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Anatomical and radiological evaluation of frontal lobe morphometry in healthy and dementia people and machine learning-based prediction of dementia

Year 2023, Volume: 48 Issue: 2, 541 - 558, 02.07.2023
https://doi.org/10.17826/cumj.1275723

Abstract

Purpose: This paper aimed to determine the morphometry of the frontal lobe and central brain region using magnetic resonance imaging in patients having dementia and healthy subjects.
Materials and Methods: 243 subjects (121 subjects having dementia; 122 subjects healthy group) aged 60-90 years over for 2 years between January 2018 and 2020 were included in this study. Also, the supervised Machine learning based (ML based) detection of dementia has been studied on this obtained real world data.
Results: The gender-related changes of frontal region measurements in dementia and healthy subjects were analyzed and, there were differences of measurements’ mean values in gender. In healthy subjects, significance differences were found in all measurements (except the distance from anterior commissure to posterior commissure and outermost of corpus callosum genu to innermost of corpus callosum genu). The means of the measurements were found higher in males than in females.
Conclusions: We believe that the knowledge of our study will provide valuable reference data for our population and will help for a surgeon in planning an operation by considering measurements related to the frontal lobe. In addition, ML based supervised methods that were trained on the collected data for detection of dementia showed that it is required to provide as many attributes and instances as possible to train an accurate estimator. However, if this is not possible, by creating new features based on the hidden patterns between attributes and instances we could increase the success of the estimators up to 96.3% f-score value.

References

  • Sacuiu SF, Dementias. Handb Clin Neurol. 2016;138:123-151.
  • Sorbi S, Hort J, Erkinjuntti T, Fladby T, Gainotti G, Gurvit H et al. EFNS-ENS Guidelines on the diagnosis and management of disorders associated with dementia. Eur J Neurol. 2012;19:1159-79.
  • Álvarez-Linera Prado J, Jiménez-Huete A. Neuroimaging in dementia. Clinical-radiological correlation. Radiologia (Engl Ed). 2019;61:66-81.
  • Tartaglia MC, Rosen HJ, Miller BL. Neuroimaging in dementia. Neurotherapeutics. 2011;8:82-92.
  • Shah H, Albanese E, Duggan C, Igor R, Kenneth ML, Carrillo MC et al. Research priorities to reduce the global burden of dementia by 2025. Lancet Neurol. 2016;15:1285-94.
  • Prince M, Albanese E, Guerchet M, Prina M. World alzheimer report 2014: Dementia and risk reduction. an analysis of protective and modifiable factors. 2014.
  • Frisoni GB, Prestia A, Rasser PE, Bonetti M, Thompson PM. In vivo mapping of incremental cortical atrophy from incipient to overt Alzheimer's disease. J Neurol. 2009;256:916-24.
  • Catani M. The anatomy of the human frontal lobe. Handb Clin Neurol. 2019;163:95-122.
  • Schoenemann PT, Sheehan MJ, Glotzer LD. Prefrontal white matter volume is disproportionately larger in humans than in other primates. Nat Neurosci. 2005;8:242-52.
  • Semendeferi K, Lu A, Schenker N, Damasio H. Humans and great apes share a large frontal cortex. Nat Neurosci. 2002;5:272-76.
  • Smaers JB, Schleicher A, Zilles K, Vinicius L. Frontal white matter volume is associated with brain enlargement and higher structural connectivity in anthropoid primates. PLoS One. 2010;5:e9123.
  • Scheltens P, Leys D, Barkhof F, Huglo D, Weinstein HC, Vermersch P et al. Atrophy of medial temporal lobes on MRI in "probable" Alzheimer's disease and normal ageing: diagnostic value and neuropsychological correlates. J Neurol Neurosurg Psychiatry. 1992;55:967-72.
  • Scheltens P, Pasquier F, Weerts JG, Barkhof F, Leys D. Qualitative assessment of cerebral atrophy on MRI: inter- and intra-observer reproducibility in dementia and normal aging. Eur Neurol. 1997;37:95-9.
  • Jordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. Science. 2015;349:255-60.
  • Kusiak, A. Feature transformation methods in data mining. Electronics packaging manufacturing, IEEE Transactions on. 2001;24:214-7.
  • Frank E, Pfahringer B. Propositionalisation of multi-instance data using random forests. In Australasian Joint Conference on Artificial Intelligence. Springer, Cham, December 2013;362-73.
  • Pal NR, Jain L. Advanced techniques in data mining and knowledge discovery. Springer, 2005.
  • Foulds JR. Learning instance weights in multi-instance learning (Doctoral dissertation). The University of Waikato, 2008.
  • Dietterich TG, Lathrop RH, Lozano-Pérez T. Solving the multiple instance problem with axis-parallel rectangles. Artificial intelligence. 1997;89:31-40.
  • Zhou, Z.H. Multi-instance learning: A survey. Department of Computer Science & Technology, Nanjing University, Tech. Rep., 2004.
  • Tian Y, Hao W, Jin D, Chen G, Zou A. A review of latest multi-instance learning. In 2020 4th International Conference on Computer Science and Artificial Intelligence, 2020;41-4.
  • Foulds J, Frank E. A review of multi-instance learning assumptions. The knowledge engineering review. 2010;25:1-25.
  • Wu J, Pan S, Zhu X, Zhang C, Wu X. Multi-instance learning with discriminative bag mapping. IEEE Transactions on Knowledge and Data Engineering. 2018;30:1065-15.
  • Huang S, Liu Z, Jin W, Mu Y. Bag dissimilarity regularized multi-instance learning. Pattern Recognition. 2022;126:108583.
  • Babenko B. Multiple instance learning: algorithms and applications. View Article PubMed/NCBI Google Scholar. 2008;1-19.
  • Weidmann N, Frank E, Pfahringer B. A two-level learning method for generalized multi-instance problems. In European Conference on Machine Learning Springer, Berlin, Heidelberg, 2003;468-11.
  • Khan A, Baharudin B, Lee LH, Khan K. A review of machine learning algorithms for text-documents classification. Journal of advances in information technology, 2010;1:4-16.
  • Coban O. Attribute inference over real-world online social networks: a comprehensive privacy analysis (Doctoral dissertation). Adana, Cukurova University, 2021.
  • Zafarani R, Abbasi MA, Liu H. Social media mining: an introduction. Cambridge University Press, 2014.
  • Kibriya AM, Frank E, Pfahringer B, Holmes G. Multinomial naive bayes for text categorization revisited. In Australasian Joint Conference on Artificial Intelligence Springer, Berlin, Heidelberg, 2004;488-11.
  • Su J, Shirab JS, Matwin S. Large scale text classification using semisupervised multinomial naive bayes. In ICML, 2011.
  • Platt J. Using analytic QP and sparseness to speed training of support vector machines. Advances in neural information processing systems. 1998:11.
  • Aha DW, Kibler D, Albert MK. Instance-based learning algorithms. Machine learning. 1991;6:37-29.
  • Breiman L. Random forests. Machine learning, 2001;45:5-27.
  • Quinlan JR. C4.5:Program for machine learning. San Francisco, Morgan Kaufmann Publishers Inc, 1993.
  • Mahesh B. Machine learning algorithms-a review. IJSR. 2020;9:381-5.
  • Alpaydin E. Introduction to machine learning. MIT press, 2020.
  • Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. Ijcai. 1995;14:1137-8.
  • Risacher SL, Saykin AJ. Neuroimaging in aging and neurologic diseases. Handb Clin Neurol. 2019;167:191-227.
  • Pandya DN, Seltzer B. Two hemispheres-one brain: functions of the corpus callosum. Neurology and neurobiology. 1986;17:16-2.
  • Salat D, Ward A, Kaye JA, Janowsky JS. Sex differences in the corpus callosum with aging. Neurobiol Aging. 1997;18:191-97.
  • Karakaş P, Koç Z, Koç F, Bozkır MG. Morphometric MRI evaluation of corpus callosum and ventricles in normal adults. Neurol Res. 2011;33:1044-49.
  • Filippi M, Agosta F, Barkhof F, Dubois B, Fox NC, Frisoni GB et al. EFNS task force: the use of neuroimaging in the diagnosis of dementia. Eur J Neurol. 2012;19:e131-1501.
  • Ardeshiri A, Ardeshiri A, Wenger E, Holtmannspötter M, Winkler PA. Surgery of the anterior part of the frontal lobe and of the central region: normative morphometric data based on magnetic resonance imaging. Neurosurg Rev. 2006;29:313-21.
  • Ono M, Ono M, Rhoton AL Jr, Barry M. Microsurgical anatomy of the region of the tentorial incisura. J Neurosurg. 1984;60:365-99.
  • Mourgela S, Anagnostopoulou S, Sakellaropoulos A, Gouliamos A. An MRI study of sex-and age-related differences in the dimensions of the corpus callosum and brain. Neuroanatomy. 2007;6:63-2.
  • Kawashima M, Matsushima T, Sasaki T. Surgical strategy for distal anterior cerebral artery aneurysms: microsurgical anatomy. J Neurosurg. 2003;99:517-25.
  • Schaltenbrand G. Atlas for stereotaxy of the human brain. Georg Thieme. 1977.
  • Nowinski WL. Modified Talairach landmarks. Acta Neurochir (Wien). 2001;143:1045-57.
  • Otake S, Taoka T, Maeda M, Yuh WT. A guide to identification and selection of axial planes in magnetic resonance imaging of the brain. Neuroradiol J. 2018;31:336-44.

Sağlıklı ve demanslı kişilerde frontal lob morfometrisinin anatomik ve radyolojik olarak değerlendirilmesi ve makine öğrenmesi’ne dayanan demans tahmini

Year 2023, Volume: 48 Issue: 2, 541 - 558, 02.07.2023
https://doi.org/10.17826/cumj.1275723

Abstract

Amaç: Bu çalışma, demanslı hastalarda ve sağlıklı bireylerde manyetik rezonans görüntüleme kullanılarak frontal lob ve merkezi beyin bölgesinin morfometrisinin belirlenmesini amaçladı.
Gereç ve Yöntem: Bu çalışmaya Ocak 2018-2020 tarihleri arasında 60-90 yaş arası 243 kişi (121 demanslı; 122 sağlıklı grup) dahil edildi. Ayrıca ortaya çıkan gerçek veriler ile denetimli Makine Öğrenmesine dayalı demans tahmini üzerinde çalışıldı.
Bulgular: Frontal bölgeyi içeren ölçümlerin cinsiyete bağlı değişimleri demans ve sağlıklı bireylerde incelendi ve ölçümlerin ortalama değerlerinde cinsiyete göre farklılıklar bulundu. Sağlıklı bireylerde bütün ölçümlerde (commissura anterior’dan comissura posterior’a olan uzaklık ölçümü ve corpus callosum genu'nun en dış kısmından corpus callosum genu'nun en iç noktasına olan mesafe ölçümleri hariç) anlamlı farklılıklar bulundu. Morfometrik ölçümlerin ortalamaları erkeklerde kadınlara göre daha yüksek bulundu.
Sonuç: Çalışmamızın, popülasyonumuz için değerli referans veriler sağlayacağına ve bir cerraha, ameliyatı planlamasında frontal lob ile ilgili ölçümlerin dikkate alınarak yardımcı olacağına inanıyoruz. Bunun yanısıra, makine öğrenmesine dayalı denetimli öğrenme yöntemleri, demansın tespiti için toplanan veriler üzerinde doğru bir sınıflayıcı ile mümkün olduğunca fazla sayıda nitelik ve örnekleme ihtiyaç duyar. Ancak, bu mümkün değilse, nitelikler ve örneklem arasındaki gizli örüntülere dayalı yeni niteliklerin oluşturulması ile sınıflayıcıların başarısı %96,3 f-skoru değerine kadar artırılabilir.

References

  • Sacuiu SF, Dementias. Handb Clin Neurol. 2016;138:123-151.
  • Sorbi S, Hort J, Erkinjuntti T, Fladby T, Gainotti G, Gurvit H et al. EFNS-ENS Guidelines on the diagnosis and management of disorders associated with dementia. Eur J Neurol. 2012;19:1159-79.
  • Álvarez-Linera Prado J, Jiménez-Huete A. Neuroimaging in dementia. Clinical-radiological correlation. Radiologia (Engl Ed). 2019;61:66-81.
  • Tartaglia MC, Rosen HJ, Miller BL. Neuroimaging in dementia. Neurotherapeutics. 2011;8:82-92.
  • Shah H, Albanese E, Duggan C, Igor R, Kenneth ML, Carrillo MC et al. Research priorities to reduce the global burden of dementia by 2025. Lancet Neurol. 2016;15:1285-94.
  • Prince M, Albanese E, Guerchet M, Prina M. World alzheimer report 2014: Dementia and risk reduction. an analysis of protective and modifiable factors. 2014.
  • Frisoni GB, Prestia A, Rasser PE, Bonetti M, Thompson PM. In vivo mapping of incremental cortical atrophy from incipient to overt Alzheimer's disease. J Neurol. 2009;256:916-24.
  • Catani M. The anatomy of the human frontal lobe. Handb Clin Neurol. 2019;163:95-122.
  • Schoenemann PT, Sheehan MJ, Glotzer LD. Prefrontal white matter volume is disproportionately larger in humans than in other primates. Nat Neurosci. 2005;8:242-52.
  • Semendeferi K, Lu A, Schenker N, Damasio H. Humans and great apes share a large frontal cortex. Nat Neurosci. 2002;5:272-76.
  • Smaers JB, Schleicher A, Zilles K, Vinicius L. Frontal white matter volume is associated with brain enlargement and higher structural connectivity in anthropoid primates. PLoS One. 2010;5:e9123.
  • Scheltens P, Leys D, Barkhof F, Huglo D, Weinstein HC, Vermersch P et al. Atrophy of medial temporal lobes on MRI in "probable" Alzheimer's disease and normal ageing: diagnostic value and neuropsychological correlates. J Neurol Neurosurg Psychiatry. 1992;55:967-72.
  • Scheltens P, Pasquier F, Weerts JG, Barkhof F, Leys D. Qualitative assessment of cerebral atrophy on MRI: inter- and intra-observer reproducibility in dementia and normal aging. Eur Neurol. 1997;37:95-9.
  • Jordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. Science. 2015;349:255-60.
  • Kusiak, A. Feature transformation methods in data mining. Electronics packaging manufacturing, IEEE Transactions on. 2001;24:214-7.
  • Frank E, Pfahringer B. Propositionalisation of multi-instance data using random forests. In Australasian Joint Conference on Artificial Intelligence. Springer, Cham, December 2013;362-73.
  • Pal NR, Jain L. Advanced techniques in data mining and knowledge discovery. Springer, 2005.
  • Foulds JR. Learning instance weights in multi-instance learning (Doctoral dissertation). The University of Waikato, 2008.
  • Dietterich TG, Lathrop RH, Lozano-Pérez T. Solving the multiple instance problem with axis-parallel rectangles. Artificial intelligence. 1997;89:31-40.
  • Zhou, Z.H. Multi-instance learning: A survey. Department of Computer Science & Technology, Nanjing University, Tech. Rep., 2004.
  • Tian Y, Hao W, Jin D, Chen G, Zou A. A review of latest multi-instance learning. In 2020 4th International Conference on Computer Science and Artificial Intelligence, 2020;41-4.
  • Foulds J, Frank E. A review of multi-instance learning assumptions. The knowledge engineering review. 2010;25:1-25.
  • Wu J, Pan S, Zhu X, Zhang C, Wu X. Multi-instance learning with discriminative bag mapping. IEEE Transactions on Knowledge and Data Engineering. 2018;30:1065-15.
  • Huang S, Liu Z, Jin W, Mu Y. Bag dissimilarity regularized multi-instance learning. Pattern Recognition. 2022;126:108583.
  • Babenko B. Multiple instance learning: algorithms and applications. View Article PubMed/NCBI Google Scholar. 2008;1-19.
  • Weidmann N, Frank E, Pfahringer B. A two-level learning method for generalized multi-instance problems. In European Conference on Machine Learning Springer, Berlin, Heidelberg, 2003;468-11.
  • Khan A, Baharudin B, Lee LH, Khan K. A review of machine learning algorithms for text-documents classification. Journal of advances in information technology, 2010;1:4-16.
  • Coban O. Attribute inference over real-world online social networks: a comprehensive privacy analysis (Doctoral dissertation). Adana, Cukurova University, 2021.
  • Zafarani R, Abbasi MA, Liu H. Social media mining: an introduction. Cambridge University Press, 2014.
  • Kibriya AM, Frank E, Pfahringer B, Holmes G. Multinomial naive bayes for text categorization revisited. In Australasian Joint Conference on Artificial Intelligence Springer, Berlin, Heidelberg, 2004;488-11.
  • Su J, Shirab JS, Matwin S. Large scale text classification using semisupervised multinomial naive bayes. In ICML, 2011.
  • Platt J. Using analytic QP and sparseness to speed training of support vector machines. Advances in neural information processing systems. 1998:11.
  • Aha DW, Kibler D, Albert MK. Instance-based learning algorithms. Machine learning. 1991;6:37-29.
  • Breiman L. Random forests. Machine learning, 2001;45:5-27.
  • Quinlan JR. C4.5:Program for machine learning. San Francisco, Morgan Kaufmann Publishers Inc, 1993.
  • Mahesh B. Machine learning algorithms-a review. IJSR. 2020;9:381-5.
  • Alpaydin E. Introduction to machine learning. MIT press, 2020.
  • Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. Ijcai. 1995;14:1137-8.
  • Risacher SL, Saykin AJ. Neuroimaging in aging and neurologic diseases. Handb Clin Neurol. 2019;167:191-227.
  • Pandya DN, Seltzer B. Two hemispheres-one brain: functions of the corpus callosum. Neurology and neurobiology. 1986;17:16-2.
  • Salat D, Ward A, Kaye JA, Janowsky JS. Sex differences in the corpus callosum with aging. Neurobiol Aging. 1997;18:191-97.
  • Karakaş P, Koç Z, Koç F, Bozkır MG. Morphometric MRI evaluation of corpus callosum and ventricles in normal adults. Neurol Res. 2011;33:1044-49.
  • Filippi M, Agosta F, Barkhof F, Dubois B, Fox NC, Frisoni GB et al. EFNS task force: the use of neuroimaging in the diagnosis of dementia. Eur J Neurol. 2012;19:e131-1501.
  • Ardeshiri A, Ardeshiri A, Wenger E, Holtmannspötter M, Winkler PA. Surgery of the anterior part of the frontal lobe and of the central region: normative morphometric data based on magnetic resonance imaging. Neurosurg Rev. 2006;29:313-21.
  • Ono M, Ono M, Rhoton AL Jr, Barry M. Microsurgical anatomy of the region of the tentorial incisura. J Neurosurg. 1984;60:365-99.
  • Mourgela S, Anagnostopoulou S, Sakellaropoulos A, Gouliamos A. An MRI study of sex-and age-related differences in the dimensions of the corpus callosum and brain. Neuroanatomy. 2007;6:63-2.
  • Kawashima M, Matsushima T, Sasaki T. Surgical strategy for distal anterior cerebral artery aneurysms: microsurgical anatomy. J Neurosurg. 2003;99:517-25.
  • Schaltenbrand G. Atlas for stereotaxy of the human brain. Georg Thieme. 1977.
  • Nowinski WL. Modified Talairach landmarks. Acta Neurochir (Wien). 2001;143:1045-57.
  • Otake S, Taoka T, Maeda M, Yuh WT. A guide to identification and selection of axial planes in magnetic resonance imaging of the brain. Neuroradiol J. 2018;31:336-44.
There are 50 citations in total.

Details

Primary Language English
Subjects Clinical Sciences
Journal Section Research
Authors

Sema Özandaç Polat 0000-0001-7330-4919

Mahmut Tunç 0000-0003-1373-4700

Mahmut Oksüzler 0000-0002-3730-5487

Selma Ayşe Özel 0000-0001-9201-6349

Önder Çoban 0000-0001-9404-2583

Pınar Göker 0000-0002-0015-3010

Early Pub Date July 10, 2023
Publication Date July 2, 2023
Acceptance Date May 25, 2023
Published in Issue Year 2023 Volume: 48 Issue: 2

Cite

MLA Özandaç Polat, Sema et al. “Anatomical and Radiological Evaluation of Frontal Lobe Morphometry in Healthy and Dementia People and Machine Learning-Based Prediction of Dementia”. Cukurova Medical Journal, vol. 48, no. 2, 2023, pp. 541-58, doi:10.17826/cumj.1275723.