An investigation of the effectiveness of neurofeedback training on motor performance in healthy adults: A systematic review and meta-analysis.

Neurofeedback training (NFT) refers to a training where the participants voluntarily aim to manipulate their own brain activity using the sensory feedback abstracted from their brain activity. NFT has attracted attention in the field of motor learning due to its potential as an alternative or additional training method for general physical training. In this study, a systematic review of NFT studies for motor performance improvements in healthy adults and a meta-analysis on the effectiveness of NFT were conducted. A computerized search was performed using the databases Web of Science, Scopus, PubMed, JDreamIII, and Ichushi-Web to identify relevant studies published between January 1st, 1990, and August 3rd, 2021. Thirty-three studies were identified for the qualitative synthesis and 16 randomized controlled trials (374 subjects) for the meta-analysis. The meta-analysis, including all trials found in the search, revealed significant effects of NFT for motor performance improvement examined at the timing after the last NFT session (standardized mean difference = 0.85, 95% CI [0.18-1.51]), but with the existence of publication biases and substantial heterogeneity among the trials. Subsequent meta-regression analysis demonstrated the dose-response gradient between NFTs and motor performance improvements; more than 125 min of cumulative training time may benefit for the subsequent motor performance. For each motor performance measure (e.g., speed, accuracy, and hand dexterity), the effectiveness of NFT remains inconclusive, mainly due to its small sample sizes. More empirical NFT studies for motor performance improvement may be needed to show beneficial effects on motor performance and to safely incorporate NFT into real-world scenarios.

A chemical genomics-aggrephagy integrated method studying functional analysis of autophagy inducers.

Macroautophagy/autophagy plays a critical role in the pathogenesis of various human diseases including neurodegenerative disorders such as Parkinson disease (PD) and Huntington disease (HD). Chemical autophagy inducers are expected to serve as disease-modifying agents by eliminating cytotoxic/damaged proteins. Although many autophagy inducers have been identified, their precise molecular mechanisms are not fully understood because of the complicated crosstalk among signaling pathways. To address this issue, we performed several chemical genomic analyses enabling us to comprehend the dominancy among the autophagy-associated pathways followed by an aggresome-clearance assay. In a first step, more than 400 target-established small molecules were assessed for their ability to activate autophagic flux in neuronal PC12D cells, and we identified 39 compounds as autophagy inducers. We then profiled the autophagy inducers by testing their effect on the induction of autophagy by 200 well-established signal transduction modulators. Our principal component analysis (PCA) and clustering analysis using a dataset of "autophagy profiles" revealed that two Food and Drug Administration (FDA)-approved drugs, memantine and clemastine, activate endoplasmic reticulum (ER) stress responses, which could lead to autophagy induction. We also confirmed that SMK-17, a recently identified autophagy inducer, induced autophagy via the PRKC/PKC-TFEB pathway, as had been predicted from PCA. Finally, we showed that almost all of the autophagy inducers tested in this present work significantly enhanced the clearance of the protein aggregates observed in cellular models of PD and HD. These results, with the combined approach, suggested that autophagy-activating small molecules may improve proteinopathies by eliminating nonfunctional protein aggregates.Abbreviations: ADK: adenosine kinase; AMPK: AMP-activated protein kinase; ATF4: activating transcription factor 4; BECN1: beclin-1; DDIT3/CHOP: DNA damage inducible transcript 3; EIF2AK3/PERK: eukaryotic translation initiation factor 2 alpha kinase 3; EIF2S1/eIF2α: eukaryotic translation initiation factor 2 subunit alpha; ER: endoplasmic reticulum; ERN1/IRE1α: endoplasmic reticulum to nucleus signaling 1; FDA: Food and Drug Administration; GSH: glutathione; HD: Huntington disease; HSPA5/GRP78: heat shock protein family A (Hsp70) member 5; HTT: huntingtin; JAK: Janus kinase, MAP1LC3B/LC3: microtubule associated protein 1 light chain 3 beta; MAP2K/MEK: mitogen-activated protein kinase kinase; MAP3K8/Tpl2: mitogen-activated protein kinase kinase kinase 8; MAPK: mitogen-activated protein kinase; MPP+: 1-methyl-4-phenylpyridinium; MTOR: mechanistic target of rapamycin kinase; MTORC: MTOR complex; NAC: N-acetylcysteine; NGF: nerve growth factor 2; NMDA: N-methyl-D-aspartate; PCA: principal component analysis; PD: Parkinson disease; PDA: pancreatic ductal adenocarcinoma; PIK3C3: phosphatidylinositol 3-kinase catalytic subunit type 3; PMA: phorbol 12-myristate 13-acetate; PRKC/PKC: protein kinase C; ROCK: Rho-associated coiled-coil protein kinase; RR: ribonucleotide reductase; SIGMAR1: sigma non-opioid intracellular receptor 1; SQSTM1/p62: sequestosome 1; STK11/LKB1: serine/threonine kinase 11; TFEB: Transcription factor EB; TGFB/TGF-ß: Transforming growth factor beta; ULK1: unc-51 like autophagy activating kinase 1; XBP1: X-box binding protein 1.