Mapping Brain Development and Decoding Brain Activity with Diffuse Optical Tomography

Functional neuroimaging has been used to map brain function as well as decode information from brain activity. However, applications like studying early brain development or enabling augmentative communication in patients with severe motor disabilities have been constrained by extant imaging modalities, which can be challenging to use in young children and entail major tradeoffs between logistics and image quality. Diffuse optical tomography (DOT) is an emerging method combining logistical advantages of optical imaging with enhanced image quality. Here, we developed one of the world’s largest DOT systems for high-performance optical brain imaging in children. From visual cortex activity in adults, we decoded the locations of checkerboard visual stimuli, e.g. localizing a 60 degree wedge rotating through 36 positions with an error of 25.8±24.7 degrees. Using animated movies as more child-friendly stimuli, we mapped reproducible responses to speech and faces with DOT in awake, typically developing 1-7 year-old children and adults. We then decoded with accuracy significantly above chance which movie a participant was watching or listening to from DOT data. This work lays a valuable foundation for ongoing research with wearable imaging systems and increasingly complex algorithms to map atypical brain development and decode covert semantic information in clinical populations

Effect of event classifiers on jet quenching-like signatures in high-multiplicity p+pp+p collisions at s=13\sqrt{s} = 13 TeV

The motivation behind exploring jet quenching-like phenomena in small systems arises from the experimental observation of heavy-ion-like behavior of particle production in high-multiplicity proton-proton ($p+p$) collisions. Quantifying the jet quenching in $p+p$ collisions is a challenging task, as the magnitude of the nuclear modification factor ($R_{\rm AA}$ or $R_{\rm CP}$), which is used to quantify jet quenching, is influenced by several factors, such as the estimation of centrality and the scaling factor. The most common method of centrality estimation employed by the ALICE collaboration is based on measuring charged-particle multiplicity with the V0 detector situated at the forward rapidity. This technique of centrality estimation makes the event sample biased towards hard processes like multijet final states. This bias of the V0 detector towards hard processes makes it difficult to study the jet quenching effect in high-multiplicity $p+p$ collisions. In the present article, we propose to explore the use of a new and robust event classifier, flattenicity which is sensitive to both the multiple soft partonic interactions and hard processes. The $\mathcal{P}_{\rm CP}$, a quantity analogous to $R_{\rm CP}$, has been estimated for high-multiplicity $p+p$ collisions at $\sqrt{s} = 13$ TeV using \texttt{PYTHIA8} model for both the V0M (the multiplicity classes selected based on V0 detector acceptance) as well as flattenicity. The evolution of $\mathcal{P}_{\rm CP}$ with $p_{\rm T}$ shows a heavy-ion-like effect for flattencity which is attributed to the selection of softer transverse momentum particles in high-multiplicity $p+p$ collisions.Comment: 7 pages, 6 figure

Mapping cortical activations underlying covert and overt language production using high-density diffuse optical tomography

Gold standard neuroimaging modalities such as functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and more recently electrocorticography (ECoG) have provided profound insights regarding the neural mechanisms underlying the processing of language, but they are limited in applications involving naturalistic language production especially in developing brains, during face-to-face dialogues, or as a brain-computer interface. High-density diffuse optical tomography (HD-DOT) provides high-fidelity mapping of human brain function with comparable spatial resolution to that of fMRI but in a silent and open scanning environment similar to real-life social scenarios. Therefore, HD-DOT has potential to be used in naturalistic settings where other neuroimaging modalities are limited. While HD-DOT has been previously validated against fMRI for mapping the neural correlates underlying language comprehension and covert (i.e., silent ) language production, HD-DOT has not yet been established for mapping the cortical responses to overt (i.e., out loud ) language production. In this study, we assessed the brain regions supporting a simple hierarchy of language tasks: silent reading of single words, covert production of verbs, and overt production of verbs in normal hearing right-handed native English speakers (n = 33). First, we found that HD-DOT brain mapping is resilient to movement associated with overt speaking. Second, we observed that HD-DOT is sensitive to key activations and deactivations in brain function underlying the perception and naturalistic production of language. Specifically, statistically significant results were observed that show recruitment of regions in occipital, temporal, motor, and prefrontal cortices across all three tasks after performing stringent cluster-extent based thresholding. Our findings lay the foundation for future HD-DOT studies of imaging naturalistic language comprehension and production during real-life social interactions and for broader applications such as presurgical language assessment and brain-machine interfaces

Decoding visual information from high-density diffuse optical tomography neuroimaging data

BACKGROUND: Neural decoding could be useful in many ways, from serving as a neuroscience research tool to providing a means of augmented communication for patients with neurological conditions. However, applications of decoding are currently constrained by the limitations of traditional neuroimaging modalities. Electrocorticography requires invasive neurosurgery, magnetic resonance imaging (MRI) is too cumbersome for uses like daily communication, and alternatives like functional near-infrared spectroscopy (fNIRS) offer poor image quality. High-density diffuse optical tomography (HD-DOT) is an emerging modality that uses denser optode arrays than fNIRS to combine logistical advantages of optical neuroimaging with enhanced image quality. Despite the resulting promise of HD-DOT for facilitating field applications of neuroimaging, decoding of brain activity as measured by HD-DOT has yet to be evaluated. OBJECTIVE: To assess the feasibility and performance of decoding with HD-DOT in visual cortex. METHODS AND RESULTS: To establish the feasibility of decoding at the single-trial level with HD-DOT, a template matching strategy was used to decode visual stimulus position. A receiver operating characteristic (ROC) analysis was used to quantify the sensitivity, specificity, and reproducibility of binary visual decoding. Mean areas under the curve (AUCs) greater than 0.97 across 10 imaging sessions in a highly sampled participant were observed. ROC analyses of decoding across 5 participants established both reproducibility in multiple individuals and the feasibility of inter-individual decoding (mean AUCs \u3e 0.7), although decoding performance varied between individuals. Phase-encoded checkerboard stimuli were used to assess more complex, non-binary decoding with HD-DOT. Across 3 highly sampled participants, the phase of a 60° wide checkerboard wedge rotating 10° per second through 360° was decoded with a within-participant error of 25.8±24.7°. Decoding between participants was also feasible based on permutation-based significance testing. CONCLUSIONS: Visual stimulus information can be decoded accurately, reproducibly, and across a range of detail (for both binary and non-binary outcomes) at the single-trial level (without needing to block-average test data) using HD-DOT data. These results lay the foundation for future studies of more complex decoding with HD-DOT and applications in clinical populations

Observation of heavy-ion like phenomena in high-multiplicity p+p collisions at LHC energies

Suppression of inclusive hadron yields in nuclear systems with respect to the elementary ${p}+{p}$ collisions is believed to be caused by the jet quenching. The quantification of the jet quenching via the nuclear modification factor is difficult to conceive in small systems since it is influenced by several experimental factors such as the estimation of the centrality and the scaling factor. In this article we propose an alternate approach to address this issue and explore the possibility of measuring jet quenching in a high-multiplicity ${p}+{p}$ system using the Pythia

Beneficiary satisfaction with mental health care services: A cross sectional study at district mental health programme OPD of Ganjam District

contact drop outs. Active participation of people with mental illness (PWMI) & their caregivers is of utmost important to achieve the objectives of DMHP and reduce the longstanding mental & neurological disorder (MND) cases. Aim: To describe the beneficiary satisfaction with mental health care services under DMHP Ganjam. Materials & Methods: Descriptive theoretical framework & cross-sectional study design. Beneficiaries were selected by probability sampling. Beneficiary satisfaction was measured by using questionnaire in a Likert scale. Results: Communication skills of doctor, waiting time for consultation, availability of drugs at drug distribution centre were in the 4th quartile, cleanliness of OPD and Drug distribution centre functioning were in 3rd quartile. Adequacy of information available at hospital and waiting time at registration were in 2nd quartile. The functioning of NIDAN diagnostic centre and behaviour of hospital staff other than doctor got lowest score and were in 1st quartile. Conclusion: The distribution score in quartiles gave a preliminary evidence on components of beneficiary satisfaction on mental health care services at DMHP OPD. Recommendation: Counselling on service availability at NIDAN, training on communication skill for hospital staff, steps to reduce waiting time & need assessment of beneficiaries. Participatory research to explore the beneficiary perception needs to be carried out. Keywords: Mental Health, PWMI, MN

Prediction of Plasma Membrane Cholesterol from 7-Transmembrane Receptor Using Hybrid Machine Learning Algorithm

The researches have been made on G-protein coupled receptors (GPCRs) over the long-ago decades. GPCR is also named as 7-transmembrane (7TM) receptor. According to biological prospective GPCRs consist of large protein family with respective subfamilies and are mediated by different physiological phenomena like taste, smell, vision etc. The main functionality of these 7TM receptors is signal transduction among various cells. In human genome, cell membrane plays significant role. All cells are made up of trillion of cells and have dissimilar functionality. Cell membrane composed of different components. GPCRs are reported to be modulated by membrane cholesterol by interacting with cholesterol recognition amino acid consensus (L/V-X (1-5)-Y-X (1-5)-R/K) (CRAC) or reverse orientation of CRAC (R/K-X (1-5)-Y-X (1-5)-L/V) (CARC) motifs present in the TM helices. Among all, cholesterol is one who is regulated by membrane proteins. Here we took GPCR as membrane proteins and this protein modulates membrane cholesterol. According to cell biology, GPCR regulates a wide diversity of vital cellular processes and are targeted by a huge fraction of approved drugs. In this paper we have concentrated our investigation on membrane protein with membrane cholesterol. A hybrid algorithm consisting of spectral clustering and support vector machine is proposed for prediction of membrane cholesterol with GPCR. Spectral clustering uses graph nodes for calculating the cluster points and also it considers other concept such as similarity matrix, low-dimensional space for projecting the data points and upon this parameter at last construct the cluster centre. Supervised learning method is used for solving regression and classification problems. From the analysis we found that our result shows better prediction accuracy in terms of time complexity when compared with two existing models such as fuzzy c-means (FCM) and rough set with FCM model

An Insoluble frontotemporal lobar degeneration-associated TDP-43 C-terminal fragment causes neurodegeneration and hippocampus pathology in transgenic mice

Frontotemporal dementia (FTD) causes progressive personality, behavior and/or language disturbances and represents the second most common form of dementia under the age of 65. Over half of all FTD cases are classified pathologically as frontotemporal lobar degeneration (FTLD) with TAR DNA-binding protein of 43 kDa (TDP-43) pathology (FTLD-TDP). In FTLD-TDP brains, TDP-43 is phosphorylated, C-terminally cleaved, lost from the nucleus and accumulates in the cytoplasm and processes of neurons and glia. However, the contribution of TDP-43 C-terminal fragments (CTFs) to pathogenesis remains poorly understood. Here, we developed transgenic (Tg) mice with forebrain Camk2a-controlled doxycycline-suppressible expression of a TDP-43 CTF (amino acids 208-414, designated 208 TDP-43 CTF), previously identified in FTLD-TDP brains. In these 208 TDP-43 Tg mice, detergent-insoluble 208 TDP-43 CTF was present in a diffuse punctate pattern in neuronal cytoplasm and dendrites without forming large cytoplasmic inclusions. Remarkably, the hippocampus showed progressive neuron loss and astrogliosis in the dentate gyrus (DG). This was accompanied by phosphorylated TDP-43 in the CA1 subfield, and ubiquitin and mitochondria accumulations in the stratum lacunosum moleculare (SLM) layer, without loss of endogenous nuclear TDP-43. Importantly, 208 TDP-43 CTF and phosphorylated TDP-43 were rapidly cleared when CTF expression was suppressed in aged Tg mice, which ameliorated neuron loss in the DG despite persistence of ubiquitin accumulation in the SLM. Our results demonstrate that Camk2a-directed 208 TDP-43 CTF overexpression is sufficient to cause hippocampal pathology and neurodegeneration in vivo, suggesting an active role for TDP-43 CTFs in the pathogenesis of FTLD-TDP and related TDP-43 proteinopathies.14 page(s

Functional recovery in new mouse models of ALS/FTLD after clearance of pathological cytoplasmic TDP-43

Accumulation of phosphorylated cytoplasmic TDP-43 inclusions accompanied by loss of normal nuclear TDP-43 in neurons and glia of the brain and spinal cord are the molecular hallmarks of amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD-TDP). However, the role of cytoplasmic TDP-43 in the pathogenesis of these neurodegenerative TDP-43 proteinopathies remains unclear, due in part to a lack of valid mouse models. We therefore generated new mice with doxycycline (Dox)-suppressible expression of human TDP-43 (hTDP-43) harboring a defective nuclear localization signal (∆NLS) under the control of the neurofilament heavy chain promoter. Expression of hTDP-43∆NLS in these 'regulatable NLS' (rNLS) mice resulted in the accumulation of insoluble, phosphorylated cytoplasmic TDP-43 in brain and spinal cord, loss of endogenous nuclear mouse TDP-43 (mTDP-43), brain atrophy, muscle denervation, dramatic motor neuron loss, and progressive motor impairments leading to death. Notably, suppression of hTDP-43∆NLS expression by return of Dox to rNLS mice after disease onset caused a dramatic decrease in phosphorylated TDP-43 pathology, an increase in nuclear mTDP-43 to control levels, and the prevention of further motor neuron loss. rNLS mice back on Dox also showed a significant increase in muscle innervation, a rescue of motor impairments, and a dramatic extension of lifespan. Thus, the rNLS mice are new TDP-43 mouse models that delineate the timeline of pathology development, muscle denervation and neuron loss in ALS/FTLD-TDP. Importantly, even after neurodegeneration and onset of motor dysfunction, removal of cytoplasmic TDP-43 and the concomitant return of nuclear TDP-43 led to neuron preservation, muscle re-innervation and functional recovery.18 page(s