Timothy D. Verstynen

Deterministic Diffusion Fiber Tracking Improved by Quantitative Anisotropy

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.

High-definition fiber tractography of the human brain: neuroanatomical validation and neurosurgical applications.

BACKGROUND High-definition fiber tracking (HDFT) is a novel combination of processing, reconstruction, and tractography methods that can track white matter fibers from cortex, through complex fiber crossings, to cortical and subcortical targets with subvoxel resolution. OBJECTIVE To perform neuroanatomical validation of HDFT and to investigate its neurosurgical applications. METHODS Six neurologically healthy adults and 36 patients with brain lesions were studied. Diffusion spectrum imaging data were reconstructed with a Generalized Q-Ball Imaging approach. Fiber dissection studies were performed in 20 human brains, and selected dissection results were compared with tractography. RESULTS HDFT provides accurate replication of known neuroanatomical features such as the gyral and sulcal folding patterns, the characteristic shape of the claustrum, the segmentation of the thalamic nuclei, the decussation of the superior cerebellar peduncle, the multiple fiber crossing at the centrum semiovale, the complex angulation of the optic radiations, the terminal arborization of the arcuate tract, and the cortical segmentation of the dorsal Broca area. From a clinical perspective, we show that HDFT provides accurate structural connectivity studies in patients with intracerebral lesions, allowing qualitative and quantitative white matter damage assessment, aiding in understanding lesional patterns of white matter structural injury, and facilitating innovative neurosurgical applications. High-grade gliomas produce significant disruption of fibers, and low-grade gliomas cause fiber displacement. Cavernomas cause both displacement and disruption of fibers. CONCLUSION Our HDFT approach provides an accurate reconstruction of white matter fiber tracts with unprecedented detail in both the normal and pathological human brain. Further studies to validate the clinical findings are needed.

How Each Movement Changes the Next: An Experimental and Theoretical Study of Fast Adaptive Priors in Reaching

Most voluntary actions rely on neural circuits that map sensory cues onto appropriate motor responses. One might expect that for everyday movements, like reaching, this mapping would remain stable over time, at least in the absence of error feedback. Here we describe a simple and novel psychophysical phenomenon in which recent experience shapes the statistical properties of reaching, independent of any movement errors. Specifically, when recent movements are made to targets near a particular location subsequent movements to that location become less variable, but at the cost of increased bias for reaches to other targets. This process exhibits the variance–bias tradeoff that is a hallmark of Bayesian estimation. We provide evidence that this process reflects a fast, trial-by-trial learning of the prior distribution of targets. We also show that these results may reflect an emergent property of associative learning in neural circuits. We demonstrate that adding Hebbian (associative) learning to a model network for reach planning leads to a continuous modification of network connections that biases network dynamics toward activity patterns associated with recent inputs. This learning process quantitatively captures the key results of our experimental data in human subjects, including the effect that recent experience has on the variance-bias tradeoff. This network also provides a good approximation of a normative Bayesian estimator. These observations illustrate how associative learning can incorporate recent experience into ongoing computations in a statistically principled way.

Coming Unbound: Disrupting Automatic Integration of Synesthetic Color and Graphemes by Transcranial Magnetic Stimulation of the Right Parietal Lobe

In some individuals, a visually presented letter or number automatically evokes the perception of a specific color, an experience known as color-grapheme synesthesia. It has been suggested that parietal binding mechanisms play a role in the phenomenon. We used a noninvasive stimulation technique, transcranial magnetic stimulation (TMS), to determine whether the posterior parietal lobe is critical for the integration of color and shape in color-grapheme synesthesia, as it appears to be for normal color-shape binding. Using a color-naming task with colored letters that were either congruent or incongruent with the synesthetic photism, we demonstrate that inhibition of the right posterior parietal lobe with repetitive TMS transiently attenuates synesthetic binding. These findings suggest that synesthesia (the induction of color from shape) relies on similar mechanisms as found in normal perception (where the perception of color is induced by wavelength).

Cerebellar activation during discrete and not continuous timed movements: an fMRI study.

Individuals with cerebellar lesions are impaired in the timing of repetitive movements that involve the concatenation of discrete events such as tapping a finger. In contrast, these individuals perform comparably to controls when producing continuous repetitive movements. Based on this, we have proposed that the cerebellum plays a key role in event timing-the representation of the temporal relationship between salient events related to the movement (e.g., flexion onset or contact with a response surface). In the current study, we used fMRI to examine cerebellar activity during discrete and continuous rhythmic movements. Participants produced rhythmic movements with the index finger either making smooth, continuous transitions between flexion and extension or with a pause inserted before each flexion phase making the movement discrete. Lateral regions in lobule VI, ipsilateral to the moving hand were activated in a similar manner for both conditions. However, activation in the superior vermis was significantly greater when the movements were discrete compared to when the movements were continuous. This pattern was not evident in cortical regions within the field of view, including M1 and SMA. The results are consistent with the hypothesis that subregions of the cerebellum are selectively engaged during tasks involving event timing.

Evidence of a Novel Somatopic Map in the Human Neocerebellum During Complex Actions

The human neocerebellum has been hypothesized to contribute to many high-level cognitive processes including attention, language, and working memory. Support for these nonmotor hypotheses comes from evidence demonstrating structural and functional connectivity between the lateral cerebellum and cortical association areas as well as a lack of somatotopy in lobules VI and VII, a hallmark of motor representations in other areas of the cerebellum and cerebral cortex. We set out to test whether somatotopy exists in these lobules by using functional magnetic resonance imaging to measure cerebellar activity while participants produced simple or complex movements, using either fingers or toes. We observed a previously undiscovered somatotopic organization in neocerebellar lobules VI and VIIA that was most prominent when participants executed complex movements. In contrast, activation in the anterior lobe showed a similar somatotopic organization for both simple and complex movements. While the anterior somatotopic representation responded selectively during ipsilateral movements, the new cerebellar map responded during both ipsi- and contralateral movements. The presence of a bilateral, task-dependent somatotopic map in the neocerebellum emphasizes an important role for this region in the control of skilled actions.

Increased Body Mass Index Is Associated With a Global and Distributed Decrease in White Matter Microstructural Integrity

Objective Obesity and decreased physical health are linked to deficits in several cognitive domains. The broad range of cognitive problems linked to obesity suggests a global mechanism that may interfere with multiple neural systems. We examined how variation in body mass index (BMI) is associated with the microstructural integrity of fiber connections in the human brain. Methods White matter structure was measured using diffusion tensor imaging in 28 participants (mean age = 30 years) with BMI scores ranging from normal weight to obese (19.5–45.7 kg/m2) based on standard BMI criteria. Results Using a whole-brain voxelwise analysis, we found that, across participants, the fractional anisotropy of white matter voxels parametrically decreased with increasing BMI (63% of white matter voxels). Midbrain and brainstem tracts were among the pathways most strongly associated with obesity (r = −0.18 to −0.33, df = 27, all p values < .05). We also observed a weaker overall diffusion signal in individuals with higher BMI than controls with normal weight (r = −0.14 to −0.71, df = 27, for 67% of fiber pathways tested, all p values < .05). After controlling for this decrease in general diffusivity, we found that decreases in fractional anisotropy stemmed from both a decrease in axial diffusivity (p < .05) and an increase in radial diffusivity (p < .05). Conclusions Our results show that increased BMI is globally associated with a reduction in white matter integrity throughout the brain, elucidating a potential mechanism by which changes in physical health may influence cognitive health.

Explicating the Face Perception Network with White Matter Connectivity

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.

Converging structural and functional connectivity of orbitofrontal, dorsolateral prefrontal, and posterior parietal cortex in the human striatum.

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.

Visuotopic cortical connectivity underlying attention revealed with white-matter tractography.

Visual attention selects behaviorally relevant information for detailed processing by resolving competition for representation among stimuli in retinotopically organized visual cortex. The signals that control this attentional biasing are thought to arise in a frontoparietal network of several brain regions, including posterior parietal cortex. Recent studies have revealed a topographic organization in the intraparietal sulcus (IPS) that mirrors the retinotopic organization in visual cortex, suggesting that connectivity between these regions might provide the mechanism by which attention acts on early cortical representations. Using white-matter imaging and functional MRI, we examined the connectivity between two topographic regions of IPS and six retinotopically defined areas in visual cortex. We observed a strong positive correlation between attention modulations in visual cortex and connectivity of posterior IPS, suggesting that these white-matter connections mediate the attention signals that resolve competition among stimuli for representation in visual cortex. Furthermore, we found that connectivity between IPS and V1 consistently respects visuotopic boundaries, whereas connections to V2 and V3/VP disperse by 60%. This pattern is consistent with changes in receptive field size across regions and suggests that a primary role of posterior IPS is to code spatially specific visual information. In summary, we have identified white-matter pathways that are ideally suited to carry attentional biasing signals in visuotopic coordinates from parietal control regions to sensory regions in humans. These results provide critical evidence for the biased competition theory of attention and specify neurobiological constraints on the functional brain organization of visual attention.