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The authors would like to thank Daniel Bullock, Satrajit Ghosh, Jason Tourville, Alfonso Nieto-Castanon, Julie Goodman, and Larry Wald for their assistance with this research. ... Figure 3 shows the main effect of overt production (GO>NOGO; P F W E < 0.05) 4 . ... Main effect of syllable complexity Figure 5 shows the main effect of syllable complexity (C_syl >S_syl ; P F W E < 0.05). ...doi:10.1016/j.neuroimage.2006.04.173 pmid:16730195 fatcat:ccded2mxejcnbfp4ckvtt2emt4
The state of each neuron in the system is updated in discrete time by the following rule: S G (t#1)"sgn , H$G w GH S H (t) , (3) where sgn( ) is the signum function and S H (t) is the output of neuron ... The network is trained with M randomly generated patterns of length N and the interconnection weights are given by w GH " 1 N + I I G I H c GH , (1) where c GH " 0 if +i, j, , G, 1 if +i, j,3G. (2) Initially ...doi:10.1016/s0925-2312(01)00378-2 fatcat:5mjk44cwrrbffe2f6so67rf5ua
., , 2006 , syllable sequences (Bohland and Guenther, 2006) , monosyllables (Ghosh et al., 2008) , and compensatory responses to shifts in auditory feedback , as well as covert singing production relative ...doi:10.1016/j.neuroimage.2010.12.065 pmid:21195191 pmcid:PMC3065208 fatcat:mfdg2vfqfbfrxeotiwtgacghie
that guides the present experiment is based on a combination 30 of the dual streams speech processing model Poeppel, 2004, 2007; Rauschecker 31 and Scott, 2009) and the GODIVA neurocomputational model (Bohland ... and Guenther, 2006; Bohland et al., 2010) . ... The authors would like to thank Jacqueline You, Jason Tourville, Frank Guenther, Mohammad Moghadamfalahi, Murat Akcakaya and Deniz 1 2 3 4 5 ...doi:10.1016/j.neuroimage.2016.07.023 pmid:27421186 fatcat:3mwwihxavfa3tjuug3ie3dlygy
Fatima Husain, Alfonso Nieto-Castanon, Satrajit Ghosh, and Jason ...doi:10.1250/ast.23.213 fatcat:koatzm4l3jerdhc3iyfhxsxpfy
Acknowledgments The authors thank Caizhi Wu, John Lin, Larry Swanson, Hans Breiter, and Jason Tourville for their help in establishing and/or carrying out this project, and especially Peter Andrews for ...doi:10.1371/journal.pone.0007200 pmid:19787067 pmcid:PMC2748707 fatcat:yp35ev7jnjd7tcqpm2pwwugrwa
Acknowledgments The authors thank Jason Tourville, Satrajit Ghosh, and Oren Civier for valuable comments. Support was provided by NIH R01 DC007683 and NIH R01 DC002852 (F. H. ... The inhibitory inputs are weighted by entries in the adjacency matrix W. In the simplest case (used in simulations here), entry W ik is 1 for i 6 ¼ k and 0 for i = k. ... This notion is based on three major findings from Bohland and Guenther (2006) , which are consistent with other reports in the literature. ...doi:10.1162/jocn.2009.21306 pmid:19583476 pmcid:PMC2937837 fatcat:frffa5oa7rcrzbq5podgidqshq
Brain and Language
The neural mechanisms that underlie generalization of treatment-induced improvements in word finding in persons with aphasia (PWA) are currently poorly understood. This study aimed to shed light on changes in functional network connectivity underlying generalization in aphasia. To this end, we used fMRI and graph theoretic analyses to examine changes in functional connectivity after a theoretically-based word-finding treatment in which abstract words were used as training items with the goal ofdoi:10.1016/j.bandl.2015.09.002 pmid:26398158 pmcid:PMC4663144 fatcat:2scvrmkvx5glndhmyl46qz6gdm
more »... promoting generalization to concrete words. Ten right-handed native English-speaking PWA (7 male, 3 female) ranging in age from 47 to 75 (mean = 59) participated in this study. Direct training effects coincided with increased functional connectivity for regions involved in abstract word processing. Generalization effects coincided with increased functional connectivity for regions involved in concrete word processing. Importantly, similarities between training and generalization effects were noted as were differences between participants who generalized and those who did not.
The unnormalized Laplacian matrix is defined as L = D − W where W is the weighted adjacency matrix (with affinity edge weights) and D is a diagonal matrix of weighted node degrees such that D ii = i W ... All networks were undirected (w ij = w ji ) and contained 116 nodes (corresponding to the regions of the AAL atlas) and 6,670 possible edge weights. ... AUTHOR CONTRIBUTIONS Jason W. Bohland and Leo Grady conceived the study and overall approach. Sara Saperstein, Leo Grady, and Jason W. Bohland conducted the initial data analyses. ...doi:10.3389/fnsys.2012.00078 pmid:23267318 pmcid:PMC3527894 fatcat:5upyyiuvvngltk4a5hmhuaxnmu
Although the Gradient Order DIVA model (Directions Into Velocities of Articulators; Bohland, Bullock, & Guenther, 2010) extends the DIVA speech motor control framework (Guenther et al., 2006) to address ... must sequentially select the appropriate phonological units from a planning buffer, while concurrently activating lower-level sensory-motor programs that drive the production of individual syllables (Bohland ...doi:10.1044/2017_jslhr-s-16-0234 pmid:28655038 pmcid:PMC5544401 fatcat:wrnzfozqgzh2xi5etmd5hqbxdy
S ¼ 1 À 4 X ij W ij X ij ð1 À X ij Þ The S index comparing K-means clustering results with the ARA was computed as a function of K, for both coarse-grained (consisting of 12 regions) and fine-grained ( ... to each cluster is computed: X ij ¼ maxðP ij ; P ji Þ along with ''weights" for each non-zero X ij, which are dependent on region/cluster volume: U ij ¼ minð r i j j; r j Þ if X ij > 0 0 otherwise ( W ...doi:10.1016/j.ymeth.2009.09.001 pmid:19733241 fatcat:dvpsspr4lbfepda7xbbgsaylee
Background-Numerous studies have examined gene × environment interactions (G×E) in cognitive and behavioral domains. However, these studies have been limited in that they have not been able to directly assess differential patterns of gene expression in the human brain. Here we assessed G×E interactions using two publically-available datasets to assess if DNA variation is associated with post-mortem brain gene expression changes based on smoking behavior, a biobehavioral construct that is partdoi:10.1111/jcpp.12119 pmid:23909413 pmcid:PMC3809890 fatcat:atsl3w7c3ff7nbynayn5bzmomu
more »... a complex system of genetic and environmental influences. Methods-We conducted an expression quantitative trait locus (eQTL) study on two independent human brain gene expression datasets assessing G×E for selected psychiatric genes and smoking status. We employed linear regression to model the significance of the Gene×Smoking interaction term, followed by meta-analysis across datasets. Results-Overall, we observed that the effect of DNA variation on gene expression is moderated by smoking status. Expression of 16 genes were significantly associated with single nucleotide polymorphisms that demonstrated G×E effects. The strongest finding (p = 1.9×10 −11 ) was neurexin 3-alpha (NRXN3), a synaptic cell-cell adhesion molecule involved in maintenance of neural connections (such as the maintenance of smoking behavior). Other significant G×E associations include four glutamate genes. Conclusions-This is one of the first studies to demonstrate G×E effects within the human brain. In particular, this study implicated NRXN3 in the maintenance of smoking. The effect of smoking on NRXN3 expression and downstream behavior is different based upon SNP genotype, indicating that DNA profiles based on SNPs could be useful in understanding the effects of smoking behaviors. These results suggest that better measurement of psychiatric conditions, and the environment in post-mortem brain studies may yield an important avenue for understanding the biological mechanisms of G×E interactions in psychiatry.
Acoustical Science and Technology
Learning sound categories : A neural model and supporting experiments
Learning sound categories : A neural model and supporting experiments
N 冫 「 Jason W ． Bohland Jason Bohland 氏 は ，ボ ス ト ン 大 学 認 知 神 経 シ ス テ ム 学 科 博 士 課 程 の 学 生 で あ る 。 ... Guenther ( Boston university ／ Massachusetts Institute of Techno 且 ogy ) ＊ ＊ ・ Jason W ． Bohland ( B ・ st ・ n uni ・ ersity ) ＊ ＊ ＊ ・ 全 訳 ： カ ラ ン 明 子 ( ATR 脳 活 動 イ メ ー ジ ン グ セ ン タ ) ・ 1〕aniel E ． ...doi:10.20697/jasj.58.7_441 fatcat:yzinqotlobcqllld46ykepalsi
triphosphate synthase 1185 51797 NM_016748 Ctsb cathepsin B 67779877 13030 NM_007798.1 Ctsl cathepsin L 1187 13039 NM_009984 Ctss cathepsin S 77371797 13040 NM_021281.1 Ctsw cathepsin W ...doi:10.1038/nn.2281 pmid:19219037 fatcat:zwzqd64ahvfqfds4eo2in7t3fy
1 χ(w)1(ρ t (w) > 0) = 1 W V w=1 W V |Supp(t)| V = ξ(t), (36) where ξ(t) is the fraction of voxels in the brain annotation supporting the estimated profile ρ t (see Eq. 22) which is one reason for which ... If one considers the ensemble of random subsets of [1..V ] (or equivalently random subsets of the voxels in the ARA), of fixed size W , denoted by ν as follows: ν v = ±1, v ∈ [1..V ], V v=1 ν v = W, (34 ...arXiv:1402.2820v3 fatcat:ofdghfhbgzahbdzyt4unhesroy
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