Logan T. Dowdle

Programming languages combine meaningful symbols with formal syntactic rules, raising questions about whether code comprehension recruits neural processes similar to those involved in natural language and how these processes change with programming expertise. We used event-related potentials (ERPs) to examine semantic and syntactic processing during active code comprehension in 51 participants with varying Python expertise. Participants mentally computed the output of Python code snippets presented in math-like and language-like contexts. Code contained either no conflict, a semantic conflict, or a syntactic conflict. Semantic and syntactic processing were assessed using the N400 (300–500 ms) and P600 (500–800 ms), respectively. Semantic conflicts elicited robust N400 responses, whereas syntactic conflicts elicited robust P600 responses, indicating sensitivity to both meaning and formal structure during code comprehension. Programming expertise selectively modulated the P600 effect in math-like contexts, with experts showing larger conflict-related P600 responses than novices, particularly at central and parietal electrodes. Python proficiency was also positively associated with conflict-related P600 amplitude. These findings suggest that code comprehension engages neural processes sensitive to semantic and syntactic information, while programming expertise is particularly associated with enhanced sensitivity to formal syntactic structure in computational contexts.

The thalamic reticular nucleus (TRN) is a thin, inhibitory shell surrounding the thalamus. It regulates the thalamocortical information flow, and thereby plays a central role in attention, task switching, and the sleep-wake cycle. Despite its importance, the TRN remains poorly studied in the human brain. This is largely because its small size and deep anatomical location limit its visibility with conventional non-invasive neuroimaging techniques. Here, we assessed whether the human TRN can be reliably visualised and segmented in vivo using ultra-high field (UHF) magnetic resonance imaging (MRI) at 7 Tesla. High resolution (0.35 mm isotropic) partial-brain T2* and T1 scans were acquired from healthy individuals, followed by manual delineation of the TRN. These in vivo segmentations were compared with TRN estimates obtained from two high-quality postmortem datasets serving as an anatomical reference. In vivo segmentations of TRN volume and thickness closely matched measurements derived from the postmortem reference datasets, and quantitative comparisons showed high consistency in TRN shape and location across individuals while also capturing meaningful inter-individual variability. Using these segmentations, we constructed a publicly available probabilistic atlas of the human TRN. This atlas provides a new resource for incorporating TRN anatomy into functional, structural, and clinical neuroimaging studies. Our findings demonstrate that the human TRN can be robustly mapped in vivo at 7T and establish a foundation for future investigations into its structure and function.

Introduction: Functional magnetic resonance imaging (fMRI) studies examining cue-reactivity in cannabis use disorder (CUD) to date have either involved non-treatment seeking participants or been small. We addressed this gap by administering an fMRI cue-reactivity task to CUD participants entering two separate clinical trials. Methods: Treatment-seeking participants with moderate or severe CUD had behavioral craving measured at baseline via the Marijuana Craving Questionnaire (MCQ-SF). They additionally completed a visual cannabis cue-reactivity paradigm during fMRI following 24-hours of abstinence from cannabis. During fMRI, the Blood Oxygen Level Dependent (BOLD) signal was acquired while participants viewed cannabis-images or matched-neutral-images. BOLD responses were correlated with the MCQ-SF using a General Linear Model. Results: N=65 participants (32% female; mean age 30.4±9.9SD) averaged 46.3±15.5SD on the MCQ-SF. When contrasting cannabis-images vs. matched-neutral-images, participants showed greater BOLD response in bilateral ventromedial prefrontal, dorsolateral prefrontal, anterior cingulate, and visual cortices, as well as the striatum. Similarly, there was stronger task-based functional-connectivity (tbFC) between the medial prefrontal cortex and both the amygdala and the visual cortex. There were no significant differences in either activation or tbFC between studies or between sexes. Craving negatively correlated with BOLD response in the left ventral striatum (R ² =-0.25; p =0.01). Conclusions: We found that, among two separate treatment-seeking CUD groups, cannabis cue-reactivity was evidenced by greater activation and tbFC in regions related to executive function and reward processing, and craving was negatively associated with cue-reactivity in the ventral striatum. Future directions include examining if pharmacological, neuromodulatory, or psychosocial interventions can alter corticostriatal cue-reactivity.

Extensive sampling of neural activity during rich cognitive phenomena is critical for robust understanding of brain function. Here we present the Natural Scenes Dataset (NSD), in which high-resolution functional magnetic resonance imaging responses to tens of thousands of richly annotated natural scenes were measured while participants performed a continuous recognition task. To optimize data quality, we developed and applied novel estimation and denoising techniques. Simple visual inspections of the NSD data reveal clear representational transformations along the ventral visual pathway. Further exemplifying the inferential power of the dataset, we used NSD to build and train deep neural network models that predict brain activity more accurately than state-of-the-art models from computer vision. NSD also includes substantial resting-state and diffusion data, enabling network neuroscience perspectives to constrain and enhance models of perception and memory. Given its unprecedented scale, quality and breadth, NSD opens new avenues of inquiry in cognitive neuroscience and artificial intelligence.

Functional magnetic resonance imaging (fMRI) has become an indispensable tool for investigating the human brain. However, the inherently poor signal-to-noise-ratio (SNR) of the fMRI measurement represents a major barrier to expanding its spatiotemporal scale as well as its utility and ultimate impact. Here we introduce a denoising technique that selectively suppresses the thermal noise contribution to the fMRI experiment. Using 7-Tesla, high-resolution human brain data, we demonstrate improvements in key metrics of functional mapping (temporal-SNR, the detection and reproducibility of stimulus-induced signal changes, and accuracy of functional maps) while leaving the amplitude of the stimulus-induced signal changes, spatial precision, and functional point-spread-function unaltered. We demonstrate that the method enables the acquisition of ultrahigh resolution (0.5 mm isotropic) functional maps but is also equally beneficial for a large variety of fMRI applications, including supra-millimeter resolution 3- and 7-Tesla data obtained over different cortical regions with different stimulation/task paradigms and acquisition strategies.