Lower-Grade Gliomas: An Epidemiological Voxel-Based Analysis of Location and Proximity to Eloquent Regions

BackgroundGlioma is the most common intra-axial tumor, and its location relative to critical areas of the brain is important for treatment decision-making. Studies often report tumor location based on anatomical taxonomy alone since the estimation of eloquent regions requires considerable knowledge of functional neuroanatomy and is, to some degree, a subjective measure. An unbiased and reproducible method to determine tumor location and eloquence is desirable, both for clinical use and for research purposes.ObjectiveTo report on a voxel-based method for assessing anatomical distribution and proximity to eloquent regions in diffuse lower-grade gliomas (World Health Organization grades 2 and 3).MethodsA multi-institutional population-based dataset of adult patients (≥18 years) histologically diagnosed with lower-grade glioma was analyzed. Tumor segmentations were registered to a standardized space where two anatomical atlases were used to perform a voxel-based comparison of the proximity of segmentations to brain regions of traditional clinical interest.ResultsExploring the differences between patients with oligodendrogliomas, isocitrate dehydrogenase (IDH) mutated astrocytomas, and patients with IDH wild-type astrocytomas, we found that the latter were older, more often had lower Karnofsky performance status, and that these tumors were more often found in the proximity of eloquent regions. Eloquent regions are found slightly more frequently in the proximity of IDH-mutated astrocytomas compared to oligodendrogliomas. The regions included in our voxel-based definition of eloquence showed a high degree of association with performing biopsy compared to resection.ConclusionWe present a simple, robust, unbiased, and clinically relevant method for assessing tumor location and eloquence in lower-grade gliomas.

Mental Visualization in the Cerebellum: Rapid Non-motor Learning at Sub-Lobular and Causal Network Levels

It is generally understood that the main role of the cerebellum is in movement planning and coordination, but neuroimaging has led to striking findings of its involvement in many aspects of cognitive processing. Mental visualization is such a cognitive process, extensively involved in learning and memory, artistic and inventive creativity, etc. Here, our aim was to conduct a multidimensional study of cerebellar involvement in the non-motor cognitive tasks. First, we used fMRI to investigate whether the cognitive task of visualization from an immediate memory of complex spatial structures (line drawings) engages the cerebellum, and identified a cerebellar network of both strongly activated and suppressed regions. Second, the task-specificity of these regions was examined by comparative analysis with the task of perceptual exploration and memorization of the drawings to be later visualized from memory. BOLD response patterns over the iterations of each task differed significantly; unexpectedly, the suppression grew markedly stronger in visualization. Third, to gain insights in the organization of these regions into cerebellar networks, we determined the directed inter-regional causal influences using Granger Causal Connectivity analysis. Additionally, the causal interactions of the cerebellar networks with a large-scale cortical network, the Default Mode Network (DMN), were studied. Fourth, we investigated rapid cognitive learning in the cerebellum at the level of short-term BOLD response evolution within each region of interest, and at the higher level of network reorganization. Our paradigm of interleaved sequences of iteration between two tasks combined with some innovative analyses were instrumental in addressing these questions. In particular, rapid forms of non-motor learning that strongly drive cerebellar plasticity through mental visualization were uncovered and characterized at both sub-lobular and network levels. Collectively, these findings provide novel and expansive insights into high-order cognitive functions in the cerebellum, and its macroscale functional neuroanatomy. They represent a basis for a framework of rapid cerebellar reorganization driven by non-motor learning, with implications for the enhancement of cognitive abilities such as learning and memory.

Where to next? A Systematic Review identifying the gaps in the Cognitive Literature regarding Attention and Working Memory in Early Childhood

Conscious, goal-directed behaviours in adults require attention and access to working memory (WM) and utilize the same visually driven parieto-frontal neural networks of the brain. Logically, the development of this goal-directed network must also underpin all learning and cognitive maturation.in children. Thus, we sought to systematically review the current literature relating to development of cognitively defined constructs of attention and WM in children from birth to 7 years old from the perspective of functional brain development. Abstracts were extracted from electronic databases (Medline, PsycINFO, EMBASE, Science Direct, and PubMed) and 24/4212 studies were identified as meeting the inclusion, risk of bias and validity criteria. Only 3/24 studies investigated visual attention in infants using preferential looking tasks and monitoring of eye movements. Twenty-one out of the 24 studies investigating attention in children aged 2+ years used visually based tasks that required participants to possess the ability to visually and auditorily attend to the presented stimuli, have adequate receptive language to understand and remember the verbal instructions, and WM and executive functions to respond verbally or manually. All studies found attention and WM to be significantly related. Few studies controlled for the inherent maturation of assumed cognitive skills or motor development, though a number have measured time required to perform a task. Further, there are very few studies in the literature where concurrent psychophysical, gaze tracking or brain imaging techniques have been used to investigate the functional neuroanatomy of attention and working memory networks in young children. Future exploration with more biologically and cognitively integrated paradigms are needed to clarify relationships between each aspect of attention and WM (verbal and visual), especially with respect to the timing and source localization of the temporal trajectory of neural activation during task completion.

Choice-relevant information transformation along a ventrodorsal axis in the medial prefrontal cortex

Choice-relevant brain regions in prefrontal cortex may progressively transform information about options into choices. Here, we examine responses of neurons in four regions of the medial prefrontal cortex as macaques performed two-option risky choices. All four regions encode economic variables in similar proportions and show similar putative signatures of key choice-related computations. We provide evidence to support a gradient of function that proceeds from areas 14 to 25 to 32 to 24. Specifically, we show that decodability of twelve distinct task variables increases along that path, consistent with the idea that regions that are higher in the anatomical hierarchy make choice-relevant variables more separable. We also show progressively longer intrinsic timescales in the same series. Together these results highlight the importance of the medial wall in choice, endorse a specific gradient-based organization, and argue against a modular functional neuroanatomy of choice.

Linking Individual Differences in Personalized Functional Network Topography to Psychopathology in Youth

The spatial layout of large-scale functional brain networks differs between individuals and is particularly variable in association cortex that has been implicated in a broad range of psychiatric disorders. However, it remains unknown whether this variation in functional topography is related to major dimensions of psychopathology in youth. Capitalizing on a large sample with 27-minutes of high-quality functional MRI data (n=790, ages 8-23 years) and advances in machine learning, we examined associations between functional topography and four correlated dimensions of psychopathology (fear, psychosis, externalizing, anxious-misery) as well as an overall psychopathology factor. We found that functional topography significantly predicted individual differences in dimensions of psychopathology, driven mainly by robust associations between topography and overall psychopathology. Reduced cortical representations of association networks were among the most important features of the model. Our results emphasize the value of considering systematic differences in functional neuroanatomy for personalized diagnostics and therapeutics in psychiatry.