PREEMACS: Pipeline for preprocessing and extraction of the macaque brain surface

García-Saldivar, Pamela; Garimella, Arun; Garza‐Villarreal, Eduardo A.; Méndez, Felipe A.; Concha, Luis; Merchant, Hugo

doi:10.1016/j.neuroimage.2020.117671

Cited by 14 publications

(17 citation statements)

References 78 publications

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Section: Discussionmentioning

confidence: 99%

“…Tuning and modularity are mechanisms for division of labor that are widely used in cortical and subcortical circuits to represent sensory, cognitive and motor information (Hubel, 1977; Georgopoulos et al, 2007; Goldman-Rakic et al, 1984; Mountcastle, 1998; Naselaris et al, 2006). Interval tuning can provide large flexibility to encode the passage of time and to predict events across behaviors that require the integration of timing with other task parameters that have a different mapping framework in MPC (Garcia-Saldivar et al, 2021; Yu et al, 2005). Since the width of cell tuning is wide, interval tuned neurons can also show temporal scaling (Crowe et al, 2014; Henke et al, 2021; Merchant et al, 2013a), which can be the substrate of the observed mixed timing encoding that combines amplitude modulation and temporal scaling.…”

Section: Discussionmentioning

confidence: 99%

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Amodal population clock in the primate medial premotor system for rhythmic tapping

Betancourt¹,

Pérez²,

Gámez

et al. 2022

Preprint

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Precise timing is a fundamental requisite for a select group of complex actions, such as music appreciation and performance, where subjects extract the regular beat of a rhythmic sequence to generate an internal pulse representation that allows for predictive responses to the beat. The neural substrate for beat extraction and response entrainment to auditory and visual rhythms is still largely unknown. Here we recorded and analyzed the population activity of hundreds of MPC neurons of two rhesus monkeys performing an isochronous tapping task guided by brief flashing stimuli or auditory tones. The animals showed a strong bias towards visual metronomes, with rhythmic tapping that was more precise and accurate than for auditory metronomes. The population dynamics in state space as well as the corresponding neural sequences shared the following properties across modalities: the circular dynamics of the neural trajectories and the neural sequences formed a regenerating loop for every produced interval, producing a relative time representation; the trajectories converged in similar state space at tapping times while the moving bumps restart at this point, resetting the beat-based clock; the tempo of the synchronized tapping was encoded by a combination of amplitude modulation and temporal scaling in the neural trajectories. The latter correlated with a mixture of response duration and recruitment properties in the neural sequences. In addition, the modality induced a displacement in the neural trajectories in auditory and visual subspaces without greatly altering time keeping mechanism. These results suggest that the interaction between the amodal internal representation of pulse within MPC and a modality specific external input generates a neural rhythmic clock whose dynamics define the temporal execution of tapping using auditory and visual metronomes.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Amodal population clock in the primate medial premotor system for rhythmic tapping

Betancourt¹,

Pérez²,

Gámez

et al. 2022

Preprint

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“…The backbone of BEN is a U-shaped network with nonlocal attention architecture (NL-U-Net, Figure 2b ). Unlike previous approaches 12–1416–18 , the segmentation network of BEN is trained not from scratch but by leveraging domain transfer techniques achieved via two critical strategies: i) an adaptive batch normalization (AdaBN, Figure 2c ) strategy for adapting the statistical parameters of the batch normalization (BN) layers in a network trained on the source domain to the new data distribution of the target domain and ii) an iterative pseudolabeling procedure for semisupervised learning in which a Monte Carlo quality assessment (MCQA, Figure 2d ) method is proposed to assess the quality of the segmentations generated by the present network and to select the optimal pseudolabels to be added to the training set for the next iteration. These two strategies are fully automatic and do not require any human intervention.…”

Section: Resultsmentioning

confidence: 95%

“…Recently, deep learning (DL)-based algorithms have been developed to enable more accurate brain tissue segmentation in animals. For example, U-Net-based algorithms have been proposed to perform skull stripping automatically for NHPs (Garcia-Saldivar et al, 2021; Wang et al, 2021; Zhao et al, 2018; Zhong et al, 2021) and for rodents (De Feo et al, 2021; Hsu et al, 2020; Valverde et al, 2020). Although these DL-based approaches outperform traditional tools such as FreeSurfer or FSL in NHP and rodent brains, they have mostly been evaluated on a single species or a single MRI modality, and suffer severe performance degradation if they are applied to other species or modalities that differ from those represented in their training data.…”

Section: Introductionmentioning

confidence: 99%

A generalizable brain extraction net (BEN) for multimodal MRI data from rodents, nonhuman primates, and humans

Han

et al. 2022

Preprint

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Extracting brain tissue is usually a vital first step in processing brain magnetic resonance imaging (MRI) data in neuroimaging studies. However, a generalizable brain tissue extraction tool is still lacking. Here, we propose a domain-adaptive and semi-supervised deep neural network, named the Brain Extraction Net (BEN), to extract brain tissues across species, MRI modalities and platforms while requiring minimal or even no annotations. We have evaluated BEN on 18 independent datasets including 1,029 rodent and nonhuman primate scans as well as 4,601 human scans, covering five species, four modalities, and six platforms with various magnetic field strengths. Compared to conventional toolboxes, the superiority of BEN is illustrated by its accuracy, robustness, and generalizability. BEN provides uncertainty maps to quantify the confidence of the network’s decisions and to model interrater variability. We design BEN as an open-source software that provides interfaces compatible with several popular neuroimaging toolboxes. Our results demonstrate that BEN has great promise to enhance neuroimaging studies at high throughput for both preclinical and clinical applications.

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“…T1-weighted volumes and diffusion-weighted images (DWI) were obtained (see Methods). For each subject, the gray/white matter interface was identified using a surface mesh (Garcia-Saldivar et al, 2021). The apparent fiber density (AFD) (Raffelt et al, 2012), derived from the DWI using constrained spherical deconvolution (CSD) (Tournier et al, 2004), was sampled at each vertex of this mesh.…”

Section: Analysis Of White Mattermentioning

confidence: 99%

White matter structural bases for predictive tapping synchronization

García-Saldivar

Leon

Concha

et al. 2022

Preprint

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We determined the intersubject association between rhythmic entrainment abilities of human subjects during a synchronization continuation tapping task (SCT) and the macro and microstructural properties of their superficial (SWM) and deep (dWM) white matter. Diffusion-weighted images were obtained from 32 subjects who also performed the SCT with auditory or visual metronomes and five tempos ranging from 550 to 950 ms. We developed a method to determine the fiber density of U-fibers running tangentially to the cortex. Notably, the right audiomotor system showed individual differences in the density of U-fibers that were correlated with the degree of predictive entrainment across subjects. These correlations were selective for the synchronization epoch with auditory metronomes and were specific for tempos around 1.5 Hz. In addition, there was a significant association between predictive rhythmic entrainment and the density and bundle diameter of the corpus callosum (CC), forming a chronotopic map where behavioural correlations of short and long intervals were found with the anterior and posterior portions of the CC. Finally, the fiber bundle cross-section of the arcuate fasciculus, the CC, and the Superior Longitudinal Fasciculus showed a significant correlation with the mean asynchronies of the auditory SCT. These findings suggest that the structural properties of the SWM and dWM in the audiomotor system support the predictive abilities of subjects during rhythmic tapping, where the density of cortical U-fibers are linked to the preferred tapping tempo, while the bundle properties of CC define an interval selective topography that has an anterior posterior gradient.

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PREEMACS: Pipeline for preprocessing and extraction of the macaque brain surface

Cited by 14 publications

References 78 publications

Amodal population clock in the primate medial premotor system for rhythmic tapping

Amodal population clock in the primate medial premotor system for rhythmic tapping

A generalizable brain extraction net (BEN) for multimodal MRI data from rodents, nonhuman primates, and humans

White matter structural bases for predictive tapping synchronization

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