5. Everything new is well-forgotten old. Is it really so?
From 1998 to the present day GLI profile comparison method was used multiple times for parcellation of different areas, and the corresponding results were meticulously published. But for all these years we constantly see the claim of novelty of presented approach. Let’s consider the list of quotations from these papers and a book chapter (2002), arranged by the publication year:
1998 – “We present a new, observer-independent method for parcellating the cerebral cortex”
1999 – “Our study presents for the first time an automated and observer-independent procedure for parcellating the cerebral cortex”
2000 – “A novel approach to architectonic parcellation”
2001 – “We used a recently described observer-independent cytoarchitectonic method”
2002 – “The critical analysis of the classical cortical map led to the development of new approaches to cytoarchitectonic…”
2005 – “Quantitative architectural analysis: a new approach to cortical mapping”
2006 – “New architectonic atlas approaches”
2009 – “Quantitative architectural analysis: a new approach to cortical mapping”
So, without more ado, shell-we ask: how did it happen that the method was new in 1998 and it is still new so many years later? Meanwhile, is it really nothing else has been proposed since 1978 for quantification and mapping of the cortical structure?
Obviously, it is not so: many similar as well as quite different approaches were developed during last 20+ years. Some examples are presented below in chronological order, and many more, I am sure, can be found easily enough:
- Automated method of cortical profiles registration in serial sections called “Automatic Morphocorticography” (V. Istomin et al., 1984 – 1992). This technology was developed in my group, in the Laboratory of Clinical Neuroanatomy of the Institute of Psychiatry, Moscow, USSR.
- Layer-Specific Patterns of Intrinsic Connections (in “Cellular Basis of Working Memory”, by P.Goldman-Rakich, 1995).
- The concept of General Map (GM, in “Mapping brains without coordinates”, by R.Kotter and E.Wanke, 2005).
- Condition-dependent functional connectivity (S. Dodel et al., 2005).
Fully automated registration of cortical profile (three parameters: area fraction, numerical density and cell profile size, in “Automatic detection of neurons in large cortical slices”, by M. Sciarabba, G. Serrao, D. Bauer, F. Arnaboldi, N.A. Borghese, 2009).
Quantification of thalamocortical projections, the number and distribution of neuronal somata in an entire cortical column and dendritic geometry of excitatory neurons (in experimental animals, V. C. Wimmer et al., 2010, H. S. Meyer et al., 2010).
- Activity time course (ATC, in “The chronoarchitecture of the cerebral cortex”, by A.Bartels and S. Zeki, 2015).
Comprehensive Cellular-Resolution Atlas of the Adult Human Brain (Song-Lin Ding, Joshua J. Royall, Susan M. Sunkin, Lydia Ng, Benjamin A.C. Facer, Phil Lesnar, Angie Guillozet-Bongaarts, Bergen McMurray, Aaron Szafer, Tim A. Dolbeare, Allison Stevens, Lee Tirrell,Thomas Benner, Shiella Caldejon, Rachel A. Dalley, Nick Dee, Christopher Lau, Julie Nyhus, Melissa Reding, Zackery L. Riley, David Sandman, Elaine Shen, Andre van der Kouwe, Ani Varjabedian, Michelle Write, Lilla Zollei, Chinh Dang, James A. Knowles, Christof Koch, John W. Phillips, Nenad Sestan,Paul Wohnoutka, H. Ronald Zielke, John G. Hohmann,Allan R. Jones, Amy Bernard,Michael J. Hawrylycz, Patrick R. Hof, Bruce Fischl,and Ed S. Lein1, 2016).
Population-based 180-area cortical parcellation using high quality multimodal data from Human Connectome Project (in “A multi-modal parcellation of human cerebral cortex”, by Matthew F. Glasser, Timothy S. Coalson1, Emma C. Robinson, Carl D. Hacker, John Harwell, Essa Yacoub, Kamil Ugurbil, Jesper Andersson, Christian F. Beckmann, Mark Jenkinson, Stephen M. Smith & David C. Van Essen, 2016).
To be fair, I have to mention that K.Amunts’ group recently published a short report about application of CNN for cortical parcellation (H. Spitzer et all, “Parcellation of visual cortex on high-resolution histological brain sections using convolutional neuronal networks”, IEEE, 2017, 920-923). Submitted as a conference presentation, it indicates the move to the right direction, however, demonstrated results indicate that it is only a beginning of a long road.