with stronger functional mOFC-RT associations that, in turn, provided an indirect pathway linking anatomical structure to behavior. Thus distributed corticostriatal processing may mediate the orbitofrontal cortex's influence on behavioral updating, even in the absence of explicit rewards.
White matter structure declines with advancing age and has been associated with a decline in memory and executive processes in older adulthood. Yet, recent research suggests that higher physical ...activity and fitness levels may be associated with less white matter degeneration in late life, although the tract-specificity of this relationship is not well understood. In addition, these prior studies infrequently associate measures of white matter microstructure to cognitive outcomes, so the behavioral importance of higher levels of white matter microstructural organization with greater fitness levels remains a matter of speculation. Here we tested whether cardiorespiratory fitness (VO2max) levels were associated with white matter microstructure and whether this relationship constituted an indirect pathway between cardiorespiratory fitness and spatial working memory in two large, cognitively and neurologically healthy older adult samples. Diffusion tensor imaging was used to determine white matter microstructure in two separate groups: Experiment 1, N=113 (mean age=66.61) and Experiment 2, N=154 (mean age=65.66). Using a voxel-based regression approach, we found that higher VO2max was associated with higher fractional anisotropy (FA), a measure of white matter microstructure, in a diverse network of white matter tracts, including the anterior corona radiata, anterior internal capsule, fornix, cingulum, and corpus callosum (PFDR-corrected<.05). This effect was consistent across both samples even after controlling for age, gender, and education. Further, a statistical mediation analysis revealed that white matter microstructure within these regions, among others, constituted a significant indirect path between VO2max and spatial working memory performance. These results suggest that greater aerobic fitness levels are associated with higher levels of white matter microstructural organization, which may, in turn, preserve spatial memory performance in older adulthood.
•Fitness was associated with white matter (WM) microstructure in multiple WM tracts.•Regions of association included tracts that connect the medial temporal and prefrontal cortices.•The fitness–WM relationship was further associated with working memory performance.
Abstract Resting heart rate may confer risk for cardiovascular disease (CVD) and other adverse cardiovascular events. While the brainstem's autonomic control over heart rate is well established, less ...is known about the regulatory role of higher level cortical and subcortical brain regions, especially in humans. This study sought to characterize the brain networks that predict variation in prevailing heart rate in otherwise healthy adults. We used machine learning approaches designed for complex, high‐dimensional data sets, to predict variation in instantaneous heart period (the inter‐heartbeat‐interval) from whole‐brain hemodynamic signals measured by fMRI. Task‐based and resting‐state fMRI, as well as peripheral physiological recordings, were taken from two data sets that included extensive repeated measurements within individuals. Our models reliably predicted instantaneous heart period from whole‐brain fMRI data both within and across individuals, with prediction accuracies being highest when measured within‐participants. We found that a network of cortical and subcortical brain regions, many linked to visceral motor and visceral sensory processes, were reliable predictors of variation in heart period. This adds to evidence on brain–heart interactions and constitutes an incremental step toward developing clinically applicable biomarkers of brain contributions to CVD risk.
Using whole‐brain fMRI data, we reliably predicted instantaneous heart period within and across individuals from the activity of a network of cortical and subcortical brain regions, many linked to visceral motor and visceral sensory processes. This adds to existing evidence on brain–heart interactions and constitutes a step toward developing clinically applicable biomarkers of brain contributions to CVD risk.
Functional magnetic resonance imaging (fMRI) evidence indicates that different subregions of ventrolateral prefrontal cortex (VLPFC) participate in distinct cortical networks. These networks have ...been shown to support separable cognitive functions: anterior VLPFC inferior frontal gyrus (IFG) pars orbitalis functionally correlates with a ventral fronto-temporal network associated with top-down influences on memory retrieval, while mid-VLPFC (IFG pars triangularis) functionally correlates with a dorsal fronto-parietal network associated with postretrieval control processes. However, it is not known to what extent subregional differences in network affiliation and function are driven by differences in the organization of underlying white matter pathways. We used high-angular-resolution diffusion spectrum imaging and functional connectivity analysis in unanesthetized humans to address whether the organization of white matter connectivity differs between subregions of VLPFC. Our results demonstrate a ventral-dorsal division within IFG. Ventral IFG as a whole connects broadly to lateral temporal cortex. Although several different individual white matter tracts form connections between ventral IFG and lateral temporal cortex, functional connectivity analysis of fMRI data indicates that these are part of the same ventral functional network. By contrast, across subdivisions, dorsal IFG was connected with the midfrontal gyrus and correlated as a separate dorsal functional network. These qualitative differences in white matter organization within larger macroanatomical subregions of VLPFC support prior functional distinctions among these regions observed in task-based and functional connectivity fMRI studies. These results are consistent with the proposal that anatomical connectivity is a crucial determinant of systems-level functional organization of frontal cortex and the brain in general.
The axons that project into the striatum are known to segregate according to macroscopic cortical systems; however, the within-region organization of these fibers has yet to be described in humans. ...We used in vivo fiber tractography, in neurologically healthy adults, to map white matter bundles that originate in different neocortical areas, navigate complex fiber crossings, and project into the striatum. As expected, these fibers were generally segregated according to cortical origin. Within a subset of pathways, a patched pattern of inputs was observed, consistent with previous ex vivo histological studies. In projections from the prefrontal cortex, we detected a topography in which fibers from rostral prefrontal areas projected mostly to rostral parts of the striatum and vice versa for inputs originating in caudal cortical areas. Importantly, within this prefrontal system there was also an asymmetry in the subset of divergent projections, with more fibers projecting in a posterior direction than anterior. This asymmetry of information projecting into the basal ganglia was predicted by previous network-level computational models. A rostral-caudal topography was also present at the local level in otherwise somatotopically organized fibers projecting from the motor cortex. This provides clear evidence that the longitudinal organization of input fields, observed at the macroscopic level across cortical systems, is also found at the microstructural scale at which information is segregated as it enters the human basal ganglia.
Socioeconomic disadvantage confers risk for aspects of ill health that may be mediated by systemic inflammatory influences on the integrity of distributed brain networks. Following this hypothesis, ...we tested whether socioeconomic disadvantage related to the structural integrity of white matter tracts connecting brain regions of distributed networks, and whether such a relationship would be mediated by anthropometric, behavioral, and molecular risk factors associated with systemic inflammation. Otherwise healthy adults (N= 155, aged 30-50 years, 78 men) completed protocols assessing multilevel indicators of socioeconomic position (SEP), anthropometric and behavioral measures of adiposity and cigarette smoking, circulating levels of C-reactive protein (CRP), and white matter integrity by diffusion tensor imaging. Mediation modeling was used to test associations between SEP indicators and measures of white matter tract integrity, as well as indirect mediating paths. Measures of tract integrity followed a socioeconomic gradient: individuals completing more schooling, earning higher incomes, and residing in advantaged neighborhoods exhibited increases in white matter fractional anisotropy and decreases in radial diffusivity, relative to disadvantaged individuals. Moreover, analysis of indirect paths showed that adiposity, cigarette smoking, and CRP partially mediated these effects. Socioeconomic inequalities may relate to diverse health disparities via inflammatory pathways impacting the structural integrity of brain networks.
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