Novel Anthracene and Carbazole Based Aggregation Induced Enhanced Emission Active Schiff Base as a Selective Sensor for Cu ions.

In present work our group has synthesized two novel Schiff-bases, Di-Carbazole based Schiff-base (DB-1) and Di-Anthracene based Schiff-base (DB-2) using condensation reaction and characterized thorough different spectroscopic techniques such as mass spectrometry, IR spectroscopy, H and C NMR spectroscopy. Furthermore, the AIE(Aggregation induced emission) studies were done using water-THF mixture. As compared to pure THF, the DB-2 showed a 17.

Methylphenidate modulates motor cortical dynamics and behavior.

Methylphenidate (MPH, brand: Ritalin) is a common stimulant used both medically and non-medically. Though typically prescribed for its cognitive effects, MPH also affects movement. While it is known that MPH noncompetitively blocks the reuptake of catecholamines through inhibition of dopamine and norepinephrine transporters, a critical step in exploring how it affects behavior is to understand how MPH directly affects neural activity.

Large-scale high-density brain-wide neural recording in nonhuman primates.

High-density, integrated silicon electrodes have begun to transform systems neuroscience, by enabling large-scale neural population recordings with single cell resolution. Existing technologies, however, have provided limited functionality in nonhuman primate species such as macaques, which offer close models of human cognition and behavior. Here, we report the design, fabrication, and performance of Neuropixels 1.

Cortical preparatory activity indexes learned motor memories.

The brain's remarkable ability to learn and execute various motor behaviours harnesses the capacity of neural populations to generate a variety of activity patterns. Here we explore systematic changes in preparatory activity in motor cortex that accompany motor learning. We trained rhesus monkeys to learn an arm-reaching task in a curl force field that elicited new muscle forces for some, but not all, movement directions.

High-fidelity musculoskeletal modeling reveals that motor planning variability contributes to the speed-accuracy tradeoff.

A long-standing challenge in motor neuroscience is to understand the relationship between movement speed and accuracy, known as the speed-accuracy tradeoff. Here, we introduce a biomechanically realistic computational model of three-dimensional upper extremity movements that reproduces well-known features of reaching movements. This model revealed that the speed-accuracy tradeoff, as described by Fitts' law, emerges even without the presence of motor noise, which is commonly believed to underlie the speed-accuracy tradeoff.

Computation Through Neural Population Dynamics.

Significant experimental, computational, and theoretical work has identified rich structure within the coordinated activity of interconnected neural populations. An emerging challenge now is to uncover the nature of the associated computations, how they are implemented, and what role they play in driving behavior. We term this computation through neural population dynamics.

Causal Role of Motor Preparation during Error-Driven Learning.

Current theories suggest that an error-driven learning process updates trial-by-trial to facilitate motor adaptation. How this process interacts with motor cortical preparatory activity-which current models suggest plays a critical role in movement initiation-remains unknown. Here, we evaluated the role of motor preparation during visuomotor adaptation.

Accurate Estimation of Neural Population Dynamics without Spike Sorting.

A central goal of systems neuroscience is to relate an organism's neural activity to behavior. Neural population analyses often reduce the data dimensionality to focus on relevant activity patterns. A major hurdle to data analysis is spike sorting, and this problem is growing as the number of recorded neurons increases.

Structure and variability of delay activity in premotor cortex.

Voluntary movements are widely considered to be planned before they are executed. Recent studies have hypothesized that neural activity in motor cortex during preparation acts as an 'initial condition' which seeds the proceeding neural dynamics. Here, we studied these initial conditions in detail by investigating 1) the organization of neural states for different reaches and 2) the variance of these neural states from trial to trial.

Unsupervised Discovery of Demixed, Low-Dimensional Neural Dynamics across Multiple Timescales through Tensor Component Analysis.

Perceptions, thoughts, and actions unfold over millisecond timescales, while learned behaviors can require many days to mature. While recent experimental advances enable large-scale and long-term neural recordings with high temporal fidelity, it remains a formidable challenge to extract unbiased and interpretable descriptions of how rapid single-trial circuit dynamics change slowly over many trials to mediate learning. We demonstrate a simple tensor component analysis (TCA) can meet this challenge by extracting three interconnected, low-dimensional descriptions of neural data: neuron factors, reflecting cell assemblies; temporal factors, reflecting rapid circuit dynamics mediating perceptions, thoughts, and actions within each trial; and trial factors, describing both long-term learning and trial-to-trial changes in cognitive state.

Neural Population Dynamics Underlying Motor Learning Transfer.

Covert motor learning can sometimes transfer to overt behavior. We investigated the neural mechanism underlying transfer by constructing a two-context paradigm. Subjects performed cursor movements either overtly using arm movements, or covertly via a brain-machine interface that moves the cursor based on motor cortical activity (in lieu of arm movement).

In Vivo Interrogation of Spinal Mechanosensory Circuits.

Spinal dorsal horn circuits receive, process, and transmit somatosensory information. To understand how specific components of these circuits contribute to behavior, it is critical to be able to directly modulate their activity in unanesthetized in vivo conditions. Here, we develop experimental tools that enable optogenetic control of spinal circuitry in freely moving mice using commonly available materials.

MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans.

Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi)automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65-80 y).

Neuronal Complexity in Subthalamic Nucleus is Reduced in Parkinson's Disease.

Several theories posit increased Subthalamic Nucleus (STN) activity is causal to Parkinsonism, yet in our previous study we showed that activity from 113 STN neurons from two epilepsy patients and 103 neurons from nine Parkinson's disease (PD) patients demonstrated no significant differences in frequencies or in the coefficients of variation of mean discharge frequencies per 1-s epochs. We continued our analysis using point process modeling to capture higher order temporal dynamics; in particular, bursting, beta-band oscillations, excitatory and inhibitory ensemble interactions, and neuronal complexity. We used this analysis as input to a logistic regression classifier and were able to differentiate between PD and epilepsy neurons with an accuracy of 92%.

Non-invasive estimation of skin thickness from hyperspectral imaging and validation using echography.

Background: The skin is the largest organ and is subject to the greatest exposure to outside elements throughout one׳s lifetime. Current data by the American Academy of Dermatology suggests that approximately ten people die each hour worldwide due to skin related conditions. Cancers such as melanoma are growths that originate in the epidermis.

Endocardial surface delineation in 3-D transesophageal echocardiography.

We describe and compare several methods for recovering endocardial walls from 3-D transesophageal echocardiography (3-D TEE), which can help with diagnostics or providing input into biomechanical models. We employ a segmentation method based on 3-D level sets that maximizes enclosed volume while minimizing surface area and uses a growth inhibition function that includes 3-D gradient magnitude (to locate the endocardial walls) and a thin tissue detector (for the mitral valve leaflets). We also study delineation using a graph cut method that performs automated seeding by leveraging a fast radial symmetry transform to determine a central axis along which the 3-D volume is warped into a cylindrical coordinate space.

Estimating physiological skin parameters from hyperspectral signatures.

We describe an approach for estimating human skin parameters, such as melanosome concentration, collagen concentration, oxygen saturation, and blood volume, using hyperspectral radiometric measurements (signatures) obtained from in vivo skin. We use a computational model based on Kubelka-Munk theory and the Fresnel equations. This model forward maps the skin parameters to a corresponding multiband reflectance spectra.