Search

Your search keyword '"Suckling, J."' showing total 733 results
Start Over Author "Suckling, J."
733 results on '"Suckling, J."'

1. Statistical Agnostic Regression: a machine learning method to validate regression models

Author

Gorriz, Juan M, Ramirez, J., Segovia, F., Martinez-Murcia, F. J., Jiménez-Mesa, C., and Suckling, J.

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning, Mathematics - Statistics Theory, Statistics - Computation
Abstract

Regression analysis is a central topic in statistical modeling, aiming to estimate the relationships between a dependent variable, commonly referred to as the response variable, and one or more independent variables, i.e., explanatory variables. Linear regression is by far the most popular method for performing this task in several fields of research, such as prediction, forecasting, or causal inference. Beyond various classical methods to solve linear regression problems, such as Ordinary Least Squares, Ridge, or Lasso regressions - which are often the foundation for more advanced machine learning (ML) techniques - the latter have been successfully applied in this scenario without a formal definition of statistical significance. At most, permutation or classical analyses based on empirical measures (e.g., residuals or accuracy) have been conducted to reflect the greater ability of ML estimations for detection. In this paper, we introduce a method, named Statistical Agnostic Regression (SAR), for evaluating the statistical significance of an ML-based linear regression based on concentration inequalities of the actual risk using the analysis of the worst case. To achieve this goal, similar to the classification problem, we define a threshold to establish that there is sufficient evidence with a probability of at least 1-eta to conclude that there is a linear relationship in the population between the explanatory (feature) and the response (label) variables. Simulations in only two dimensions demonstrate the ability of the proposed agnostic test to provide a similar analysis of variance given by the classical $F$ test for the slope parameter., Comment: 17 pages, 15 figures

Published
2024

2. Is K-fold cross validation the best model selection method for Machine Learning?

Author

Gorriz, Juan M, Segovia, F, Ramirez, J, Ortiz, A, and Suckling, J.

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, Electrical Engineering and Systems Science - Signal Processing
Abstract

As a technique that can compactly represent complex patterns, machine learning has significant potential for predictive inference. K-fold cross-validation (CV) is the most common approach to ascertaining the likelihood that a machine learning outcome is generated by chance and frequently outperforms conventional hypothesis testing. This improvement uses measures directly obtained from machine learning classifications, such as accuracy, that do not have a parametric description. To approach a frequentist analysis within machine learning pipelines, a permutation test or simple statistics from data partitions (i.e. folds) can be added to estimate confidence intervals. Unfortunately, neither parametric nor non-parametric tests solve the inherent problems around partitioning small sample-size datasets and learning from heterogeneous data sources. The fact that machine learning strongly depends on the learning parameters and the distribution of data across folds recapitulates familiar difficulties around excess false positives and replication. The origins of this problem are demonstrated by simulating common experimental circumstances, including small sample sizes, low numbers of predictors, and heterogeneous data sources. A novel statistical test based on K-fold CV and the Upper Bound of the actual error (K-fold CUBV) is composed, where uncertain predictions of machine learning with CV are bounded by the \emph{worst case} through the evaluation of concentration inequalities. Probably Approximately Correct-Bayesian upper bounds for linear classifiers in combination with K-fold CV is used to estimate the empirical error. The performance with neuroimaging datasets suggests this is a robust criterion for detecting effects, validating accuracy values obtained from machine learning whilst avoiding excess false positives., Comment: 36 pages, 24 figures

Published
2024

4. A hypothesis-driven method based on machine learning for neuroimaging data analysis

Author

Gorriz, JM, Martin-Clemente, R., Puntonet, C. G., Ortiz, A., Ramirez, J., and Suckling, J.

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

There remains an open question about the usefulness and the interpretation of Machine learning (MLE) approaches for discrimination of spatial patterns of brain images between samples or activation states. In the last few decades, these approaches have limited their operation to feature extraction and linear classification tasks for between-group inference. In this context, statistical inference is assessed by randomly permuting image labels or by the use of random effect models that consider between-subject variability. These multivariate MLE-based statistical pipelines, whilst potentially more effective for detecting activations than hypotheses-driven methods, have lost their mathematical elegance, ease of interpretation, and spatial localization of the ubiquitous General linear Model (GLM). Recently, the estimation of the conventional GLM has been demonstrated to be connected to an univariate classification task when the design matrix is expressed as a binary indicator matrix. In this paper we explore the complete connection between the univariate GLM and MLE \emph{regressions}. To this purpose we derive a refined statistical test with the GLM based on the parameters obtained by a linear Support Vector Regression (SVR) in the \emph{inverse} problem (SVR-iGLM). Subsequently, random field theory (RFT) is employed for assessing statistical significance following a conventional GLM benchmark. Experimental results demonstrate how parameter estimations derived from each model (mainly GLM and SVR) result in different experimental design estimates that are significantly related to the predefined functional task. Moreover, using real data from a multisite initiative the proposed MLE-based inference demonstrates statistical power and the control of false positives, outperforming the regular GLM., Comment: 12 figures

Published
2022
Full Text
View/download PDF

6. Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends

Author

Górriz, J.M., Álvarez-Illán, I., Álvarez-Marquina, A., Arco, J.E., Atzmueller, M., Ballarini, F., Barakova, E., Bologna, G., Bonomini, P., Castellanos-Dominguez, G., Castillo-Barnes, D., Cho, S.B., Contreras, R., Cuadra, J.M., Domínguez, E., Domínguez-Mateos, F., Duro, R.J., Elizondo, D., Fernández-Caballero, A., Fernandez-Jover, E., Formoso, M.A., Gallego-Molina, N.J., Gamazo, J., González, J. García, Garcia-Rodriguez, J., Garre, C., Garrigós, J., Gómez-Rodellar, A., Gómez-Vilda, P., Graña, M., Guerrero-Rodriguez, B., Hendrikse, S.C.F., Jimenez-Mesa, C., Jodra-Chuan, M., Julian, V., Kotz, G., Kutt, K., Leming, M., de Lope, J., Macas, B., Marrero-Aguiar, V., Martinez, J.J., Martinez-Murcia, F.J., Martínez-Tomás, R., Mekyska, J., Nalepa, G.J., Novais, P., Orellana, D., Ortiz, A., Palacios-Alonso, D., Palma, J., Pereira, A., Pinacho-Davidson, P., Pinninghoff, M.A., Ponticorvo, M., Psarrou, A., Ramírez, J., Rincón, M., Rodellar-Biarge, V., Rodríguez-Rodríguez, I., Roelofsma, P.H.M.P., Santos, J., Salas-Gonzalez, D., Salcedo-Lagos, P., Segovia, F., Shoeibi, A., Silva, M., Simic, D., Suckling, J., Treur, J., Tsanas, A., Varela, R., Wang, S.H., Wang, W., Zhang, Y.D., Zhu, H., Zhu, Z., and Ferrández-Vicente, J.M.

Published
2023
Full Text
View/download PDF

9. Brain charts for the human lifespan

Author

Bethlehem, R. A. I., Seidlitz, J., White, S. R., Vogel, J. W., Anderson, K. M., Adamson, C., Adler, S., Alexopoulos, G. S., Anagnostou, E., Areces-Gonzalez, A., Astle, D. E., Auyeung, B., Ayub, M., Bae, J., Ball, G., Baron-Cohen, S., Beare, R., Bedford, S. A., Benegal, V., Beyer, F., Blangero, J., Blesa Cábez, M., Boardman, J. P., Borzage, M., Bosch-Bayard, J. F., Bourke, N., Calhoun, V. D., Chakravarty, M. M., Chen, C., Chertavian, C., Chetelat, G., Chong, Y. S., Cole, J. H., Corvin, A., Costantino, M., Courchesne, E., Crivello, F., Cropley, V. L., Crosbie, J., Crossley, N., Delarue, M., Delorme, R., Desrivieres, S., Devenyi, G. A., Di Biase, M. A., Dolan, R., Donald, K. A., Donohoe, G., Dunlop, K., Edwards, A. D., Elison, J. T., Ellis, C. T., Elman, J. A., Eyler, L., Fair, D. A., Feczko, E., Fletcher, P. C., Fonagy, P., Franz, C. E., Galan-Garcia, L., Gholipour, A., Giedd, J., Gilmore, J. H., Glahn, D. C., Goodyer, I. M., Grant, P. E., Groenewold, N. A., Gunning, F. M., Gur, R. E., Gur, R. C., Hammill, C. F., Hansson, O., Hedden, T., Heinz, A., Henson, R. N., Heuer, K., Hoare, J., Holla, B., Holmes, A. J., Holt, R., Huang, H., Im, K., Ipser, J., Jack, Jr, C. R., Jackowski, A. P., Jia, T., Johnson, K. A., Jones, P. B., Jones, D. T., Kahn, R. S., Karlsson, H., Karlsson, L., Kawashima, R., Kelley, E. A., Kern, S., Kim, K. W., Kitzbichler, M. G., Kremen, W. S., Lalonde, F., Landeau, B., Lee, S., Lerch, J., Lewis, J. D., Li, J., Liao, W., Liston, C., Lombardo, M. V., Lv, J., Lynch, C., Mallard, T. T., Marcelis, M., Markello, R. D., Mathias, S. R., Mazoyer, B., McGuire, P., Meaney, M. J., Mechelli, A., Medic, N., Misic, B., Morgan, S. E., Mothersill, D., Nigg, J., Ong, M. Q. W., Ortinau, C., Ossenkoppele, R., Ouyang, M., Palaniyappan, L., Paly, L., Pan, P. M., Pantelis, C., Park, M. M., Paus, T., Pausova, Z., Paz-Linares, D., Pichet Binette, A., Pierce, K., Qian, X., Qiu, J., Qiu, A., Raznahan, A., Rittman, T., Rodrigue, A., Rollins, C. K., Romero-Garcia, R., Ronan, L., Rosenberg, M. D., Rowitch, D. H., Salum, G. A., Satterthwaite, T. D., Schaare, H. L., Schachar, R. J., Schultz, A. P., Schumann, G., Schöll, M., Sharp, D., Shinohara, R. T., Skoog, I., Smyser, C. D., Sperling, R. A., Stein, D. J., Stolicyn, A., Suckling, J., Sullivan, G., Taki, Y., Thyreau, B., Toro, R., Traut, N., Tsvetanov, K. A., Turk-Browne, N. B., Tuulari, J. J., Tzourio, C., Vachon-Presseau, É., Valdes-Sosa, M. J., Valdes-Sosa, P. A., Valk, S. L., van Amelsvoort, T., Vandekar, S. N., Vasung, L., Victoria, L. W., Villeneuve, S., Villringer, A., Vértes, P. E., Wagstyl, K., Wang, Y. S., Warfield, S. K., Warrier, V., Westman, E., Westwater, M. L., Whalley, H. C., Witte, A. V., Yang, N., Yeo, B., Yun, H., Zalesky, A., Zar, H. J., Zettergren, A., Zhou, J. H., Ziauddeen, H., Zugman, A., Zuo, X. N., Bullmore, E. T., and Alexander-Bloch, A. F.

Published
2022
Full Text
View/download PDF

11. Publisher Correction: Brain charts for the human lifespan

Author

Bethlehem, R. A. I., Seidlitz, J., White, S. R., Vogel, J. W., Anderson, K. M., Adamson, C., Adler, S., Alexopoulos, G. S., Anagnostou, E., Areces-Gonzalez, A., Astle, D. E., Auyeung, B., Ayub, M., Bae, J., Ball, G., Baron-Cohen, S., Beare, R., Bedford, S. A., Benegal, V., Beyer, F., Blangero, J., Blesa Cábez, M., Boardman, J. P., Borzage, M., Bosch-Bayard, J. F., Bourke, N., Calhoun, V. D., Chakravarty, M. M., Chen, C., Chertavian, C., Chetelat, G., Chong, Y. S., Cole, J. H., Corvin, A., Costantino, M., Courchesne, E., Crivello, F., Cropley, V. L., Crosbie, J., Crossley, N., Delarue, M., Delorme, R., Desrivieres, S., Devenyi, G. A., Di Biase, M. A., Dolan, R., Donald, K. A., Donohoe, G., Dunlop, K., Edwards, A. D., Elison, J. T., Ellis, C. T., Elman, J. A., Eyler, L., Fair, D. A., Feczko, E., Fletcher, P. C., Fonagy, P., Franz, C. E., Galan-Garcia, L., Gholipour, A., Giedd, J., Gilmore, J. H., Glahn, D. C., Goodyer, I. M., Grant, P. E., Groenewold, N. A., Gunning, F. M., Gur, R. E., Gur, R. C., Hammill, C. F., Hansson, O., Hedden, T., Heinz, A., Henson, R. N., Heuer, K., Hoare, J., Holla, B., Holmes, A. J., Holt, R., Huang, H., Im, K., Ipser, J., Jack, Jr, C. R., Jackowski, A. P., Jia, T., Johnson, K. A., Jones, P. B., Jones, D. T., Kahn, R. S., Karlsson, H., Karlsson, L., Kawashima, R., Kelley, E. A., Kern, S., Kim, K. W., Kitzbichler, M. G., Kremen, W. S., Lalonde, F., Landeau, B., Lee, S., Lerch, J., Lewis, J. D., Li, J., Liao, W., Liston, C., Lombardo, M. V., Lv, J., Lynch, C., Mallard, T. T., Marcelis, M., Markello, R. D., Mathias, S. R., Mazoyer, B., McGuire, P., Meaney, M. J., Mechelli, A., Medic, N., Misic, B., Morgan, S. E., Mothersill, D., Nigg, J., Ong, M. Q. W., Ortinau, C., Ossenkoppele, R., Ouyang, M., Palaniyappan, L., Paly, L., Pan, P. M., Pantelis, C., Park, M. M., Paus, T., Pausova, Z., Paz-Linares, D., Pichet Binette, A., Pierce, K., Qian, X., Qiu, J., Qiu, A., Raznahan, A., Rittman, T., Rodrigue, A., Rollins, C. K., Romero-Garcia, R., Ronan, L., Rosenberg, M. D., Rowitch, D. H., Salum, G. A., Satterthwaite, T. D., Schaare, H. L., Schachar, R. J., Schultz, A. P., Schumann, G., Schöll, M., Sharp, D., Shinohara, R. T., Skoog, I., Smyser, C. D., Sperling, R. A., Stein, D. J., Stolicyn, A., Suckling, J., Sullivan, G., Taki, Y., Thyreau, B., Toro, R., Traut, N., Tsvetanov, K. A., Turk-Browne, N. B., Tuulari, J. J., Tzourio, C., Vachon-Presseau, É., Valdes-Sosa, M. J., Valdes-Sosa, P. A., Valk, S. L., van Amelsvoort, T., Vandekar, S. N., Vasung, L., Victoria, L. W., Villeneuve, S., Villringer, A., Vértes, P. E., Wagstyl, K., Wang, Y. S., Warfield, S. K., Warrier, V., Westman, E., Westwater, M. L., Whalley, H. C., Witte, A. V., Yang, N., Yeo, B., Yun, H., Zalesky, A., Zar, H. J., Zettergren, A., Zhou, J. H., Ziauddeen, H., Zugman, A., Zuo, X. N., Bullmore, E. T., and Alexander-Bloch, A. F.

Published
2022
Full Text
View/download PDF

16. Computational Approaches to Explainable Artificial Intelligence: Advances Intheory, Applications and Trends

Author

Górriz, Juan Manuel, primary, Álvarez-Illán, I., additional, Álvarez-Marquina, A., additional, Arcoa, J.E., additional, Atzmueller, M., additional, Ballarini, F., additional, Barakova, E., additional, Bologna, G., additional, Bonomini, P., additional, Castellanos-Dominguez, G., additional, Castillo-Barnes, D., additional, Cho, S.B., additional, Contreras, R., additional, Cuadra, J.M., additional, Domínguez, E., additional, Domínguez-Mateos, F., additional, Duro, R.J., additional, Elizondo, D., additional, Fernández-Caballero, A., additional, Fernandez-Jover, E., additional, Formoso, M.A., additional, Gallego-Molina, N.J., additional, Gamazo, J., additional, González, J. García, additional, Garcia-Rodriguez, J., additional, Garre, C., additional, Garrigós, J., additional, Gómez-Rodellar, A., additional, Gómez-Vilda, P., additional, Graña, M., additional, Guerrero-Rodriguez, B., additional, Hendrikse, S. C. F., additional, Jimenez-Mesa, C., additional, Jodra-Chuan, M., additional, Julian, V., additional, Kotz, G., additional, Kutt, K., additional, Leming, M., additional, Lope, J. de, additional, Macas, B., additional, Marrero-Aguiar, V., additional, Martinez, J.J., additional, Martinez-Murcia, F.J., additional, Martínez-Tomás, R., additional, Mekyska, J., additional, Nalepa, G.J., additional, Novais, P., additional, Orellana, D., additional, Ortiz, A., additional, Palacios-Alonso, D., additional, Palma, J., additional, Pereira, A., additional, Pinacho-Davidson, P., additional, Pinninghof, M.A., additional, Ponticorvo, M., additional, Psarrou, A., additional, Ramírez, J., additional, Rincón, M., additional, Rodellar-Biarge, V., additional, Rodríguez-Rodríguez, I., additional, Roelofsma, P. H.M.P., additional, Santos, J., additional, Salas-Gonzalez, D., additional, Salcedo-Lagos, P., additional, Segovia, F., additional, Shoeibi, A., additional, Silva, M., additional, Simic, D., additional, Suckling, J., additional, Treur, J., additional, Tsanas, Athanasios, additional, Varela, R., additional, Wang, SH., additional, Wang, W., additional, Zhang, YD., additional, Zhu, H., additional, Zhu, Z., additional, and Ferrández-Vicente, J.M., additional

Published
2023
Full Text
View/download PDF

18. Alterations in white matter microstructure in alcohol and alcohol-polydrug dependence: Associations with lifetime alcohol and nicotine exposure

Author

Agunbiade, K, Fonville, L, McGonigle, J, Elliott, R, Ersche, KD, Flechais, R, Orban, C, Murphy, A, Smith, DG, Suckling, J, Taylor, EM, Deakin, B, Robbins, TW, Nutt, DJ, Lingford-Hughes, AR, Paterson, LM, Nutt, D, Lingford-Hughes, A, Paterson, L, Taylor, E, Ersche, K, Smith, D, Reed, L, Passetti, F, Faravelli, L, Erritzoe, D, Mick, I, Kalk, N, Waldman, A, Nestor, L, Kuchibatla, S, Boyapati, V, Metastasio, A, Faluyi, Y, Fernandez-Egea, E, Abbott, S, Sahakian, B, Voon, V, Rabiner, I, Orban, C [0000-0001-9133-3561], Paterson, LM [0000-0001-9137-4419], and Apollo - University of Cambridge Repository

Subjects
neuroimaging, polydrug dependence, DTI, alcohol dependence, addiction, TBSS
Abstract

Funder: GlaxoSmithKline; Id: http://dx.doi.org/10.13039/100004330, Evidence suggests that alcohol dependence (AD) is associated with microstructural deficits in white matter, but the relationship with lifetime alcohol exposure and the impact of polydrug dependence is not well understood. Using diffusion tensor magnetic resonance (MR) imaging, we examined white matter microstructure in relation to alcohol and polydrug dependence using data from the Imperial College Cambridge Manchester (ICCAM) platform study. Tract‐based spatial statistics were used to examine fractional anisotropy (FA) in a cohort of abstinent AD participants, most of whom had a lifetime history of dependence to nicotine. A further subgroup also had a lifetime history of dependence to cocaine and/or opiates. Individuals with AD had lower FA throughout the corpus callosum, and negative associations with alcohol and nicotine exposure were found. A group‐by‐age interaction effect was found showing greater reductions with age in the alcohol‐dependent group within corpus callosum, overlapping with the group difference. We found no evidence of recovery with abstinence. A comparison of alcohol‐only‐ and alcohol‐polydrug‐dependent groups found no differences in FA. Overall, our findings show that AD is associated with lower FA and suggest that these alterations are primarily driven by lifetime alcohol consumption and cigarette smoking, showing no relationship with exposure to other substances such as cocaine, opiates or cannabis. Reductions in FA across the adult lifespan are more pronounced in AD and offer further support for the notion of accelerated ageing in relation to alcohol dependence. These findings highlight there may be lasting structural differences in white matter in alcohol dependence, despite continued abstinence.

Published
2022

21. Relationship Between Cortical Gyrification, White Matter Connectivity, and Autism Spectrum Disorder

Author

Ecker, C., Andrews, D., DellʼAcqua, F., Daly, E., Murphy, C., Catani, M., Thiebaut de Schotten, M., Baron-Cohen, S., Lai, M.C., Lombardo, M.V., Bullmore, E.T., Suckling, J., Williams, S., Jones, D.K., Chiocchetti, A., Murphy, D.G.M., Bailey, Anthony J., Baron-Cohen, Simon, Bolton, Patrick F., Bullmore, Edward T., Carrington, Sarah, Catani, Marco, Chakrabarti, Bhismadev, Craig, Michael C., Daly, Eileen M., Deoni, Sean C.L., Ecker, Christine, Happé, Francesca, Henty, Julian, Jezzard, Peter, Johnston, Patrick, Jones, Derek K., Lai, Meng-Chuan, Lombardo, Michael V., Madden, Anya, Mullins, Diane, Murphy, Clodagh M., Murphy, Declan G.M., Pasco, Greg, Ruigrok, Amber N.V., Sadek, Susan A., Spain, Debbie, Stewart, Rose, Suckling, John, Wheelwright, Sally J., Williams, Steven C., and Wilson, C.Ellie

Published
2016
Full Text
View/download PDF

25. Chronic alcohol exposure differentially modulates structural and functional properties of amygdala: A cross-sectional study

Author

Orban, C, McGonigle, J, Flechais, RSA, Paterson, LM, Elliott, R, Erritzoe, D, Ersche, KD, Murphy, A, Nestor, LJ, Passetti, F, Reed, LJ, Ribeiro, AS, Smith, DG, Suckling, J, Taylor, EM, Waldman, AD, Wing, VC, Deakin, JFW, Robbins, TW, Nutt, DJ, Lingford-Hughes, AR, Nutt, D, Lingford-Hughes, A, Paterson, L, Flechais, R, Deakin, B, Taylor, E, Robbins, T, Ersche, K, Smith, D, Reed, L, Faravelli, L, Mick, I, Kalk, N, Waldman, A, Nestor, L, Kuchibatla, S, Boyapati, V, Metastasio, A, Faluyi, Y, Fernandez-Egea, E, Abbott, S, Sahakian, B, Voon, V, Rabiner, I, Ersche, Karen [0000-0002-3203-1878], Robbins, Trevor [0000-0003-0642-5977], Suckling, John [0000-0002-5098-1527], Sahakian, Barbara [0000-0001-7352-1745], Voon, Valerie [0000-0001-6790-1776], and Apollo - University of Cambridge Repository

Subjects
nervous system, alcohol, fMRI, amygdala, psychological phenomena and processes
Abstract

© 2020 The Authors. Addiction Biology published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction Animal models have shown that chronic alcohol exposure is associated with persistent neuroadaptations in amygdala synaptic function, whereas human studies have consistently reported amygdala grey-matter volume (GMV) reductions in alcohol dependent patients (ADP). We hypothesised that chronic alcohol use associated with neuroadaptations may entail a reconfiguration of the amygdala's functional interactions and that these mechanisms may be affected by structural atrophy. We compared amygdala resting state functional connectivity (RSFC) using a whole brain seed-based approach and amygdala GMV in abstinent ADP (n = 20) and healthy controls (HC; n = 39), balanced for age, gender and levels of head motion. The potential moderating influence of age, cumulative alcohol exposure, abstinence length and head motion was further examined in the two groups separately using correlational analyses. We found increased amygdala RSFC with substantia nigra/ventral tegmental area (SN/VTA) in ADP compared with HC. As expected, amygdala GMV was lower in ADP. Multiple regression analyses of the ADP group showed that amygdala-SN/VTA RSFC increases were primarily associated with cumulative alcohol exposure rather than age, whereas amygdala GMV reductions were primarily associated with age rather than cumulative alcohol exposure. The same association between age and amygdala GMV was not observed amongst HC. Importantly, amygdala GMV and amygdala-SN/VTA RSFC were uncorrelated in ADP, and neither measure was correlated with abstinence length. These results suggest that chronic alcohol exposure is associated with persistent elevations in amygdala-SN/VTA RSFC and accelerated age-related grey-matter atrophy through potentially distinct mechanisms.

Published
2021

28. Sensory perception in autism: an fMRI ALE meta-analysis

Author

Baron-Cohen S, Suckling J, and Jassim N

Subjects
bepress|Life Sciences|Research Methods in Life Sciences, business.industry, media_common.quotation_subject, PsyArXiv|Meta-science, medicine.disease, behavioral disciplines and activities, bepress|Life Sciences|Neuroscience and Neurobiology, PsyArXiv|Neuroscience|Cognitive Neuroscience, Text mining, PsyArXiv|Neuroscience, PsyArXiv|Psychiatry, Perception, Meta-analysis, medicine, bepress|Medicine and Health Sciences|Medical Specialties|Psychiatry, Autism, bepress|Life Sciences|Neuroscience and Neurobiology|Cognitive Neuroscience, business, Psychology, psychological phenomena and processes, Cognitive psychology, media_common
Abstract

Sensory sensitivities occur in up to 90% of autistic individuals. With the recent inclusion of sensory symptoms in the diagnostic criteria for autism, there is a current need to develop neural hypotheses related to autistic sensory perception. Using activation likelihood estimation (ALE), we meta-analysed 52 task-based fMRI studies investigating differences between autistic (n=891) and control (n=967) participants during non-social sensory perception. During complex perception, autistic groups showed more activity in the secondary somatosensory and occipital cortices, insula, caudate, superior temporal gyrus, and inferior parietal lobule, while control groups showed more activity in the frontal and parietal regions. During basic sensory processing, autistic groups showed hyperactivity in the lateral occipital cortex, primary somatosensory and motor cortices, insula, caudate, and thalamus, while controls showed heightened activity in the precentral gyrus, middle frontal gyrus, precuneus, and anterior cingulate cortex. We conclude that autistic individuals, on average, show distinct engagement of sensory-related brain networks during sensory perception. These findings may help guide future research to focus on relevant neurobiological mechanisms underpinning the autistic experience.

Published
2020

30. Dopaminergic modulation of reinforcement learning in stimulant drug addiction

Author

Lim, TV, Cardinal, RN, Meng, C, Murray, GK, Craig, KJ, Abbott, S, Shabbir, SS, Suckling, J, Sahakian, BJ, Bullmore, ET, Robbins, TW, and Ersche, KD

Subjects
nervous system, 52 Psychology, 5202 Biological Psychology, 3209 Neurosciences, 32 Biomedical and Clinical Sciences
Abstract

Background: Chronic stimulant use has been associated with disruptions in fronto-striatal systems implicated in as- sociative learning [1]. Experimental studies have also shown that individuals with stimulant drug addiction experience difficulties in selecting the appropriate action following feedback [2]. However, the precise impairments in feedback learning in stimulant-addicted individuals are still unclear. A possible explanation might lie in an abnormal prediction error mechanism, as stimulant drugs directly target striatal dopaminergic neurons [3].

Published
2020
Full Text
View/download PDF

31. The relationship between reward processing and impulsivity in addiction: a functional magnetic resonance imaging study

Author

Hayes, A, Wing, V, McGonigle, J, Turton, S, Elliot, R, Ersche, KD, Flechais, R, Orban, C, Murphy, A, Smith, DG, Suckling, J, Taylor, EM, Deakin, JF, Robbins, TW, Nutt, DJ, Lingford-Hughes, AR, Paterson, LM, and Medical Research Council (MRC)

Subjects
Psychiatry, Science & Technology, Clinical Neurology, Neurosciences, Pharmacology & Pharmacy, Neurosciences & Neurology, Life Sciences & Biomedicine, psychological phenomena and processes, 11 Medical and Health Sciences, 17 Psychology and Cognitive Sciences
Abstract

Introduction: Evidence suggests that abnormalities in reward processing and increased impulsivity contribute to the pathophysiology of addiction. However, the relationship between the two is currently not well characterised. This study used fMRI to investigate the BOLD response during reward and inhibitory control tasks and how such responses were associated with subjective and behavioural measures of impulsivity in abstinent alcohol, cocaine and polydrug addiction. We hypothesized a negative correlation between non-drug related reward anticipation and impulsivity measures in polydrug and alcohol dependence compared with healthy controls owing to increased impulsivity and thus more inhibitory control effort needed to maintain successful abstinence. Methods: Abstinent alcohol dependent (AD, n=27), polydrug dependent (PD, n=57) and healthy control (HC, n=65) participants were recruited [1] (REC number 11/H0707/9). Participants completed a battery of impulsivity measures; the Barratt Impulsiveness Scale (BIS-11) and the Urgency, Premeditation, (lack of), Perseverance (lack of), Sensation Seeking, Positive Urgency, Impulsive Behaviour Scale (UPPS-P) to measure trait impulsivity, the Kirby Delay Discounting task to measure choice impulsivity and the Stop Signal Task (SST) to measure impulsive action. Participants also underwent fMRI scanning (3-T) using the monetary incentive delay (MID) and Go/No-go (GNG) tasks. An a-priori region of interest approach was used to image BOLD response during the following contrasts: reward anticipation>neutral anticipation and no-go/go in the rIFG, OFC and caudate. Data was analysed using ANOVA or Kruskal- Wallis tests (with Tukey or Mann-Whitney U post-hoc tests respectively) and Pearson's or Spearman's rank correlations, as appropriate. Holm-Bonferroni correction was applied. Results: There were significant group differences in the BIS-11 (pneutral anticipation and no-go>go contrasts. In the overall population, significant but weak positive correlations were observed between BOLD response for the reward anticipation>neutral anticipation and no-go>go contrast in the rIFG (r=0.208, p=0.001), OFC (r=0.219, p=0.008) and caudate (r=0.208, p=0.012). Interestingly, while these associations were observed in the HC group no such associations existed in the AD and PD groups. Finally, no significant associations were observed between subjective impulsivity measures and BOLD signal change during either task. Conclusion: The overall findings of this study suggest that there is a relationship between BOLD response during reward processing and inhibitory control. However, in contrast to our hypothesis this association was positive and was not observed in AD or PD individuals, suggesting that such an association is dysregulated in addiction. Further work is required to better understand the association between BOLD response during reward processing and impulse control. This study was funded by the MRC (G1000018) with support from GSK.

Published
2020

32. Granger causality-based information fusion applied to electrical measurements from power transformers

Author

Rodriguez-Rivero, J, Ramirez, J, Martínez-Murcia, FJ, Segovia, F, Ortiz, A, Salas, D, Castillo-Barnes, D, Illan, IA, Puntonet, CG, Jimenez-Mesa, C, Leiva, FJ, Carillo, S, Suckling, J, Gorriz, JM, Suckling, John [0000-0002-5098-1527], and Apollo - University of Cambridge Repository

Subjects
Functional connectivity, SCADA measurements, Granger causality, Time series analysis, Power transformers
Published
2020

33. Global Effects of Focal Brain Tumors on Functional Complexity and Network Robustness: A Prospective Cohort Study

Author

Hart, MG, Romero-Garcia, R, Price, SJ, Suckling, J, Hart, Michael [0000-0002-1789-2039], Price, Stephen [0000-0002-7535-3009], Suckling, John [0000-0002-5098-1527], and Apollo - University of Cambridge Repository

Subjects
Adult, Male, Brain Mapping, Brain Neoplasms, Neurooncology, Brain, Middle Aged, Magnetic Resonance Imaging, Research—Human—Clinical Studies, Fractals, Connectome, Humans, Female, Prospective Studies, Nerve Net, Fractal, Glioblastoma, Functional MRI, Aged
Abstract

BACKGROUND Neurosurgical management of brain tumors has entered a paradigm of supramarginal resections that demands thorough understanding of peritumoral functional effects. Historically, the effects of tumors have been believed to be local, and long-range effects have not been considered. OBJECTIVE To test the hypothesis that tumors affect the brain globally, producing long-range gradients in cortical function. METHODS Resting-state functional magnetic resonance imaging (fMRI) data were acquired from 11 participants with glioblastoma and split into discovery and validation datasets in a single-center prospective cohort study. Fractal complexity was computed with a wavelet-based estimator of the Hurst exponent. Distance-related effects of the tumors were tested with a tumor mask-dilation technique and parcellation of the underlying Hurst maps. RESULTS Fractal complexity demonstrates a penumbra of suppression in the peritumoral region. At a global level, as distance from the tumor increases, this initial suppression is balanced by a subsequent overactivity before finally normalizing. These effects were best fit by a quadratic model and were consistent across different network construction pipelines. The Hurst exponent was correlated with graph theory measures of centrality including network robustness, but graph theory measures did not demonstrate distance-dependent effects. CONCLUSION This work provides evidence supporting the theory that focal brain tumors produce long-range gradients in function. Consequently, the effects of focal lesions need to be interpreted in terms of the global changes on functional complexity and network architecture rather than purely in terms of functional localization. Determining whether peritumoral changes represent potential plasticity may facilitate extended resection of tumors without functional cost.

Published
2018

39. P.331 The relationship between reward processing and impulsivity in addiction: a functional magnetic resonance imaging study

Author

Hayes, A., primary, Wing, V., additional, McGonigle, J., additional, Turton, S., additional, Elliot, R., additional, Ersche, K.D., additional, Flechais, R., additional, Orban, C., additional, Murphy, A., additional, Smith, D.G., additional, Suckling, J., additional, Taylor, E.M., additional, Deakin, J.F., additional, Robbins, T.W., additional, Nutt, D.J., additional, Lingford-Hughes, A.R., additional, and Paterson, L.M., additional

Published
2020
Full Text
View/download PDF

45. Advanced magnetic resonance imaging and neuropsychological assessment for detecting brain injury in a prospective cohort of university amateur boxers

Author

Hart, M.G., Housden, C.R., Suckling, J., Tait, R., Young, A., Müller, U., Newcombe, V.F.J., Jalloh, I., Pearson, B., Cross, J., Trivedi, R.A., Pickard, J.D., Sahakian, B.J., Hutchinson, P.J., Hart, Michael [0000-0002-1789-2039], Suckling, John [0000-0002-5098-1527], Newcombe, Virginia [0000-0001-6044-9035], Pickard, John [0000-0002-5762-6667], Sahakian, Barbara [0000-0001-7352-1745], Hutchinson, Peter [0000-0002-2796-1835], and Apollo - University of Cambridge Repository

Subjects
Adult, Male, neuroimaging, Universities, brain structure, boxing, Regular Article, CANTAB, Neuropsychological Tests, lcsh:Computer applications to medicine. Medical informatics, Magnetic Resonance Imaging, lcsh:RC346-429, Young Adult, Brain Injuries, Athletic Injuries, Humans, lcsh:R858-859.7, Female, Prospective Studies, human activities, lcsh:Neurology. Diseases of the nervous system
Abstract

Background/aim: The safety of amateur and professional boxing is a contentious issue. We hypothesised that advanced magnetic resonance imaging and neuropsychological testing could provide evidence of acute and early brain injury in amateur boxers. Methods: We recruited 30 participants from a university amateur boxing club in a prospective cohort study. Magnetic resonance imaging (MRI) and neuropsychological testing was performed at three time points: prior to starting training; within 48h following a first major competition to detect acute brain injury; and one year follow-up. A single MRI acquisition was made from control participants. Imaging analysis included cortical thickness measurements with Advanced Normalization Tools (ANTS) and FreeSurfer, voxel based morphometry (VBM), and Tract Based Spatial Statistics (TBSS). A computerized battery of neuropsychological tests was performed assessing attention, learning, memory and impulsivity. Results: During the study period, one boxer developed seizures controlled with medication while another developed a chronic subdural hematoma requiring neurosurgical drainage. A total of 10 boxers contributed data at to the longitudinal assessment protocol. Reasons for withdrawal were: logistics (10), stopping boxing (7), withdrawal of consent (2), and development of a chronic subdural hematoma (1). No significant changes were detected using VBM, TBSS, cortical thickness measured with FreeSurfer or ANTS, either cross-sectionally at baseline, or longitudinally. Neuropsychological assessment of boxers found attention/concentration improved over time while planning and problem solving ability latency decreased after a bout but recovered after one year. Conclusion: While this neuroimaging and neuropsychological assessment protocol could not detect any evidence of brain injury, one boxer developed seizures and another developed a chronic sub-dural haematoma. Keywords: Boxing, Neuroimaging, Brain structure, CANTAB

Published
2017

Catalog

Books, media, physical & digital resources
See catalog results