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5 results on '"Kherif, F"'

1. Machine Learning for Health: Algorithm Auditing & Quality Control

Author

Oala, L, Murchison, A, Balachandran, P, Choudhary, S, Fehr, J, Leite, A, Goldschmidt, P, Johner, C, Schorverth, E, Nakasi, R, Meyer, M, Cabitza, F, Baird, P, Prabhu, C, Weicken, E, Liu, X, Wenzel, M, Vogler, S, Akogo, D, Alsalamah, S, Kazim, E, Koshiyama, A, Piechottka, S, Macpherson, S, Shadforth, I, Geierhofer, R, Matek, C, Krois, J, Sanguinetti, B, Arentz, M, Bielik, P, Calderon-Ramirez, S, Abbood, A, Langer, N, Haufe, S, Kherif, F, Pujari, S, Samek, W, Wiegand, T, Oala L., Murchison A. G., Balachandran P., Choudhary S., Fehr J., Leite A. W., Goldschmidt P. G., Johner C., Schorverth E. D. M., Nakasi R., Meyer M., Cabitza F., Baird P., Prabhu C., Weicken E., Liu X., Wenzel M., Vogler S., Akogo D., Alsalamah S., Kazim E., Koshiyama A., Piechottka S., Macpherson S., Shadforth I., Geierhofer R., Matek C., Krois J., Sanguinetti B., Arentz M., Bielik P., Calderon-Ramirez S., Abbood A., Langer N., Haufe S., Kherif F., Pujari S., Samek W., Wiegand T., Oala, L, Murchison, A, Balachandran, P, Choudhary, S, Fehr, J, Leite, A, Goldschmidt, P, Johner, C, Schorverth, E, Nakasi, R, Meyer, M, Cabitza, F, Baird, P, Prabhu, C, Weicken, E, Liu, X, Wenzel, M, Vogler, S, Akogo, D, Alsalamah, S, Kazim, E, Koshiyama, A, Piechottka, S, Macpherson, S, Shadforth, I, Geierhofer, R, Matek, C, Krois, J, Sanguinetti, B, Arentz, M, Bielik, P, Calderon-Ramirez, S, Abbood, A, Langer, N, Haufe, S, Kherif, F, Pujari, S, Samek, W, Wiegand, T, Oala L., Murchison A. G., Balachandran P., Choudhary S., Fehr J., Leite A. W., Goldschmidt P. G., Johner C., Schorverth E. D. M., Nakasi R., Meyer M., Cabitza F., Baird P., Prabhu C., Weicken E., Liu X., Wenzel M., Vogler S., Akogo D., Alsalamah S., Kazim E., Koshiyama A., Piechottka S., Macpherson S., Shadforth I., Geierhofer R., Matek C., Krois J., Sanguinetti B., Arentz M., Bielik P., Calderon-Ramirez S., Abbood A., Langer N., Haufe S., Kherif F., Pujari S., Samek W., and Wiegand T.

Abstract

Developers proposing new machine learning for health (ML4H) tools often pledge to match or even surpass the performance of existing tools, yet the reality is usually more complicated. Reliable deployment of ML4H to the real world is challenging as examples from diabetic retinopathy or Covid-19 screening show. We envision an integrated framework of algorithm auditing and quality control that provides a path towards the effective and reliable application of ML systems in healthcare. In this editorial, we give a summary of ongoing work towards that vision and announce a call for participation to the special issue Machine Learning for Health: Algorithm Auditing & Quality Control in this journal to advance the practice of ML4H auditing.

Published
2021

2. Towards a European health research and innovation cloud (HRIC)

Author

Aarestrup, Frank Møller, Albeyatti, A., Armitage, W. J., Auffray, C., Augello, L., Balling, R., Benhabiles, N., Bertolini, G., Bjaalie, J. G., Black, M., Blomberg, N., Bogaert, P., Bubak, M., Claerhout, B., Clarke, L., De Meulder, B., D'Errico, G., Di Meglio, A., Forgo, N., Gans-Combe, C., Gray, A. E., Gut, I., Gyllenberg, A., Hemmrich-Stanisak, G., Hjorth, L., Ioannidis, Y., Jarmalaite, S., Kel, A., Kherif, F., Korbel, J. O., Larue, C., Laszlo, M., Maas, A., Magalhaes, L., Manneh-Vangramberen, I., Morley-Fletcher, E., Ohmann, C., Oksvold, P., Oxtoby, N. P., Perseil, I., Pezoulas, V., Riess, O., Riper, H., Roca, J., Rosenstiel, P., Sabatier, P., Sanz, F., Tayeb, M., Thomassen, G., Van Bussel, J., Van Den Bulcke, M., Van Oyen, H., Aarestrup, Frank Møller, Albeyatti, A., Armitage, W. J., Auffray, C., Augello, L., Balling, R., Benhabiles, N., Bertolini, G., Bjaalie, J. G., Black, M., Blomberg, N., Bogaert, P., Bubak, M., Claerhout, B., Clarke, L., De Meulder, B., D'Errico, G., Di Meglio, A., Forgo, N., Gans-Combe, C., Gray, A. E., Gut, I., Gyllenberg, A., Hemmrich-Stanisak, G., Hjorth, L., Ioannidis, Y., Jarmalaite, S., Kel, A., Kherif, F., Korbel, J. O., Larue, C., Laszlo, M., Maas, A., Magalhaes, L., Manneh-Vangramberen, I., Morley-Fletcher, E., Ohmann, C., Oksvold, P., Oxtoby, N. P., Perseil, I., Pezoulas, V., Riess, O., Riper, H., Roca, J., Rosenstiel, P., Sabatier, P., Sanz, F., Tayeb, M., Thomassen, G., Van Bussel, J., Van Den Bulcke, M., and Van Oyen, H.

Abstract

The European Union (EU) initiative on the Digital Transformation of Health and Care (Digicare) aims to provide the conditions necessary for building a secure, flexible, and decentralized digital health infrastructure. Creating a European Health Research and Innovation Cloud (HRIC) within this environment should enable data sharing and analysis for health research across the EU, in compliance with data protection legislation while preserving the full trust of the participants. Such a HRIC should learn from and build on existing data infrastructures, integrate best practices, and focus on the concrete needs of the community in terms of technologies, governance, management, regulation, and ethics requirements. Here, we describe the vision and expected benefits of digital data sharing in health research activities and present a roadmap that fosters the opportunities while answering the challenges of implementing a HRIC. For this, we put forward five specific recommendations and action points to ensure that a European HRIC: i) is built on established standards and guidelines, providing cloud technologies through an open and decentralized infrastructure; ii) is developed and certified to the highest standards of interoperability and data security that can be trusted by all stakeholders; iii) is supported by a robust ethical and legal framework that is compliant with the EU General Data Protection Regulation (GDPR); iv) establishes a proper environment for the training of new generations of data and medical scientists; and v) stimulates research and innovation in transnational collaborations through public and private initiatives and partnerships funded by the EU through Horizon 2020 and Horizon Europe.

Published
2020

3. The 16p11.2 locus modulates brain structures common to autism, schizophrenia and obesity

Author

Maillard, AM, Ruef, A, Pizzagalli, F, Migliavacca, E, Hippolyte, L, Adaszewski, S, Dukart, J, Ferrari, C, Conus, P, Maennik, K, Zazhytska, M, Siffredi, V, Maeder, P, Kutalik, Z, Kherif, F, Hadjikhani, N, Beckmann, JS, Reymond, A, Draganski, B, Jacquemont, S, Maillard, AM, Ruef, A, Pizzagalli, F, Migliavacca, E, Hippolyte, L, Adaszewski, S, Dukart, J, Ferrari, C, Conus, P, Maennik, K, Zazhytska, M, Siffredi, V, Maeder, P, Kutalik, Z, Kherif, F, Hadjikhani, N, Beckmann, JS, Reymond, A, Draganski, B, and Jacquemont, S

Abstract

Anatomical structures and mechanisms linking genes to neuropsychiatric disorders are not deciphered. Reciprocal copy number variants at the 16p11.2 BP4-BP5 locus offer a unique opportunity to study the intermediate phenotypes in carriers at high risk for autism spectrum disorder (ASD) or schizophrenia (SZ). We investigated the variation in brain anatomy in 16p11.2 deletion and duplication carriers. Beyond gene dosage effects on global brain metrics, we show that the number of genomic copies negatively correlated to the gray matter volume and white matter tissue properties in cortico-subcortical regions implicated in reward, language and social cognition. Despite the near absence of ASD or SZ diagnoses in our 16p11.2 cohort, the pattern of brain anatomy changes in carriers spatially overlaps with the well-established structural abnormalities in ASD and SZ. Using measures of peripheral mRNA levels, we confirm our genomic copy number findings. This combined molecular, neuroimaging and clinical approach, applied to larger datasets, will help interpret the relative contributions of genes to neuropsychiatric conditions by measuring their effect on local brain anatomy.

Published
2015

4. Generative FDG-PET and MRI model of aging and disease progression in Alzheimer's disease.

Author

Alzheimer's Disease Neuroimaging Initiative, Weiner, M., Aisen, P., Petersen, R., Jack CR.<Suffix>Jr</Suffix>, Jagust, W., Trojanowki, JQ., Toga, AW., Beckett, L., Green, RC., Saykin, AJ., Morris, J., Liu, E., Montine, T., Gamst, A., Thomas, RG., Donohue, M., Walter, S., Gessert, D., Sather, T., Harvey, D., Kornak, J., Dale, A., Bernstein, M., Felmlee, J., Fox, N., Thompson, P., Schuff, N., DeCarli, C., Bandy, D., Koeppe, RA., Foster, N., Reiman, EM., Chen, K., Mathis, C., Cairns, NJ., Taylor-Reinwald, L., Shaw, L., Lee, VM., Korecka, M., Crawford, K., Neu, S., Foroud, TM., Potkin, S., Shen, L., Kachaturian, Z., Frank, R., Snyder, PJ., Molchan, S., Kaye, J., Quinn, J., Lind, B., Dolen, S., Schneider, LS., Pawluczyk, S., Spann, BM., Brewer, J., Vanderswag, H., Heidebrink, JL., Lord, JL., Johnson, K., Doody, RS., Villanueva-Meyer, J., Chowdhury, M., Stern, Y., Honig, LS., Bell, KL., Morris, JC., Ances, B., Carroll, M., Leon, S., Mintun, MA., Schneider, S., Marson, D., Griffith, R., Clark, D., Grossman, H., Mitsis, E., Romirowsky, A., deToledo-Morrell, L., Shah, RC., Duara, R., Varon, D., Roberts, P., Albert, M., Onyike, C., Kielb, S., Rusinek, H., de Leon MJ., Glodzik, L., De Santi, S., Doraiswamy, P., Petrella, JR., Coleman, R., Arnold, SE., Karlawish, JH., Wolk, D., Smith, CD., Jicha, G., Hardy, P., Lopez, OL., Oakley, M., Simpson, DM., Porsteinsson, AP., Goldstein, BS., Martin, K., Makino, KM., Ismail, M., Brand, C., Mulnard, RA., Thai, G., Mc-Adams-Ortiz, C., Womack, K., Mathews, D., Quiceno, M., Diaz-Arrastia, R., King, R., Martin-Cook, K., DeVous, M., Levey, AI., Lah, JJ., Cellar, JS., Burns, JM., Anderson, HS., Swerdlow, RH., Apostolova, L., Lu, PH., Bartzokis, G., Silverman, DH., Graff-Radford, NR., Parfitt, F., Johnson, H., Farlow, MR., Hake, AM., Matthews, BR., Herring, S., van Dyck CH., Carson, RE., MacAvoy, MG., Chertkow, H., Bergman, H., Hosein, C., Black, S., Stefanovic, B., Caldwell, C., Hsiung, GY., Feldman, H., Mudge, B., Assaly, M., Kertesz, A., Rogers, J., Trost, D., Bernick, C., Munic, D., Kerwin, D., Mesulam, MM., Lipowski, K., Wu, CK., Johnson, N., Sadowsky, C., Martinez, W., Villena, T., Turner, RS., Reynolds, B., Sperling, RA., Johnson, KA., Marshall, G., Frey, M., Yesavage, J., Taylor, JL., Lane, B., Rosen, A., Tinklenberg, J., Sabbagh, M., Belden, C., Jacobson, S., Kowall, N., Killiany, R., Budson, AE., Norbash, A., Johnson, PL., Obisesan, TO., Wolday, S., Bwayo, SK., Lerner, A., Hudson, L., Ogrocki, P., Fletcher, E., Carmichael, O., Olichney, J., Kittur, S., Borrie, M., Lee, TY., Bartha, R., Johnson, S., Asthana, S., Carlsson, CM., Potkin, SG., Preda, A., Nguyen, D., Tariot, P., Fleisher, A., Reeder, S., Bates, V., Capote, H., Rainka, M., Scharre, DW., Kataki, M., Zimmerman, EA., Celmins, D., Brown, AD., Pearlson, GD., Blank, K., Anderson, K., Santulli, RB., Schwartz, ES., Sink, KM., Williamson, JD., Garg, P., Watkins, F., Ott, BR., Querfurth, H., Tremont, G., Salloway, S., Malloy, P., Correia, S., Rosen, HJ., Miller, BL., Mintzer, J., Longmire, CF., Spicer, K., Finger, E., Rachinsky, I., Drost, D., Dukart, J., Kherif, F., Mueller, K., Adaszewski, S., Schroeter, M.L., Frackowiak, R.S., Draganski, B., Alzheimer's Disease Neuroimaging Initiative, Weiner, M., Aisen, P., Petersen, R., Jack CR.<Suffix>Jr</Suffix>, Jagust, W., Trojanowki, JQ., Toga, AW., Beckett, L., Green, RC., Saykin, AJ., Morris, J., Liu, E., Montine, T., Gamst, A., Thomas, RG., Donohue, M., Walter, S., Gessert, D., Sather, T., Harvey, D., Kornak, J., Dale, A., Bernstein, M., Felmlee, J., Fox, N., Thompson, P., Schuff, N., DeCarli, C., Bandy, D., Koeppe, RA., Foster, N., Reiman, EM., Chen, K., Mathis, C., Cairns, NJ., Taylor-Reinwald, L., Shaw, L., Lee, VM., Korecka, M., Crawford, K., Neu, S., Foroud, TM., Potkin, S., Shen, L., Kachaturian, Z., Frank, R., Snyder, PJ., Molchan, S., Kaye, J., Quinn, J., Lind, B., Dolen, S., Schneider, LS., Pawluczyk, S., Spann, BM., Brewer, J., Vanderswag, H., Heidebrink, JL., Lord, JL., Johnson, K., Doody, RS., Villanueva-Meyer, J., Chowdhury, M., Stern, Y., Honig, LS., Bell, KL., Morris, JC., Ances, B., Carroll, M., Leon, S., Mintun, MA., Schneider, S., Marson, D., Griffith, R., Clark, D., Grossman, H., Mitsis, E., Romirowsky, A., deToledo-Morrell, L., Shah, RC., Duara, R., Varon, D., Roberts, P., Albert, M., Onyike, C., Kielb, S., Rusinek, H., de Leon MJ., Glodzik, L., De Santi, S., Doraiswamy, P., Petrella, JR., Coleman, R., Arnold, SE., Karlawish, JH., Wolk, D., Smith, CD., Jicha, G., Hardy, P., Lopez, OL., Oakley, M., Simpson, DM., Porsteinsson, AP., Goldstein, BS., Martin, K., Makino, KM., Ismail, M., Brand, C., Mulnard, RA., Thai, G., Mc-Adams-Ortiz, C., Womack, K., Mathews, D., Quiceno, M., Diaz-Arrastia, R., King, R., Martin-Cook, K., DeVous, M., Levey, AI., Lah, JJ., Cellar, JS., Burns, JM., Anderson, HS., Swerdlow, RH., Apostolova, L., Lu, PH., Bartzokis, G., Silverman, DH., Graff-Radford, NR., Parfitt, F., Johnson, H., Farlow, MR., Hake, AM., Matthews, BR., Herring, S., van Dyck CH., Carson, RE., MacAvoy, MG., Chertkow, H., Bergman, H., Hosein, C., Black, S., Stefanovic, B., Caldwell, C., Hsiung, GY., Feldman, H., Mudge, B., Assaly, M., Kertesz, A., Rogers, J., Trost, D., Bernick, C., Munic, D., Kerwin, D., Mesulam, MM., Lipowski, K., Wu, CK., Johnson, N., Sadowsky, C., Martinez, W., Villena, T., Turner, RS., Reynolds, B., Sperling, RA., Johnson, KA., Marshall, G., Frey, M., Yesavage, J., Taylor, JL., Lane, B., Rosen, A., Tinklenberg, J., Sabbagh, M., Belden, C., Jacobson, S., Kowall, N., Killiany, R., Budson, AE., Norbash, A., Johnson, PL., Obisesan, TO., Wolday, S., Bwayo, SK., Lerner, A., Hudson, L., Ogrocki, P., Fletcher, E., Carmichael, O., Olichney, J., Kittur, S., Borrie, M., Lee, TY., Bartha, R., Johnson, S., Asthana, S., Carlsson, CM., Potkin, SG., Preda, A., Nguyen, D., Tariot, P., Fleisher, A., Reeder, S., Bates, V., Capote, H., Rainka, M., Scharre, DW., Kataki, M., Zimmerman, EA., Celmins, D., Brown, AD., Pearlson, GD., Blank, K., Anderson, K., Santulli, RB., Schwartz, ES., Sink, KM., Williamson, JD., Garg, P., Watkins, F., Ott, BR., Querfurth, H., Tremont, G., Salloway, S., Malloy, P., Correia, S., Rosen, HJ., Miller, BL., Mintzer, J., Longmire, CF., Spicer, K., Finger, E., Rachinsky, I., Drost, D., Dukart, J., Kherif, F., Mueller, K., Adaszewski, S., Schroeter, M.L., Frackowiak, R.S., and Draganski, B.

Abstract

The failure of current strategies to provide an explanation for controversial findings on the pattern of pathophysiological changes in Alzheimer's Disease (AD) motivates the necessity to develop new integrative approaches based on multi-modal neuroimaging data that captures various aspects of disease pathology. Previous studies using [18F]fluorodeoxyglucose positron emission tomography (FDG-PET) and structural magnetic resonance imaging (sMRI) report controversial results about time-line, spatial extent and magnitude of glucose hypometabolism and atrophy in AD that depend on clinical and demographic characteristics of the studied populations. Here, we provide and validate at a group level a generative anatomical model of glucose hypo-metabolism and atrophy progression in AD based on FDG-PET and sMRI data of 80 patients and 79 healthy controls to describe expected age and symptom severity related changes in AD relative to a baseline provided by healthy aging. We demonstrate a high level of anatomical accuracy for both modalities yielding strongly age- and symptom-severity- dependant glucose hypometabolism in temporal, parietal and precuneal regions and a more extensive network of atrophy in hippocampal, temporal, parietal, occipital and posterior caudate regions. The model suggests greater and more consistent changes in FDG-PET compared to sMRI at earlier and the inversion of this pattern at more advanced AD stages. Our model describes, integrates and predicts characteristic patterns of AD related pathology, uncontaminated by normal age effects, derived from multi-modal data. It further provides an integrative explanation for findings suggesting a dissociation between early- and late-onset AD. The generative model offers a basis for further development of individualized biomarkers allowing accurate early diagnosis and treatment evaluation.

Published
2013

5. Does semantic context benefit speech understanding through top-down processes? Evidence from time-resolved sparse fMRI.

Author

Davis, M.H., Ford, M.A., Kherif, F., Johnsrude, Ingrid, Davis, M.H., Ford, M.A., Kherif, F., and Johnsrude, Ingrid

Abstract

When speech is degraded, word report is higher for semantically coherent sentences (e.g., her new skirt was made of denim) than for anomalous sentences (e.g., her good slope was done in carrot). Such increased intelligibility is often described as resulting from “top–down” processes, reflecting an assumption that higher-level (semantic) neural processes support lower-level (perceptual) mechanisms. We used time-resolved sparse fMRI to test for top–down neural mechanisms, measuring activity while participants heard coherent and anomalous sentences presented in speech envelope/spectrum noise at varying signal-to-noise ratios (SNR). The timing of BOLD responses to more intelligible speech provides evidence of hierarchical organization, with earlier responses in peri-auditory regions of the posterior superior temporal gyrus than in more distant temporal and frontal regions. Despite Sentence content × SNR interactions in the superior temporal gyrus, prefrontal regions respond after auditory/perceptual regions. Although we cannot rule out top–down effects, this pattern is more compatible with a purely feedforward or bottom–up account, in which the results of lower-level perceptual processing are passed to inferior frontal regions. Behavioral and neural evidence that sentence content influences perception of degraded speech does not necessarily imply “top–down” neural processes.

Published
2011
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