An Approach to Automatically Label and Order Brain Activity/Component Maps

Brain Connectivity, Journal Year: 2021, Volume and Issue: 12(1), P. 85 - 95

Published: May 27, 2021

Background: Functional magnetic resonance imaging (fMRI) is a brain technique that provides detailed insights into function and its disruption in various disorders. The data-driven analysis of fMRI activity maps involves several postprocessing steps, the first which identifying whether estimated network capture signals interest, for example, intrinsic connectivity networks (ICNs), or artifacts. This followed by linking ICNs to standardized anatomical functional parcellations. Optionally, as study (FNC), rearranging graph also necessary facilitate interpretation. Methods: Here we develop novel efficient method (Autolabeler) implementing integrating all these processes fully automated manner. Autolabeler pretrained on cross-validated elastic-net regularized general linear model from noisecloud toolbox separate neuroscientifically meaningful It capable automatically labeling with labels well-known Subsequently, this maximizes modularity within domains generate more systematically structured FNC matrix post hoc analyses. Results: Results show our achieves 86% accuracy at classifying artifacts an independent validation data set. automatic have high degree similarity manual selected human raters. Discussion: At time ever-increasing rates generating analyzing activity, proposed intended automate such analyses faster reproducible research. Impact statement Our some crucial tasks studies. incorporate without need expert intervention. We open-source can stand-alone software additionally seamless integration widely used group component (GIFT). aid investigators conduct studies end-to-end

Language: Английский

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Sequential bilateral accelerated theta burst stimulation in adolescents with suicidal ideation associated with major depressive disorder: Protocol for a randomized controlled trial

PLoS ONE, Journal Year: 2023, Volume and Issue: 18(4), P. e0280010 - e0280010

Published: April 13, 2023

Background Suicide is a leading cause of death in adolescents worldwide. Previous research findings suggest that suicidal with depression have pathophysiological dorsolateral prefrontal cortex (DLPFC) deficits γ-aminobutyric acid neurotransmission. Interventions transcranial magnetic stimulation (TMS) directly address these underlying the cortex. Theta burst (TBS) newer dosing approach for TMS. Accelerated TBS (aTBS) involves administering multiple sessions TMS daily as this may be more efficient, tolerable, and rapid acting than standard Materials methods This randomized, double-blind, sham-controlled trial sequential bilateral aTBS major depressive disorder (MDD) ideation. Three are administered 10 days. During each session, continuous first to right DPFC, which 1,800 pulses delivered continuously over 120 seconds. Then intermittent applied left 2-second bursts repeated every seconds 570 The parameters were adopted from prior research, 3-pulse, 50-Hz given 200 ms (at 5 Hz) an intensity 80% active motor threshold. comparison group will receive 3 sham treatment All participants care patients ideation including psychotherapeutic skill sessions. Long-interval intracortical inhibition (LICI) biomarkers measured before after treatment. Exploratory measures collected electroencephalography biomarker development. Discussion known randomized controlled examine efficacy treating MDD. Results study also provide opportunities further understand neurophysiological molecular mechanisms adolescents. Trial registration Investigational device exemption (IDE) Number: G200220, ClinicalTrials.gov (ID: NCT04701840 ). Registered August 6, 2020. https://clinicaltrials.gov/ct2/show/NCT04502758?term=NCT04701840&draw=2&rank=1 .

Language: Английский

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Probabilistic Models of Larval Zebrafish Behavior: Structure on Many Scales

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2019, Volume and Issue: unknown

Published: June 15, 2019

Abstract Nervous systems have evolved to combine environmental information with internal state select and generate adaptive behavioral sequences. To better understand these computations their implementation in neural circuits, natural behavior must be carefully measured quantified. Here, we collect high spatial resolution video of single zebrafish larvae swimming a naturalistic environment develop models action selection across exploration hunting. Zebrafish swim punctuated bouts separated by longer periods rest called interbout intervals. We take advantage this structure categorizing into discrete types representing as labeled sequences bout-types emitted over time. then construct probabilistic – specifically, marked renewal processes evaluate how intervals are selected the fish function its hunger state, history, locations properties nearby prey. Finally, predictive likelihood ability realistic trajectories virtual through simulated environments. Our simulations capture multiple timescales larval expose many ways which influences promote food seeking during safety satiety.

Language: Английский

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NeuroEthics and the BRAIN Initiative: Where Are We? Where Are We Going?

AJOB Neuroscience, Journal Year: 2020, Volume and Issue: 11(3), P. 140 - 147

Published: July 2, 2020

From its inception, the NIH Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative, an ambitious project focused on understanding human brain, has made a concerted effort to integrate neuroethics into science. In past five years, BRAIN Initiative given rise powerful tools and neurotechnologies capable of probing deeply brain circuits in animal models. As these mature move applications they will raise host important neuroethical considerations not just for medical community but society as whole. Now marks pivotal moment assess status consider future Initiative's efforts. Here we describe core issues neuroscience advances, state neuroscience, how ethics be incorporated this ten-year enters second phase. have immense potential transform way diagnose treat neurological disease; therefore, may become more commonplace research, medicine, society. We also discuss global efforts ensure continued guidance open dialogue surrounding neuroethics.

Language: Английский

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NS-Forest: A machine learning method for the objective identification of minimum marker gene combinations for cell type determination from single cell RNA sequencing

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2020, Volume and Issue: unknown

Published: Sept. 24, 2020

Abstract Single cell genomics is rapidly advancing our knowledge of phenotypic types and states. Driven by single cell/nucleus RNA sequencing (scRNA-seq) data, comprehensive atlas projects covering a wide range organisms tissues are currently underway. As result, it critical that the transcriptional phenotypes discovered defined disseminated in consistent concise manner. Molecular biomarkers have historically played an important role biological research, from defining immune cell-types surface protein expression to diseases molecular drivers. Here we describe machine learning-based marker gene selection algorithm, NS-Forest version 2.0, which leverages non-linear attributes random forest feature binary scoring approach discover minimal combinations precisely captures type identity represented complete scRNA-seq profiles. The genes selected provide barcode necessary sufficient characteristics for semantic definition serve as useful tools downstream investigation. use identify human brain middle temporal gyrus reveals importance signaling non-coding RNAs neuronal identity.

Language: Английский

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