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Showing 1 to 12 of 16 entries
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An Efficient and Reliable Statistical Method for Estimating Functional Connectivity in Large Scale Brain Networks Using Partial Correlation.

Frontiers in neuroscience

Wang Y, Kang J, Kemmer PB, Guo Y.
PMID: 27242395
Front Neurosci. 2016 Mar 31;10:123. doi: 10.3389/fnins.2016.00123. eCollection 2016.

Currently, network-oriented analysis of fMRI data has become an important tool for understanding brain organization and brain networks. Among the range of network modeling methods, partial correlation has shown great promises in accurately detecting true brain network connections. However,...

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Wang Y, Kang J, Kemmer PB, et al. An Efficient and Reliable Statistical Method for Estimating Functional Connectivity in Large Scale Brain Networks Using Partial Correlation. Front Neurosci. 2016;10:123doi: 10.3389/fnins.2016.00123.
Wang, Y., Kang, J., Kemmer, P. B., & Guo, Y. (2016). An Efficient and Reliable Statistical Method for Estimating Functional Connectivity in Large Scale Brain Networks Using Partial Correlation. Frontiers in neuroscience, 10123. https://doi.org/10.3389/fnins.2016.00123
Wang, Yikai, et al. "An Efficient and Reliable Statistical Method for Estimating Functional Connectivity in Large Scale Brain Networks Using Partial Correlation." Frontiers in neuroscience vol. 10 (2016): 123. doi: https://doi.org/10.3389/fnins.2016.00123
Wang Y, Kang J, Kemmer PB, Guo Y. An Efficient and Reliable Statistical Method for Estimating Functional Connectivity in Large Scale Brain Networks Using Partial Correlation. Front Neurosci. 2016 Mar 31;10:123. doi: 10.3389/fnins.2016.00123. eCollection 2016. PMID: 27242395; PMCID: PMC4876368.
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ODVBA-C: Optimally-Discriminative Voxel-Based Analysis of Continuous Variables.

... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in Neuroimaging

Zhang T, Satterthwaite TD, Davatzikos C.
PMID: 25505793
Int Workshop Pattern Recognit Neuroimaging. 2013 Jun;2013:161-164. doi: 10.1109/PRNI.2013.49.

In this paper, we propose a new method that utilizes a novel spatially adaptive scheme for detection of multivariate neuroimaging patterns relating to a continuous subject-level variable, aiming to effectively determine the optimal spatially adaptive filtering of neuroimaging data...

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Zhang T, Satterthwaite TD, Davatzikos C. ODVBA-C: Optimally-Discriminative Voxel-Based Analysis of Continuous Variables. Int Workshop Pattern Recognit Neuroimaging. 2013;2013:161-164doi: 10.1109/PRNI.2013.49.
Zhang, T., Satterthwaite, T. D., & Davatzikos, C. (2013). ODVBA-C: Optimally-Discriminative Voxel-Based Analysis of Continuous Variables. ... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in Neuroimaging, 2013161-164. https://doi.org/10.1109/PRNI.2013.49
Zhang, Tianhao, et al. "ODVBA-C: Optimally-Discriminative Voxel-Based Analysis of Continuous Variables." ... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in Neuroimaging vol. 2013 (2013): 161-164. doi: https://doi.org/10.1109/PRNI.2013.49
Zhang T, Satterthwaite TD, Davatzikos C. ODVBA-C: Optimally-Discriminative Voxel-Based Analysis of Continuous Variables. Int Workshop Pattern Recognit Neuroimaging. 2013 Jun;2013:161-164. doi: 10.1109/PRNI.2013.49. PMID: 25505793; PMCID: PMC4259270.
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Brain age prediction using fMRI network coupling in youths and associations with psychiatric symptoms.

NeuroImage. Clinical

Lund MJ, Alnæs D, de Lange AG, Andreassen OA, Westlye LT, Kaufmann T.
PMID: 34959052
Neuroimage Clin. 2021 Dec 20;33:102921. doi: 10.1016/j.nicl.2021.102921. Epub 2021 Dec 20.

OBJECTIVE: Magnetic resonance imaging (MRI) has shown that estimated brain age is deviant from chronological age in various common brain disorders. Brain age estimation could be useful for investigating patterns of brain maturation and integrity, aiding to elucidate brain...

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Lund MJ, Alnæs D, de Lange AG, et al. Brain age prediction using fMRI network coupling in youths and associations with psychiatric symptoms. Neuroimage Clin. 2021;33:102921doi: 10.1016/j.nicl.2021.102921.
Lund, M. J., Alnæs, D., de Lange, A. G., Andreassen, O. A., Westlye, L. T., & Kaufmann, T. (2021). Brain age prediction using fMRI network coupling in youths and associations with psychiatric symptoms. NeuroImage. Clinical, 33102921. https://doi.org/10.1016/j.nicl.2021.102921
Lund, Martina J, et al. "Brain age prediction using fMRI network coupling in youths and associations with psychiatric symptoms." NeuroImage. Clinical vol. 33 (2021): 102921. doi: https://doi.org/10.1016/j.nicl.2021.102921
Lund MJ, Alnæs D, de Lange AG, Andreassen OA, Westlye LT, Kaufmann T. Brain age prediction using fMRI network coupling in youths and associations with psychiatric symptoms. Neuroimage Clin. 2021 Dec 20;33:102921. doi: 10.1016/j.nicl.2021.102921. Epub 2021 Dec 20. PMID: 34959052; PMCID: PMC8718718.
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Brain age prediction using fMRI network coupling in youths and associations with psychiatric symptoms.

NeuroImage. Clinical

Lund MJ, Alnæs D, de Lange AG, Andreassen OA, Westlye LT, Kaufmann T.
PMID: 34959052
Neuroimage Clin. 2021 Dec 20;33:102921. doi: 10.1016/j.nicl.2021.102921. Epub 2021 Dec 20.

OBJECTIVE: Magnetic resonance imaging (MRI) has shown that estimated brain age is deviant from chronological age in various common brain disorders. Brain age estimation could be useful for investigating patterns of brain maturation and integrity, aiding to elucidate brain...

Cite
Lund MJ, Alnæs D, de Lange AG, et al. Brain age prediction using fMRI network coupling in youths and associations with psychiatric symptoms. Neuroimage Clin. 2021;33:102921doi: 10.1016/j.nicl.2021.102921.
Lund, M. J., Alnæs, D., de Lange, A. G., Andreassen, O. A., Westlye, L. T., & Kaufmann, T. (2021). Brain age prediction using fMRI network coupling in youths and associations with psychiatric symptoms. NeuroImage. Clinical, 33102921. https://doi.org/10.1016/j.nicl.2021.102921
Lund, Martina J, et al. "Brain age prediction using fMRI network coupling in youths and associations with psychiatric symptoms." NeuroImage. Clinical vol. 33 (2021): 102921. doi: https://doi.org/10.1016/j.nicl.2021.102921
Lund MJ, Alnæs D, de Lange AG, Andreassen OA, Westlye LT, Kaufmann T. Brain age prediction using fMRI network coupling in youths and associations with psychiatric symptoms. Neuroimage Clin. 2021 Dec 20;33:102921. doi: 10.1016/j.nicl.2021.102921. Epub 2021 Dec 20. PMID: 34959052; PMCID: PMC8718718.
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When CAT is not an option: complementary methods of test abbreviation for neurocognitive batteries.

Cognitive neuropsychiatry

Moore TM, Butler ER, Cobb Scott J, Port AM, Ruparel K, Njokweni LJ, Gur RE, Gur RC.
PMID: 33308027
Cogn Neuropsychiatry. 2021 Jan;26(1):35-54. doi: 10.1080/13546805.2020.1859360. Epub 2020 Dec 11.

INTRODUCTION: There is an obvious need for efficient measurement of neuropsychiatric phenomena. A proven method-computerized adaptive testing (CAT)-is not feasible for all tests, necessitating alternatives for increasing test efficiency.METHODS: We combined/compared two methods for abbreviating rapid tests using two...

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Moore TM, Butler ER, Cobb Scott J, et al. When CAT is not an option: complementary methods of test abbreviation for neurocognitive batteries. Cogn Neuropsychiatry. 2020;26(1):35-54doi: 10.1080/13546805.2020.1859360.
Moore, T. M., Butler, E. R., Cobb Scott, J., Port, A. M., Ruparel, K., Njokweni, L. J., Gur, R. E., & Gur, R. C. (2021). When CAT is not an option: complementary methods of test abbreviation for neurocognitive batteries. Cognitive neuropsychiatry, 26(1), 35-54. https://doi.org/10.1080/13546805.2020.1859360
Moore, Tyler M, et al. "When CAT is not an option: complementary methods of test abbreviation for neurocognitive batteries." Cognitive neuropsychiatry vol. 26,1 (2021): 35-54. doi: https://doi.org/10.1080/13546805.2020.1859360
Moore TM, Butler ER, Cobb Scott J, Port AM, Ruparel K, Njokweni LJ, Gur RE, Gur RC. When CAT is not an option: complementary methods of test abbreviation for neurocognitive batteries. Cogn Neuropsychiatry. 2021 Jan;26(1):35-54. doi: 10.1080/13546805.2020.1859360. Epub 2020 Dec 11. PMID: 33308027; PMCID: PMC7855518.
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Persistence of psychosis spectrum symptoms in the Philadelphia Neurodevelopmental Cohort: a prospective two-year follow-up.

World psychiatry : official journal of the World Psychiatric Association (WPA)

Calkins ME, Moore TM, Satterthwaite TD, Wolf DH, Turetsky BI, Roalf DR, Merikangas KR, Ruparel K, Kohler CG, Gur RC, Gur RE.
PMID: 28127907
World Psychiatry. 2017 Feb;16(1):62-76. doi: 10.1002/wps.20386.

Prospective evaluation of youths with early psychotic-like experiences can enrich our knowledge of clinical, biobehavioral and environmental risk and protective factors associated with the development of psychotic disorders. We aimed to investigate the predictors of persistence or worsening of...

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Calkins ME, Moore TM, Satterthwaite TD, et al. Persistence of psychosis spectrum symptoms in the Philadelphia Neurodevelopmental Cohort: a prospective two-year follow-up. World Psychiatry. 2017;16(1):62-76doi: 10.1002/wps.20386.
Calkins, M. E., Moore, T. M., Satterthwaite, T. D., Wolf, D. H., Turetsky, B. I., Roalf, D. R., Merikangas, K. R., Ruparel, K., Kohler, C. G., Gur, R. C., & Gur, R. E. (2017). Persistence of psychosis spectrum symptoms in the Philadelphia Neurodevelopmental Cohort: a prospective two-year follow-up. World psychiatry : official journal of the World Psychiatric Association (WPA), 16(1), 62-76. https://doi.org/10.1002/wps.20386
Calkins, Monica E, et al. "Persistence of psychosis spectrum symptoms in the Philadelphia Neurodevelopmental Cohort: a prospective two-year follow-up." World psychiatry : official journal of the World Psychiatric Association (WPA) vol. 16,1 (2017): 62-76. doi: https://doi.org/10.1002/wps.20386
Calkins ME, Moore TM, Satterthwaite TD, Wolf DH, Turetsky BI, Roalf DR, Merikangas KR, Ruparel K, Kohler CG, Gur RC, Gur RE. Persistence of psychosis spectrum symptoms in the Philadelphia Neurodevelopmental Cohort: a prospective two-year follow-up. World Psychiatry. 2017 Feb;16(1):62-76. doi: 10.1002/wps.20386. PMID: 28127907; PMCID: PMC5269480.
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The psychosis spectrum in a young U.S. community sample: findings from the Philadelphia Neurodevelopmental Cohort.

World psychiatry : official journal of the World Psychiatric Association (WPA)

Calkins ME, Moore TM, Merikangas KR, Burstein M, Satterthwaite TD, Bilker WB, Ruparel K, Chiavacci R, Wolf DH, Mentch F, Qiu H, Connolly JJ, Sleiman PA, Hakonarson H, Gur RC, Gur RE.
PMID: 25273303
World Psychiatry. 2014 Oct;13(3):296-305. doi: 10.1002/wps.20152.

Little is known about the occurrence and predictors of the psychosis spectrum in large non-clinical community samples of U.S. youths. We aimed to bridge this gap through assessment of psychosis spectrum symptoms in the Philadelphia Neurodevelopmental Cohort, a collaborative...

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Calkins ME, Moore TM, Merikangas KR, et al. The psychosis spectrum in a young U.S. community sample: findings from the Philadelphia Neurodevelopmental Cohort. World Psychiatry. 2014;13(3):296-305doi: 10.1002/wps.20152.
Calkins, M. E., Moore, T. M., Merikangas, K. R., Burstein, M., Satterthwaite, T. D., Bilker, W. B., Ruparel, K., Chiavacci, R., Wolf, D. H., Mentch, F., Qiu, H., Connolly, J. J., Sleiman, P. A., Hakonarson, H., Gur, R. C., & Gur, R. E. (2014). The psychosis spectrum in a young U.S. community sample: findings from the Philadelphia Neurodevelopmental Cohort. World psychiatry : official journal of the World Psychiatric Association (WPA), 13(3), 296-305. https://doi.org/10.1002/wps.20152
Calkins, Monica E, et al. "The psychosis spectrum in a young U.S. community sample: findings from the Philadelphia Neurodevelopmental Cohort." World psychiatry : official journal of the World Psychiatric Association (WPA) vol. 13,3 (2014): 296-305. doi: https://doi.org/10.1002/wps.20152
Calkins ME, Moore TM, Merikangas KR, Burstein M, Satterthwaite TD, Bilker WB, Ruparel K, Chiavacci R, Wolf DH, Mentch F, Qiu H, Connolly JJ, Sleiman PA, Hakonarson H, Gur RC, Gur RE. The psychosis spectrum in a young U.S. community sample: findings from the Philadelphia Neurodevelopmental Cohort. World Psychiatry. 2014 Oct;13(3):296-305. doi: 10.1002/wps.20152. PMID: 25273303; PMCID: PMC4219071.
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Brain Regions Identified as Being Associated with Verbal Reasoning through the Use of Imaging Regression via Internal Variation.

Journal of the American Statistical Association

Feng L, Bi X, Zhang H.
PMID: 34955572
J Am Stat Assoc. 2021;116(533):144-158. doi: 10.1080/01621459.2020.1766468. Epub 2019 Jun 08.

Brain-imaging data have been increasingly used to understand intellectual disabilities. Despite significant progress in biomedical research, the mechanisms for most of the intellectual disabilities remain unknown. Finding the underlying neurological mechanisms has been proved difficult, especially in children due...

Cite
Feng L, Bi X, Zhang H. Brain Regions Identified as Being Associated with Verbal Reasoning through the Use of Imaging Regression via Internal Variation. J Am Stat Assoc. 2019;116(533):144-158doi: 10.1080/01621459.2020.1766468.
Feng, L., Bi, X., & Zhang, H. (2021). Brain Regions Identified as Being Associated with Verbal Reasoning through the Use of Imaging Regression via Internal Variation. Journal of the American Statistical Association, 116(533), 144-158. https://doi.org/10.1080/01621459.2020.1766468
Feng, Long, et al. "Brain Regions Identified as Being Associated with Verbal Reasoning through the Use of Imaging Regression via Internal Variation." Journal of the American Statistical Association vol. 116,533 (2021): 144-158. doi: https://doi.org/10.1080/01621459.2020.1766468
Feng L, Bi X, Zhang H. Brain Regions Identified as Being Associated with Verbal Reasoning through the Use of Imaging Regression via Internal Variation. J Am Stat Assoc. 2021;116(533):144-158. doi: 10.1080/01621459.2020.1766468. Epub 2019 Jun 08. PMID: 34955572; PMCID: PMC8693017.
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Development of a probability calculator for psychosis risk in children, adolescents, and young adults.

Psychological medicine

Moore TM, Calkins ME, Rosen AFG, Butler ER, Ruparel K, Fusar-Poli P, Koutsouleris N, McGuire P, Cannon TD, Gur RC, Gur RE.
PMID: 33431073
Psychol Med. 2021 Jan 12;1-9. doi: 10.1017/S0033291720005231. Epub 2021 Jan 12.

BACKGROUND: Assessment of risks of illnesses has been an important part of medicine for decades. We now have hundreds of 'risk calculators' for illnesses, including brain disorders, and these calculators are continually improving as more diverse measures are collected...

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Moore TM, Calkins ME, Rosen AFG, et al. Development of a probability calculator for psychosis risk in children, adolescents, and young adults. Psychol Med. 2021;1-9doi: 10.1017/S0033291720005231.
Moore, T. M., Calkins, M. E., Rosen, A. F. G., Butler, E. R., Ruparel, K., Fusar-Poli, P., Koutsouleris, N., McGuire, P., Cannon, T. D., Gur, R. C., & Gur, R. E. (2021). Development of a probability calculator for psychosis risk in children, adolescents, and young adults. Psychological medicine, 1-9. https://doi.org/10.1017/S0033291720005231
Moore, Tyler M, et al. "Development of a probability calculator for psychosis risk in children, adolescents, and young adults." Psychological medicine vol. (2021): 1-9. doi: https://doi.org/10.1017/S0033291720005231
Moore TM, Calkins ME, Rosen AFG, Butler ER, Ruparel K, Fusar-Poli P, Koutsouleris N, McGuire P, Cannon TD, Gur RC, Gur RE. Development of a probability calculator for psychosis risk in children, adolescents, and young adults. Psychol Med. 2021 Jan 12;1-9. doi: 10.1017/S0033291720005231. Epub 2021 Jan 12. PMID: 33431073; PMCID: PMC8273212.
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Brain age prediction using fMRI network coupling in youths and associations with psychiatric symptoms.

NeuroImage. Clinical

Lund MJ, Alnæs D, de Lange AG, Andreassen OA, Westlye LT, Kaufmann T.
PMID: 34959052
Neuroimage Clin. 2021 Dec 20;33:102921. doi: 10.1016/j.nicl.2021.102921. Epub 2021 Dec 20.

OBJECTIVE: Magnetic resonance imaging (MRI) has shown that estimated brain age is deviant from chronological age in various common brain disorders. Brain age estimation could be useful for investigating patterns of brain maturation and integrity, aiding to elucidate brain...

Cite
Lund MJ, Alnæs D, de Lange AG, et al. Brain age prediction using fMRI network coupling in youths and associations with psychiatric symptoms. Neuroimage Clin. 2021;33:102921doi: 10.1016/j.nicl.2021.102921.
Lund, M. J., Alnæs, D., de Lange, A. G., Andreassen, O. A., Westlye, L. T., & Kaufmann, T. (2021). Brain age prediction using fMRI network coupling in youths and associations with psychiatric symptoms. NeuroImage. Clinical, 33102921. https://doi.org/10.1016/j.nicl.2021.102921
Lund, Martina J, et al. "Brain age prediction using fMRI network coupling in youths and associations with psychiatric symptoms." NeuroImage. Clinical vol. 33 (2021): 102921. doi: https://doi.org/10.1016/j.nicl.2021.102921
Lund MJ, Alnæs D, de Lange AG, Andreassen OA, Westlye LT, Kaufmann T. Brain age prediction using fMRI network coupling in youths and associations with psychiatric symptoms. Neuroimage Clin. 2021 Dec 20;33:102921. doi: 10.1016/j.nicl.2021.102921. Epub 2021 Dec 20. PMID: 34959052; PMCID: PMC8718718.
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Brain Regions Identified as Being Associated with Verbal Reasoning through the Use of Imaging Regression via Internal Variation.

Journal of the American Statistical Association

Feng L, Bi X, Zhang H.
PMID: 34955572
J Am Stat Assoc. 2021;116(533):144-158. doi: 10.1080/01621459.2020.1766468. Epub 2019 Jun 08.

Brain-imaging data have been increasingly used to understand intellectual disabilities. Despite significant progress in biomedical research, the mechanisms for most of the intellectual disabilities remain unknown. Finding the underlying neurological mechanisms has been proved difficult, especially in children due...

Cite
Feng L, Bi X, Zhang H. Brain Regions Identified as Being Associated with Verbal Reasoning through the Use of Imaging Regression via Internal Variation. J Am Stat Assoc. 2019;116(533):144-158doi: 10.1080/01621459.2020.1766468.
Feng, L., Bi, X., & Zhang, H. (2021). Brain Regions Identified as Being Associated with Verbal Reasoning through the Use of Imaging Regression via Internal Variation. Journal of the American Statistical Association, 116(533), 144-158. https://doi.org/10.1080/01621459.2020.1766468
Feng, Long, et al. "Brain Regions Identified as Being Associated with Verbal Reasoning through the Use of Imaging Regression via Internal Variation." Journal of the American Statistical Association vol. 116,533 (2021): 144-158. doi: https://doi.org/10.1080/01621459.2020.1766468
Feng L, Bi X, Zhang H. Brain Regions Identified as Being Associated with Verbal Reasoning through the Use of Imaging Regression via Internal Variation. J Am Stat Assoc. 2021;116(533):144-158. doi: 10.1080/01621459.2020.1766468. Epub 2019 Jun 08. PMID: 34955572; PMCID: PMC8693017.
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Brain age prediction using fMRI network coupling in youths and associations with psychiatric symptoms.

NeuroImage. Clinical

Lund MJ, Alnæs D, de Lange AG, Andreassen OA, Westlye LT, Kaufmann T.
PMID: 34959052
Neuroimage Clin. 2021 Dec 20;33:102921. doi: 10.1016/j.nicl.2021.102921. Epub 2021 Dec 20.

OBJECTIVE: Magnetic resonance imaging (MRI) has shown that estimated brain age is deviant from chronological age in various common brain disorders. Brain age estimation could be useful for investigating patterns of brain maturation and integrity, aiding to elucidate brain...

Cite
Lund MJ, Alnæs D, de Lange AG, et al. Brain age prediction using fMRI network coupling in youths and associations with psychiatric symptoms. Neuroimage Clin. 2021;33:102921doi: 10.1016/j.nicl.2021.102921.
Lund, M. J., Alnæs, D., de Lange, A. G., Andreassen, O. A., Westlye, L. T., & Kaufmann, T. (2021). Brain age prediction using fMRI network coupling in youths and associations with psychiatric symptoms. NeuroImage. Clinical, 33102921. https://doi.org/10.1016/j.nicl.2021.102921
Lund, Martina J, et al. "Brain age prediction using fMRI network coupling in youths and associations with psychiatric symptoms." NeuroImage. Clinical vol. 33 (2021): 102921. doi: https://doi.org/10.1016/j.nicl.2021.102921
Lund MJ, Alnæs D, de Lange AG, Andreassen OA, Westlye LT, Kaufmann T. Brain age prediction using fMRI network coupling in youths and associations with psychiatric symptoms. Neuroimage Clin. 2021 Dec 20;33:102921. doi: 10.1016/j.nicl.2021.102921. Epub 2021 Dec 20. PMID: 34959052; PMCID: PMC8718718.
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Showing 1 to 12 of 16 entries